Wearable AI for on-device frailty assessment

Domain: neuroscience
Published: 2025-12-20
Editorial outcome: minor_revision

Abstract
Continuously operating wearables offer detailed insights into chronic health conditions and have the potential to reshape diagnostic and screening tools. However, the energy demands and large datasets created by constant monitoring over weeks to months are difficult or impossible to integrate into existing clinical practice, limiting the utility of this device class. Machine learning offers the opportunity to condense these large datasets into streamlined, digestible trends with the potential for significant clinical impact, although off-device inference requires advanced network infrastructure and substantial power availability for radios. Here, we introduce a device framework that integrates artificial intelligence with clinical grade biosignal acquisition at the edge, performing on-device inference with clinical grade fidelity over extended durations with no interaction required by the wearer. We utilize this framework to perform gait-based frailty assessment during in vivo trials (N = 16) with results that match gold standard diagnostic tools. Clinical utility, model stability, and on-device inference are validated through in vivo trials (N = 14) and ten-day-long extended wear experiments, demonstrating continuous operation without wearer intervention and autonomous longitudinal analysis of high sampling rate biosignals.

Introduction
Often confounded with natural aging and disability, frailty syndrome is a clinically recognizable predisposition to negative health outcomes, including falls, hospitalization, and mortality1–3. Defined by decreases in biologic reserves, exacerbated sarcopenia, and diminished strength2, frailty poses significant risk to a patient, with 3-year mortality of frail patients 6× higher than healthy patients1. Early intervention, however, shows significant improvement in clinical outcomes for patients at-risk for frailty or pre-frailty2, highlighting the need to advance existing diagnostic capacity and accessibility.

The current clinical standard for frailty diagnosis, illustrated Fig. 1a, is reactionary rather than preventive, with diagnoses often only occurring after incidental fall or hospitalization1,4. From there, consulting with a clinician typically results in scheduling one or more follow-up visits and in-clinic frailty assessments, requiring time, reimbursement, and the capacity to repeatedly transport a potentially frail patient to a clinic. Although no one gold standard exists, the primary approach for frailty assessment is according to the Fried Frailty Phenotype (FFP)1,3, which broadly considers five metrics: exhaustion, slowness, weakness, low activity, and unexpected weight loss. Exhaustion, low activity, and unexpected weight loss are all subjective, self-reported metrics, whereas weakness is typically evaluated using a grip strength test and slowness through various mobility/gait tests, with research actively examining the efficacy of different testing modalities3. Alternative approaches for frailty assessment utilize gait analysis through instrumented walkways, force plates, vision-based systems, and wearable technologies3,5–7.

Gait-based frailty analysis traditionally relies upon assessing speed, variability, and step length during habitual walking3,7,8. Short clinical assessments, therefore, do not provide an accurate representation of potentially pathological gait for numerous reasons, such as patient comfort and intentionality and differences in walking surfaces6, e.g., flat, vinyl flooring in laboratory and medical settings versus uneven and textured materials elsewhere. Therefore, continuous monitoring during daily living is critical for establishing ground truth in gait-based frailty determination6. While continuously operating devices provide significantly richer datasets and opportunities for valuable health insight or intervention9,10, the large volumes of data they produce, especially over weeks or months, pose a notable hindrance for integration into existing clinical practice as it is impractical for a physician to meaningfully analyze large datasets in a timely manner. Since conventional statistical analysis approaches for gait-based frailty assessment, such as examining stance time, step length, or frequency component analysis11, are difficult to automate and tailor to individuals12,13, these large datasets provide a strong use case for machine learning (ML)-based data analysis, which can rapidly and accurately identify features that may be difficult for humans to recognize in a timely manner14, especially repeated over longer time scales. ML approaches also capture complex, non-linear interactions which can be missed with conventional approaches, scale well, and offer flexibility with the potential to learn from data13. Performing on-device ML-based data analysis significantly condenses these large datasets, transforming weeks of continuous analysis into easily digested trends that can be transmitted with minimal bandwidth and data management requirements, shifting point-of-care from hospitals and clinics to homes through telehealth, reducing financial burdens on patients and decreasing strain on healthcare systems.

By embedding edge AI into a continuously operating wearable device, we introduce a method for gait-based frailty assessment during habitual walking over extended durations, shown Fig. 1b. The biosymbiotic edge AI device (BEAD) utilizes biosymbiotic15–20 design to comfortably enable autonomous gait acquisition via inertial measurement unit (IMU) over weeks, requiring no interaction by the wearer at any point, including during recharging and data collection. On-device continuous step isolation and inference reduce the amount of data transmitted by the device by nearly 99% (8 bytes for a time-stamped step inference vs 436 bytes of raw data per step, see “Continuous gait monitoring for frailty detection” “Results”), conserving battery life with a 21% decrease in average power consumption (see “Continuously operating frailty assessment” “Results”), enhancing data privacy by minimizing data management requirements, and providing quantifiable long-term metrics with an overall accuracy greater than 90% when classifying between healthy and pre-frail steps (see “On-device machine learning for gait-based frailty assessment” “Results”). In vivo studies with healthy and pre-frail subjects aged 65 and older demonstrate gait measurement fidelity on par with current clinical gold standard devices (see “Continuously operating frailty assessment” “Results”), and showcase the BEAD’s ability to perform real-time (<330 ms from raw data to inference result, “On-device machine learning for gait-based frailty assessment” “Results”), exclusively on-device inference for continuous frailty status determination.

Results
Gold standard validation
Wearable technologies for gait-based frailty assessment have significant advantages over instrumented walkways, force plates, and vision-based systems, namely portability and ease of data collection5,7,21. Although technically portable, walkways and force plates remain fixed in one location during testing and require setup and monitoring, typically in a laboratory or medical setting, precluding their use for habitual monitoring. Vision-based systems are costly, may compromise patient privacy or comfort, and eventually require manual data evaluation by trained personnel. Therefore, wearable technologies capable of chronic operation22,23, e.g., biosymbiotic electronics, are critical for establishing a modernized standard for frailty determination. Biosymbiotic technology utilizes soft, conformal 3D-printed mesh bodies, far-field power harvesting, and autonomous ultralow-power electronics to create sensing platforms that operate continuously without any interaction required from the wearer15–20.

To demonstrate the high sensing fidelity of the BEAD introduced in this work, gold standard clinical-grade gait assessment devices are compared against biosymbiotic wearable devices, shown in Fig. 2A, during in vivo trials. Notable is the brick-and-strap architecture of the gold standard (Biosensics, LEGSys), which results in significant inertia-induced accelerometer/gyroscope noise that is not present in the biosymbiotic devices’ gait recordings15 due to their distributed, conformal form factor and low-mass sensor design.

Gait parameters from continuous 60-s walk test during in vivo trials (n = 12 subjects, 5 healthy, 7 pre-frail) are shown in Fig. 2B. Step/stride time and step/stride variability are gait parameters typically assessed when performing frailty assessment in clinic, and can reliably distinguish frailty severity3,5. No statistically significant differences are noted between data collected by gold standard devices versus biosymbiotic devices across all assessed gait metrics, see Supplementary Table S1. The data agree with current gait-based frailty assessment tools5, notably step/stride variability increased in pre-frail versus healthy subjects, and left/right thigh mid-swing velocities higher in healthy versus pre-frail subjects8. Data and extracted metrics from continuous walking demonstrate continuous, high-fidelity, clinical grade gait assessment in line with existing literature.

Shown Fig. 2C is a photo of a subject during 5 sit-to-stand (5STS) testing, with a circle highlighting upper leg placement used to extract the 5STS metrics shown in Fig. 2D (n = 11 subjects, one subject did not complete the test). Consistent with Fig. 2B, there is no statistically significant difference in data collected by the gold standard devices versus biosymbiotic devices. A distinct separation between data from healthy and pre-frail subjects during 5STS is in line with current literature, namely increased sit/stand transition times and increased time required to complete the 5STS cycles24. Timed Up-and-Go (TUG) results, shown in Supplementary Fig. 1, further demonstrate continuous functionality and clinical grade fidelity during high acceleration daily activities; however, TUG tests do not reliably identify prefrail versus healthy individuals25,26, and the data collected are used solely to calculate minimum walking speed for FFP slowness determination.

Results from in vivo studies demonstrate clear and consistent differences between healthy and pre-frail subjects. Additionally, the data recorded by the gold-standard clinical grade LEGSys system and the biosymbiotic wearable devices show high consistency and concurrency at all placements with no statistically significant differences in data collected by both device types across all assessed gait parameters, demonstrating the biosymbiotic wearable device’s clinical grade gait assessment ability.

Continuously operating frailty assessment
Clinical frailty analysis, outlined above, requires trained personnel, specialized equipment, and a defined experimental arena, making diagnostics cumbersome and localized to well-equipped care facilities. Shifting diagnosis into an ambulant setting would provide a substantial step in diagnostic efficiency, however several key hurdles remain unaddressed: transmitting raw data at the sampling rates required27 (100–500+ Hz) for this application is prohibitively energy expensive, managing a constantly connected data processing device is impractical for most lifestyles, and significant datasets would be created even by short recordings: 12 kB/min (assuming 100 Hz and 16-bit single axis resolution). ML inference exclusively on the wearable device (versus transmitting raw/pre-processed data off-device for inference) poses a significant opportunity for optimizing battery life over longer recording durations, safeguarding patient privacy, and minimizing data management requirements28.

Current generation wearable devices face issues with subject interaction through charging requirements, technology management of smartphones or specialized devices, and secure placement and skin attachment, with patient discomfort and irritation often arising from rigid device bodies, abrasive textile or non-breathable plastic attachment straps, and adhesives which need to be replaced often23,29. To avoid these common issues, BEADs employ biosymbiotic device architecture, which ensures lightweight comfort and the ability to operate continuously for weeks without input from the wearer15,20. A photograph of a BEAD with subsystems labeled is shown in Fig. 3a. Biosymbiotic devices embed soft electronics in lightweight, breathable, water- and sweatproof, patient-tailored (using 3D scans, photogrammetry, or direct measurements) polymer meshes that are rapidly 3D-printed using FDA-approved skin-safe thermoplastic polyurethane (TPU) filament15. The thin, low mass form factor of the device (6 mm height max, weight 15 g with 30 mAh LiPo battery) and breathable mesh allow for nearly imperceptible wear without slippage, adhesives, or irritation15,19. A functional block diagram, a labeled component diagram, and an electrical schematic are included in Fig. 3b and Supplementary Figs. 2 and 3, respectively. Onboard wireless far-field power harvesting using commercially available, FCC-approved power casting systems enable wireless recharging at-distance, such as at a work desk or during sleep, without wearer interaction15,16. These power casting systems plug into any wall outlet and require only a quick initial setup for continuous operation: they are simply plugged in and rotated to face the BEAD, providing wireless power up to 2 m away15,16.

Although BLE consumes low power relative to other communication modalities23,28, it uses considerable power during transmission and receiving events (up to 30+ mW depending on chipset) relative to the power consumption of the microcontroller core during typical operation (<8 mW active, <0.25 mW sleep/idle), see Supplementary Fig. 4. The BEAD’s onboard IMU (Bosch, BMI270) embeds a proprietary decision tree model that continuously evaluates accelerometer signals to detect motion and perform walking/running activity recognition, generating hardware-level interrupts on activity change. Using this interrupt allows the wearable device to enter a low-power mode that consumes 10× less power while maintaining quick wake timing (<35 ms from low-power standby to normal operation, Supplementary Fig. 5) to collect real time gait data immediately once the wearer begins walking, with no additional effort or interaction required to begin data logging.

Figure 3c illustrates current consumption for a BEAD performing on-device inference versus transmitting raw step data for off-device analysis. Integrating over the entire 14 s duration reveals a 21% decrease in energy consumption (19.68 milliJoule versus 24.97 milliJoule, respectively) for single-step inference. Shown in Supplementary Fig. 6, system energy consumption depends on walking speed, with the IMU’s motion detection hardware interrupt triggering more reliably at higher step rates. In response to the interrupt, the microcontroller activates the IMU’s gyroscope, which has high power demands relative to the low-power idle state of the BEAD. Walking speeds below 2.89 kph (1.8 mph) did not reliably trigger the IMU’s activity detection during this test; thus, no changes in system current consumption are noted for 0.97 and 1.93 kph (0.6 mph and 1.2 mph, respectively). At or above 3.86 kph (2.4 mph), the gyroscope is practically enabled continuously and current consumption plateaus. By employing more conservative inference approaches, e.g., limiting the number of steps classified for a given duration, one can maintain low energy consumption. Since the BEAD is worn for extended durations and can store inference results (8 bytes/step) when external logging or processing devices are not available, rate-limiting inference is not as prohibitive as during data collection from short, dedicated, in-clinic sessions. Results from nearly 27,000 events can be stored on-device, enabling 7–14 days (depending on daily step count) without the need to activate BLE, providing exceptional flexibility in firmware design and increased reliability without the need for large onboard memory.

Analyzed data, i.e., timestamped inferences, can be offloaded via an internet-connected device such as a smartphone, aggregated into total steps classified as frail versus healthy, and deposited directly into electronic health records (EHRs), shown in Supplementary Fig. 7A. Unlike with traditional brick-and-strap wearable devices, a patient is not required to continuously interact with the BEAD to perform data collection, see Supplementary Fig. 7B for the control flow that enables autonomous data collection. Patients also do not need to remove the device for charging or when bathing. Only a brief initial setup of the power caster is required for indefinite collection of high-fidelity biosignals, enhancing accessibility, user experience, and improving overall patient compliance10,15.

On-device machine learning for gait-based frailty assessment
The significance of this device compared to existing wearable technologies for frailty detection3,5,7,12,27,30–36, see Supplementary Table S2, is demonstrated by its ability to autonomously and efficiently evaluate a patient’s gait in real time without any external infrastructure.

Angular velocity from upper thigh located biosymbiotic devices, sample data shown in Fig. 4a (left) and device placement shown in Fig. 2C, is normalized (Fig. 4a, middle) and used to create the training dataset Fig. 4a (right). This placement and modality are chosen because of their reduced sensitivity to intersubject variance, i.e., the data are less noisy relative to shank placement. Since biosymbiotic devices have low mass relative to current gold standard gait analysis devices, the data from on-device IMUs contain features which may be occluded by the inertia of brick-and-strap clinical standard devices15. Illustrated in Fig. 4b, a classifier model trained on a workstation is readily quantized and optimized into memory-efficient C code for deployment on resource-constrained embedded devices. Once integrated with the wearable device, inference can be performed on-device using incoming sensor data in real time, in this case allowing for angular velocity data from individual steps to be extracted, pre-processed, and evaluated by the µC while consuming minimal power and memory footprint.

In this work, ROCKET37 and MINIROCKET38 are used for feature extraction from time series data containing 1-dimensional data (angular velocity, 218 points per step), and 2-dimensional data (angular velocity, 218 points per step, with timestamps for each point, 218 × 2 = 436 points per step). One significant advantage of MINIROCKET over ROCKET is its ability to be deployed fully deterministic, meaning transform biases and kernels can be pre-calculated by the external workstation and stored in non-volatile memory on the microcontroller, reducing both computation time and complexity with little impact to model accuracy. Assessing several common machine learning approaches, including a ridge regression classifier, K-Nearest-Neighbors classifiers with 3-, 5-, 7-, and 9-nearest neighbors, and a Random Forest (RF) classifier, the class of the evaluation subset is predicted with an accuracy around 70%, 75%, and 95%, respectively, results shown Fig. 4c. Accuracy evaluation of a ridge regression classifier using a ROCKET transform is included, although validation accuracy is significantly worse than other approaches, likely due to overfitting the relatively small training dataset (N = 2057 steps from 12 unique subjects) and imbalance in sample number between each class (5 healthy versus 7 pre-frail).

Deployment of a RF classifier with a fully deterministic MINIROCKET transformer provides the highest average classification accuracy among the ML approaches surveyed, ~96.2%, however time per inference and memory footprint are both high, averaging 3 s per inference and occupying over 80 kB of non-volatile memory for a 1-dimensional transformer. The high time per inference precludes real-time operation, causing a significant number of missed steps while the microcontroller is busy processing the transform, and the large memory footprint significantly limit the number of microcontrollers which can fit the final transformer. It may also increase overall system design complexity as off-µC nonvolatile memory becomes necessary.

Shown Supplementary Fig. 8, the MINIROCKET transform can be optimized for memory footprint and computational time by reducing the number of convolutional kernels from 10,000 (actually 9996) to 420, and the maximum number of dilations per kernel from 32 to 16, achieving nearly tenfold reductions in both transform time per step (~330 ms vs 3 s) and memory footprint (8 kB of weights per transform dimension vs 80 kB) while maintaining high model accuracy (91% for optimized MINIROCKET vs 96% for default parameters). Model optimization indicates that around 500 kernels are necessary to achieve reliable performance and accuracy in this case. Although the MINIROCKET algorithm requires a multiple of 84 kernels, performance at 504 kernels versus 420 kernels is not substantially better to merit the higher computational cost and RAM requirement. Decreasing computation time per step to 330 ms enables real-time continuous per-step inference, uninterrupted device functionality, and lowers overall system power consumption since the ultralow-power microcontroller can sleep between subject steps.

Figure 4d contains characteristic steps from subjects who met FFP criteria for either healthy or pre-frail classification, showing small variations which are common amongst each class. The differences between the two classes are subtle, making clinical assessment of an extended study impractical to do by hand and impossible to integrate into existing clinical practice. The issue is further complicated by high waveform variance across subjects, with pre-frail individuals exhibiting additional intrasubject gait variance relative to healthy subjects, see Supplementary Fig. 9.

Shown in Fig. 4e, the RF classifier with compressed MINIROCKET transform retains a high overall accuracy of 91.33% (with a false-negative rate of 8.05% and false-positive rate of 6.35%), as compared to 96.2% before parameter optimization. Given the significant imbalance between class sample n, accuracy does not necessarily provide an absolute metric of classification performance. The f1-scores of the classification pipeline are therefore assessed and shown in Supplementary Table S3, with scores of 0.92 and 0.90 when predicting the healthy and pre-frail, respectively, indicating excellent precision, recall, and ability to distinguish between classes. Figure S10 contains learning curves generated using two different cross-validation schemes: stratified, fivefold (Supplementary Fig. 10A) and leave-one-group-out (Supplementary Fig. 10B), showing the model’s ability to learn and generalize as the training set increases in size, although it is limited by the number of pre-frail subjects in the training set.

Ultimately, these results demonstrate the ability to deploy a performant, memory-, time-, and power-optimized classification pipeline on ultralow-power devices without interfering with designed device operation. They also reveal subject-specific overfitting that limits generalizability. While minor improvements may be gained through hyperparameter tuning or with a more advanced classification algorithm, clinical deployment will require substantially larger, more diverse training cohorts. Given local population dynamics (Tucson, AZ: 92,990 adults aged 65+)39, the general incidence of frailty in adults age 65 and older (~10–15%)40,41, and the ability of the local university-healthcare network to reach, recruit, and retain longitudinal study subjects, there become fewer than 200 potentially recruitable subjects in a large city. Considering recruitment rates of non-oncological clinical trials average as low as 0.85 subjects recruited per site per month42,43, statistically significant subject recruitment would require considerable resources, necessitating years-long, multi-institution, multi-site collaboration. Such a study would set the foundation for establishing and translating a new clinical tool.

Continuous gait monitoring for frailty detection
At this time, there are no technologies capable of automated frailty detection, i.e., continuous operation, monitoring gait, and performing automated classification, either on- or off-device, critical for assessment in the absence of trained personnel27. The device introduced in this work, showcased Fig. 5a, provides an approach for home-based frailty diagnostics in a low-profile, unobtrusive form factor which is easily hidden under clothing, shifting point of care away from medical clinics, and enhancing current telehealth offerings.

This proposed clinical use, illustrated Fig. 5b, can be offered preventively rather than post-hospitalization, as is often the case with the current approach, improving patient outcomes44,45. An initial pre-diagnostic meeting with a telehealth provider results in a device and commercially available wireless power caster shipped directly to the patient. Since the BEAD requires no interaction from the patient beyond initial placement of the power caster at a desk or bedside, is recharged wirelessly, and can be worn while bathing, the patient would simply wear the device on their thigh and continue with their normal routine. During habitual gait, i.e., step activity recognized by the BEAD’s onboard IMU, the device wakes up, records continuous angular velocity of the leg, isolates individual steps, and performs inference, aggregating the results over time and transmitting them off-device when possible, potentially integrating them directly into EHRs/electronic patient health information28 or transmitting them directly to the telehealth provider. After several weeks, or given an early, clear trend in the inference results, a follow-up appointment with a healthcare provider enables informed advanced diagnostics and treatment plans.

Validation of on-device inference is performed with a second round of in vivo trials (N = 14 subjects: 11 healthy, 3 pre-frail). The BEAD monitors gait continuously using real time angular velocity from upper thigh placement devices, extracting individual steps and performing inference, with results from 60 s continuous walk testing plotted Fig. 5c. Characteristic angular velocity data from a healthy subject is plotted Fig. 5c (left), with inset of a step shown (second left). A well-defined peak with little oscillation is visible. Characteristic data from a pre-frail subject is shown in Fig. 5c (second right) with step highlighted inset (right). This step is diminished in magnitude and exhibits a wider, more oscillatory, and unstable waveform relative to the healthy subject’s step. The inference results are obtained after adjusting the confidence threshold required for the model to predict one class versus the other, see Supplementary Fig. 11. A threshold of 70% confidence resulted in zero false-positives or false-negatives for either subject, highlighting the reliability of the model. On-device inference does not capture every step, approximately 70–80%, however this is sufficient over days to weeks given that the current clinical approach assesses only steps taken during very limited on-location continuous walk testing.

To demonstrate feasibility of chronic operation, a ten-day extended wear experiment is performed. During the experiment, a BEAD worn by a healthy subject monitors their gait continuously, storing only timestamped step inference results in nonvolatile memory on-device until data can be transferred off. In this typical use case, the BLE packet rate can be decreased to conserve power without affecting sensing fidelity, since all processing and inference occur on-device without BLE. System performance is likewise unaffected by decreasing the packet rate, since only small packets of timestamped inference data are offloaded only when a logging device connects over BLE. Figure 5d plots continuous battery life from this experiment, with far-field charging shown in orange sections. Numerous healthy step predictions are logged daily, with fewer than ten false positives detected during the ten-day experiment. The experiment shows continuous high-fidelity digital gait assessment without impact by state of charge (Supplementary Fig. 12), reliable recharging, and long-term model stability throughout, with only ~8.6% false positive rate. Combined, these results demonstrate that the power of per-step inference is realized through temporal ML analysis, i.e., statistical analysis of per-step inferences recorded from routine life over weeks to months. Cross-sectional analysis, i.e., statistical analysis of per-step inference results from short 60-s walks in labs or clinics once a month, is less representative and less accurate than temporal analysis46. Extracted through temporal ML analysis, the diagnostic indicator is the percentage of steps classified as frail (or pre-frail) (PSCF) relative to steps classified as healthy over the study duration. Ratios can be easily integrated into EHRs and read by a clinician at a glance, providing insight into a patient’s status.

In cases where a patient’s PSCF is not decisive, deeper analysis can generate longitudinal trend reports to enhance understanding. Since each inference result is timestamped, one can extract potentially insightful metrics such as the number of frail steps per day and explore trends by time of day, day of the week, or from day-to-day and week-to-week, providing a complete profile of when diagnosis occurs. Combining clinical history, patient lifestyle, and ML-based symptom analysis drives precision medicine and personalized care, likely improving patient outcomes and reducing healthcare cost47.

Methods
In vivo gold standard validation
All human subject studies were approved by a University of Arizona Internal Review Board, protocol #2005648028, and were performed indoors in a hospital setting. All subjects provided both written and verbal consent to the experiments. For comparison against gold standard/training data collection (N = 16), subjects aged 65 and older with no history of sensorimotor disorders or conditions (Parkinson’s disease, multiple sclerosis, stroke, recent hip, leg, or foot surgery, etc.) were recruited and their frailty status assessed according to the FFP and frailty determination protocol described below. Subjects must have been able to walk 60 s unassisted to participate in the study.

Subjects responded to multiple questionnaires, including the Patient Health Questionnaire-9 (PHQ-9), a Mini-Mental State Examination (MMSE), the Barthel Index for Activities of Daily Living (ADL), the Falls Efficacy Scale-International (FES-I), and a questionnaire asking about their average and recent levels of activity, with breakdowns by activity and frequency, their average and recent levels of exhaustion, and if they had lost ten or more pounds unintentionally within the past year.

Subjects were then asked to perform a grip strength test where they squeezed a hand dynamometer 3× with each hand. A member of the study team recorded results from each trial. Finally, for gait analysis, initial cohort subjects were outfitted with five gold standard gait analysis devices (BioSensics, LEGSys), each co-located with a biosymbiotic wearable device with 6-axis IMU (Bosch, BMI270), on their left and right shanks, left and right thighs, and trunk. All ten devices recorded continuous accelerometer and gyroscope data. Subjects were then asked to perform a series of movement tasks commonly used in gait-based frailty assessment: 1. 60-S Continuous Walk: Subjects were allowed to walk at their own pace and unassisted indoors on level ground for sixty seconds. Step count was extracted from the angular velocity data of each thigh-located device (one gold-standard LEGSys device and one biosymbiotic device per thigh, four devices total). Step count extraction was automated using a MATLAB script and validated by a member of the study team; the MATLAB script isolated per-device all peaks in the angular velocity data with magnitude at least 50 degrees per second and inter-peak spacing at least 1 s apart. 2. TUG: Subjects began seated, stood up, walked around a cone in the center of the room 3 meters away, and sat back down. Their total time from start to re-seating was recorded by a member of the study team. Walking speed was determined from this test by manual analysis of the angular velocity data of each thigh-located device (one gold-standard LEGSys device and one biosymbiotic device per thigh, four devices total): the distance covered during the walk (3 m from chair to cone + 3 m from cone to chair) was divided by the time from toe-off of the first step after standing up to heel-strike of the last step before turning around to sit down. These times were extracted by manual inspection of the angular velocity waveforms. 3. STS: Subjects began seated and were asked to stand up and sit back down without using their hands. This was repeated five times with their total time recorded by a member of the study team directly observing the subjects. Four subjects did not complete grip strength testing and their frailty status could not be determined; thus, their data are not included in our analysis.

Frailty determination protocol
Frailty status was determined as follows: TUG, STS, and daily activity survey data collected from each subject were input into a custom Microsoft Access database preloaded with normative and threshold data derived from the National Heart, Lung, and Blood Institute’s Cardiovascular Health Study (CHS). The database automatically processed these metrics using established CHS criteria, including gait speed, grip strength, and reported activity levels, to classify subjects as healthy, pre-frail, or frail according to the FFP. None of the subjects recruited met the classification for frailty.

Wearable electronics fabrication
All wearable devices were hand assembled using externally manufactured 2-layer flexible printed circuit boards (flex PCBs). The flex PCBs were manufactured in a panelized design with a layer stackup as follows: a polyimide substrate (25 µm) between two layers of electroless nickel immersion gold-finished copper (18 µm/layer, 12 µm Cu + 6 µm Au each). Polyimide layers (27.5 µm/layer) above the finished copper on both sides served as solder mask. Devices were depaneled using UV (355 nm) laser ablation (LPKF; Protolaser U4) and cleaned by hand in isopropanol (IPA). Surface mount components were placed and reflowed manually with temperature-stable solder paste (Chip Quick; TS391LT). After assembly, devices were cleaned once more with IPA.

Wearable mesh fabrication
Flexible mesh designs were drawn in two sizes (small/medium and large/extra-large). Mesh drawings were exported from 2D CAD (AutoDesk, AutoCAD) and imported into 3D modeling software (Dassault Systèmes, Solidworks) for extrusion. Stereolithography (STL) files generated from the 3D model were imported into 3D slicing software (BambuLab, BambuStudio) to generate machine code for a fusion deposition modeling 3D printer (BambuLab, X1-C). A TPU filament (NinjaTek, NinjaFlex) was printed at 100 mm/s and 240 °C with a bed temperature of 45 °C. After printing, segmented sections of the mesh structure were joined by melting TPU material together at junctions to form the completed linear structures.

Wearable device electronics were embedded into the 3D-printed TPU mesh. After embedding, the electronics were covered in a transparent, flexible, UV-curable flexible resin (3DMaterials; SuperFlex) dyed white (Alumilite, Resin Dye), to provide wear resistance and protect them from the external environment. The resin-covered electronics were cured with a UV lamp (24 W) for ten minutes.

Wearable device power consumption characterization
A BEAD was programmed with firmware to perform on-device inference. The device was worn on the thigh of a member of the study team. Using an STLINK-V3PWR and STM32CubeMonitor-Power, the BEAD was powered on, with the STLINK sampling current consumption at 10 kHz with Vout at 1.9 V.

After initial boot, the BEAD entered sleep mode. The study team member then took steps in place until the onboard IMU’s step detection triggered the µC to begin step data collection, which took place over ten seconds. The device autonomously performed inference and then returned to sleep mode. Data collection was terminated.

The BEAD was then reflashed with firmware that transmitted raw angular velocity data over BLE instead of performing on-device inference. The setup was performed as above, with the study team member additionally initiating a connection with the BEAD using an iPhone 14 Pro Max and Light Blue app before stepping in place to trigger data collection. The BEAD transmitted step data off device before returning to sleep. Data collection was terminated.

Training dataset creation
Angular velocity data from upper thigh placed devices were isolated from each original cohort subject’s continuous 60-s walk dataset. Subject data were separated into individual steps and normalized using an automated MATLAB (r2024b) script, with all single steps aggregated into the training dataset (n = 2057 steps). The individual step snippets were manually reviewed to remove any erroneously tagged peaks or step boundary detection errors. The dataset was split 70–30 for training and validation. Labels were generated per subject according to their FFP status determined from in vivo studies.

Model creation and deployment
Single-step angular velocity data were isolated from continuous walk segments from 12/16 subjects in the initial cohort (4 subjects did not complete the grip strength evaluation and frailty status could not be determined). The single-step dataset was normalized in time and magnitude and split 70–30 into training and validation sets.

Using Keras 3.11.3 and Python version 3.10.0, a MINIROCKET transform was fitted to the training data. The biases and kernels for the transformer (k = 420 kernels, 16 max dilations per kernel) were exported as arrays and hardcoded into the wearable device’s firmware. A time series RF classifier was trained off device on a desktop workstation using the transformed training dataset and validated using the transformed test set. During training, fivefold stratified cross-validation was employed and model parameters adjusted accordingly to mitigate model overfitting on training data and improve generalizability. The trained model was quantized using the open-source artificial neural network library Keras and converted into Open Neural Network Exchange (version 1.17.0) format, floating-point layers compressed/optimized using STMicroelectronics’ X-Cube-AI version 9.0.0 toolset, and deployed onto a microcontroller. To mitigate the effect of class imbalance on the model, balanced class weights and randomly shuffled, stratified sampling of the training dataset were used during model training. Class weights were set to be inversely proportional to frequency of the class in the training set. This imposed a greater penalty on misclassification of the minority class (pre-frail) and encouraged better recall and f1-score.

Optimization of MINIROCKET for resource constrained devices
Using Python version 3.10.0, default parameters of a MINIROCKET transform were adjusted to minimize compute time and RAM and flash memory footprint. Predictive performance was evaluated using randomly shuffled training and test sets as during model creation. Final parameters (420 kernels, 16 max dilations per kernel) were chosen to maintain average accuracy above 90% across shuffled test sets, ensuring high generalizability while mitigating overfitting to the training set.

On-device inference
When the BEAD’s onboard IMU recognizes walking/running activity (through its proprietary embedded decision tree model analyzing its accelerometer signals), the IMU sends a hardware-level interrupt to the onboard microcontroller to wake it up. The microcontroller then enables the IMU’s gyroscope and buffers ten seconds of 16-bit angular velocity data sampled at 200 Hz.

Individual step isolation is then performed by finding peaks in the angular velocity buffer (local maxima with at least 50 degrees per second angular velocity and ~1.1 s inter-peak spacing, parameters determined from the training dataset) and copying 218-sample step snippets centered at the index of each peak to a new buffer.

The sample times and angular velocity from a step are normalized [0,1] and transformed using the minimized, embedded MINIROCKET to extract features. The features are then passed to the on-device random forest classifier, with the output class timestamped and stored in on-device non-volatile memory. All steps detected in the buffer are analyzed sequentially. During this time, the IMU’s gyroscope remains active and continues to collect samples. Once the microcontroller has processed all steps, it retrieves any samples from the gyroscope and continues feature extraction and inference until the IMU detects that the wearer has stopped moving. The microcontroller then performs any last inferences, disables the IMU’s gyroscope to save power, and enters a low power sleep until motion is detected again.

Walking speed versus current consumption
Current consumption by a BEAD was monitored using a source measurement unit (SMU) (STMicroelectronics, STLINK-V3PWR) and STM32CubeMonitor-Power software version 1.2.1. The output voltage of the SMU was adjusted to 1.9 V, the 1.8 V operating voltage of the BEAD with overhead for the LDO’s (Low dropout voltage regulator) dropout voltage. The SMU recorded sourced current with 50 kHz sampling rate. A member of the study team wore a BEAD above the knee and stood adjacent to a treadmill (ASUNA, 8730). The treadmill speed was set to 0.6 mph, and the study team member stepped onto the treadmill and walked at speed for 60 s. The team member then stepped off the treadmill for ten seconds while the speed was adjusted to 1.2 mph. The team member then stepped onto the treadmill and walked at speed for 60 s. This procedure was repeated for 1.8, 2.4, and 3.0 mph.

On-device inference was triggered automatically by the BEAD IMU’s activity detection, with results logged to on-device non-volatile memory. For off-device inference, the same setup was used with a BLE connection established before the initial 0.6 mph test. During off-device inference measurements, the BEAD transmitted continuous raw angular velocity data off-device to a laptop.

IMU data fidelity versus system voltage
The same SMU, BEAD, and treadmill setup was used as during the walking speed versus current consumption test. A member of the study team wore the BEAD above the knee while walking on the treadmill at 1.8 mph. Continuous gait data rate was measured while the output voltage of the SMU powering the wearable was adjusted to 1.8, 3.0, 3.3, and 3.6 Volts.

In vivo device validation
A second cohort of subjects (N = 14) were recruited for in vivo device validation, subject to the same selection criteria and methodology as the gold standard validation cohort. All subjects provided both written and verbal consent to the experiments. Subjects in the second cohort wore only one device, on the thigh of their preference. The device performed continuous angular velocity data recording and on-device step isolation/inference. Data and inference results collected during 60 s continuous walk test were isolated and used to generate the plots in Fig. 5c.

Ten-day continuous wear trials
All on-body device characterization experiments were approved by a University of Arizona Internal Review Board, study #STUDY00005692. Subjects provided both written and verbal consent to the experiments.

Using thigh measurements taken from a member of the study team, a BEAD was assembled and flashed with firmware for on-device inference. The BLE advertising interval was modified from the default 400 ms to 10,024 ms to conserve power, since regular BLE connections are not necessary. The device was worn on the thigh by a member of the study team for ten days.

During the test, the device remained in a low power mode until the IMU accelerometer detected steps. The IMU then raised a hardware interrupt to wake the µC. The µC then enabled the IMU’s gyroscope and recorded angular velocity data for ten seconds while the subject took steps, sleeping between data ready interrupts from the IMU. After ten seconds, the data was processed to identify and extract steps, which were transformed using MINIROCKET and fed to the on-device RF classifier. Inference results and timestamps were stored in nonvolatile memory on the device. If the wearer remained in motion, data collection and processing continued, otherwise, the device returned to low power mode.

A commercial 915 MHz power caster (Powercast, TX91501B) was placed under the study team member’s office desk. The wearable was recharged during the work week while the team member was at their desk. The wearable device was not recharged during the weekend (days 3 and 4) or when the team member was out of office (day 6). No step predictions are logged on day 3, as the wearer performed vigorous non-running sport during most of the day and led a more sedentary lifestyle in the evening. Aggregated inference data were offloaded over BLE at regular intervals. The BEAD battery voltage was also measured at these times with a multimeter (AstroAI, AM33B), timestamped, and recorded.

Statistical analysis
All statistical analyses were carried out in MATLAB (R2024b). Gold standard validation used continuous gait data from co-located biosymbiotic devices and gold standard LEGSys devices during a single session per-subject (N = 16) in a medical setting. Gait metrics were derived for each subject and device type. An ordinary unpaired two-tailed t-test was performed to determine statistical significance between derived gait metrics from each device type. Effect size was calculated using ordinary Cohen’s d (mean difference between two groups divided by the pooled standard deviation). All bar charts are shown as mean +/− standard deviation. Statistical significance is demonstrated as p* < 0.05, p** < 0.01, p*** < 0.001, and p**** < 0.0001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This article is tackling a very important problem in the AI and wearables field which limits their integration into clinical practice.\n• What are the noteworthy results?\nThe concept of a new wearable device for frailty measurement is proposed that has some advantages over existing wearables, mainly in terms of size and recharging mechanism.\n• Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\nThe work is interesting, and the idea has some novelty over the state of art in the combination of a few new elements. For example, Canary Medical/Zimmer Biomet has a commercially available implantable knee sensor for joint arthroplasty which also charges by placing a device under the patient’s bed without human intervention. OPUM Technologies has a wearable sensor with edge AI that does real-time gait analytics and exercise recognition to reduce the data transmitted back to the patient’s phone via BLE in order to preserve battery life. It seems frailty measurement is also known in-clinic. So, the novelty would be in combining all these things, however it seems the paper does not actually report long-term frailty measurement, the only long-term operation data is with one healthy subject for 10 days.\n• Does the work support the conclusions and claims, or is additional evidence needed?\nTo prove the device is actually able to do long term frailty monitoring, further results of this would need to be collected and presented. For example, the acceptability of a frail patient wearing the sensor is not tested, let alone over a period of time at home.\n• Are there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\nNot that I could determine, although some more details on the specific gait and activity detection algorithms would be helpful\n• Is the methodology sound? Does the work meet the expected standards in your field?\nThe methodology is sound, but does fall short in validating the proposed novelty of the research\n• Is there enough detail provided in the methods for the work to be reproduced?\nNo, more details would be needed.\n• Other comments\nInclude number of participants and other quantitative data in the abstract. What were the results? Did it work?\nIntro: Authors say “…requiring no interaction by the wearer at any point, including during recharging and data collection”\nHowever recharging and data collection are examples of user interaction. Where does the BLE go? How does that get back to the clinic? If the device is shipped how does the user setup the charger under their seat?\nPg 3. These statements have no references.. “On-device continuous step isolation and inference significantly reduces the amount of data transmitted by the device, conserving battery life, enhancing data privacy, and providing quantifiable long-term metrics with an overall accuracy greater than 90% when classifying between healthy and pre-frail steps. Studies with healthy and pre-frail subjects aged 65 and older demonstrate fidelity on-par with current clinical gold standard gait analysis devices, and showcase device ability to perform real time, exclusively on-device inference for continuous frailty status determination”\nThe authors say “The current clinical standard for frailty diagnosis, illustrated Fig. 1A, is reactionary rather than preventive, with diagnoses often only occurring after incidental fall or hospitalization.” The new proposed method as per Figure 1 is also reactionary, as the gait assessment appears to happen after a fall and initial consultation. All this new method would do is remove one follow up with an in-clinic assessment, with an in-home assessment. If anything this actually delays diagnosis as at home measurement takes longer than in clinic assessment, and will no doubt have some usability and setup issues with elderly folks.\nAuthors should add some downsides of wearables to provide a balance, e.g. skin attachment, slipping, rubbing/comfort, social stigma etc\nResults section\nGold standard. When were results taken from Bio sensors? If the goal is to show they can last for long periods then the data should be collected after say 60 days and/or with low charge, not when fully charged.\nMethods\nSeems like methods come after results section, doesn’t make sense? Also some methods are in results section?\nWhat is the functionality of the mesh fabrication, not clear what that is for and who it fits into the sensor design, manufacture and operation.\nNo clear description of electronics circuit and sensor design, i.e. controller specification/model, power storage, sensor architecture etc\nNo clear description of algorithms for step count, walking speed and sit to stand detection. I found some description but it’s in the results section\nSeems like the sensor parameters (BME rate) were adjusted from the gold standard testing to 10-day tests, this doesn’t make the gold standard representative of at home use.", "reviewer_2": "1. The manuscript highlights its main contribution as enabling a transition from reactive, post-hospitalization frailty diagnosis to preventive, home-based diagnostics. The topic should be of interest to the readers of NC, and the paper is generally well written.\nSome comments are provided below for consideration.\n1. However, from Figure 1A it appears that patients are still prescribed the device only after a fall or hospitalization, following a clinical diagnosis. This raises a quick question: if such reactive clinical events still trigger device usage, how can the approach be truly considered preventive rather than reactive? The authors should consider clarifying under what circumstances the device would be deployed in practice, and whether there is a clear pathway for preventive adoption in at-risk but not yet hospitalized individuals.\n2. In the model training process, the dataset contains a strong imbalance between healthy and pre-frail subjects (16 vs. 4). Such imbalance may risk biasing the classifier towards the majority class. It is recommended that the authors explore strategies to mitigate this issue, such as stratified sampling, data resampling, or synthetic minority oversampling methods (e.g., SMOTE), to avoid skewing results and to better validate the robustness of the model.\n3. The work relies on a single 70/30 train-test split for model evaluation. While this provides an initial performance estimate, it does not fully rule out overfitting or ensure generalizability, especially given the small sample size. Cross-validation (e.g., leave-one-subject-out) or external validation would strengthen the claims regarding model robustness and clinical applicability.\n4. Beyond cross-validation, the inclusion of learning curves would provide additional evidence regarding the model’s bias–variance behavior and its capacity to generalize with more data.\n5. Although the dataset includes a relatively large number of gait segments, these segments are not truly independent samples, as many originate from the same individuals. This could raise the risk that the model may be overfit to subject-specific characteristics, thereby limiting its generalizability to new individuals. Please discuss.\n6. While the manuscript provides some waveform-level interpretations, the discussion remains largely algorithm-centric. A clearer articulation of how per-step inferences can be translated into clinically meaningful diagnostic indicators or longitudinal trend reports would strengthen the clinical relevance of the work. Please think about and discuss this.\n7. The evaluation of the reported ~21% energy savings for on-device inference is confined to short-term, laboratory-based measurements. In real-world conditions across varying walking speeds, activity types, and usage durations, the device’s overall power consumption may differ substantially. A more comprehensive energy analysis under daily living conditions is needed."}, "author_response": "Response to comments of Reviewer 1:\nGeneral comments:\nThis article is tackling a very important problem in the AI and wearables field which limits their\nintegration into clinical practice.\nWhat are the noteworthy results?\nThe concept of a new wearable device for frailty measurement is proposed that has some\nadvantages over existing wearables, mainly in terms of size and recharging mechanism.\nOur Response\nWe thank the reviewer for their comments and positive assessment of the manuscript’s\nsignificance. We appreciate the detailed comments and have provided responses below.\nComment 1\nWill the work be of significance to the field and related fields? How does it compare to the\nestablished literature? If the work is not original, please provide relevant references.\nThe work is interesting, and the idea has some novelty over the state of art in the combination of\na few new elements. For example, Canary Medical/Zimmer Biomet has a commercially\navailable implantable knee sensor for joint arthroplasty which also charges by placing a device\nunder the patient’s bed without human intervention. OPUM Technologies has a wearable sensor\nwith edge AI that does real-time gait analytics and exercise recognition to reduce the data\ntransmitted back to the patient’s phone via BLE in order to preserve battery life. It seems frailty\nmeasurement is also known in-clinic. So, the novelty would be in combining all these things,\nhowever it seems the paper does not actually report long-term frailty measurement, the only\nlong-term operation data is with one healthy subject for 10 days.\nOur Response\nWe thank the reviewer for their comment, thorough comparison with clinically available\ntechnologies, and suggestion to highlight the novelty of this work. As mentioned, there are no\ntechnologies which can assess frailty outside of the clinic, which significantly limits diagnostics,\nespecially in resource-constrained communities. This platform would be the first device to provide\nseamless diagnostic capacity for at-home frailty assessment. The OPUM technologies wearable\nsensor requires significant patient interaction to collect data using a phone application and to\nremove and replace the sensor for battery charging.\nThe novelty of the demonstrated device is in the minimal interaction needed by the wearer. 1.\nThis technology utilizes far field wireless power transfer that allows the user to keep the device\non 24/7 without removal for battery charging, critical for frail patient populations with limited\nmobility. 2. The device has a fundamentally different form factor than common brick-and-strap\ndevices (like the OPUM device noted). Together with the wireless charging, this breathable,\nwater- and sweatproof form factor enables the wearer to keep the device on for weeks without\ninteracting with it. 3. The onboard AI allows for inference on device. This has multiple benefits: (i)\nIt saves energy on the wearable, as shown by the measurements in Fig. 3C. (ii) It omits the need\nto transmit raw biosignals from the device to the cloud which can quickly result in large data\namounts that are expensive to handle due to high bandwidth connectivity requirements, which\ncan be challenging with a mobile phone as an access point, especially in rural communities. (iii)\nInference data can be transferred directly into the Electronic Healthcare Record (EHR), making\nintegration into standard of care and alerting systems much easier.\nConceptually we showcase the BEAD as a device for frailty assessment in this work, however,\nthe device architecture, model training approach and on-device model deployment have broad\nutility for long-term assessment of high data rate biosignals where machine learning can\ncondense large data streams into clinically relevant, actionable metrics.\nWe have modified the manuscript to improve clarity and emphasize the novelty of the work.\nModifications to the Manuscript\nPage 18 Line 346\nSince the biosymbiotic wearable deviceBEAD requires no interaction from the patient beyond\ninitial placement of the powercaster at a desk or bedside, is recharged wirelessly, and can be worn\nwhile bathing, the patient would simply wear the device on their thigh and continue with their\nnormal routine.\nPage 18 Line 349\nDuring habitual gait, i.e., step activity recognized by the BEAD’s onboard IMU, the device wakes\nup, records continuous angular velocity of the leg, isolates individual steps, and performs\ninference, aggregating the results over time and transmitting them off-device when possible,\npotentially integrating them directly into EHRs / electronic patient health information (ePHI)28 or\ntransmitting them directly to the telehealth provider.\nPage 22 Line 423\nBiosymbiotic wearable technologies with on-device ML offer substantial advantages over existing\nwearable devices, allowing patients in low-resource settings to receive clinical-grade frailty\nassessment. The soft, breathable, lightweight form factor that defines biosymbiotic architecture is\nadhesive-free and tailored to conform to each patient for maximum comfort, allowing for weeks-\nlong wear without irritation. Far-field power harvesting enables continuous operation without\nremoval for charging (which is typically required by conventional brick-and-strap wearable\ndevices), critical for potentially frail individuals with limited mobility. Exclusively on-device\ninference removes the requirement for constant connectivity that is often limited or unavailable,\nespecially in rural communities, and eliminates the need for storing the large amounts of data on\ndevices or costly cloud infrastructure, and allows for straightforward integration into EHRs.\nComment 2\nDoes the work support the conclusions and claims, or is additional evidence needed?\nTo prove the device is actually able to do long term frailty monitoring, further results of this\nwould need to be collected and presented. For example, the acceptability of a frail patient\nwearing the sensor is not tested, let alone over a period of time at home.\nOur Response\nWe thank the reviewer for their comment and concerns for device acceptability and practical use.\nWe acknowledge that demonstrating long-term in-home wear by a frail patient would be a strong\nvalidation of the technology. However, user acceptability studies, clinical validation with statistical\nsignificance, and healthcare system integration (which would be required to carry out such studies\nefficiently) are typically part of commercialization efforts beyond exploratory technology\ndemonstration such as the device presented in this work.\nFor context, considerations that frame the magnitude of such a study: given the population and\ndemographics of Tucson, Arizona (547,000 general population with 17% age 65+)39, the general\nincidence of frailty (15%)40,41, and the number of frail subjects who are admitted to a hospital at\nleast once in a year (42% frail with >=1 hospital visit/year)40, the subject pool is rapidly limited:\n547,000 * 0.17 * 0.15 * 0.42 = 5,858 patients\nFactoring in the number of subjects who would benefit from intervention (40%)R1, the ability to\ncontact and recruit these subjects for a study (estimate ~30% of Tucson seen by all Banner\nUniversity Medical Centers combinedR2, 44% willingness to enrollR3), and general study retention\n(70%)R4, 5,858 * 0.4 * 0.3 * 0.44 * .7 = 216 potential test subjects, assuming exceptional enrollment\nefficiency and broad clinician subscription to the trial across the entire health system in Tucson.\nIt becomes clear that this clinical trial would need to be a multi-institution, multi-city effort. A trial\nthat would yield many interesting results, improve the model, and could set the foundation of\nestablishing and translating a new clinical tool, however, is well beyond this initial tech\ndemonstration and we acknowledge that subject recruitment is a limitation of this study.\n39. U.S. Census Bureau (2023). American Community Survey 1-year estimates. Retrieved from\nCensus Reporter Profile page for Tucson, AZ. Accessed 09/18/2025.\nhttp://censusreporter.org/profiles/16000US0477000-tucson-az/\n40. Bandeen-Roche K, Seplaki CL, Huang J, Buta B, Kalyani RR, Varadhan R, Xue QL,\nWalston JD, Kasper JD. Frailty in Older Adults: A Nationally Representative Profile in the\nUnited States. J Gerontol A Biol Sci Med Sci. 2015 Nov;70(11):1427-34. doi:\n10.1093/gerona/glv133. Epub 2015 Aug 21. PMID: 26297656; PMCID: PMC4723664.\n41. Kim, D. H. & Rockwood, K. Frailty in Older Adults. N. Engl. J. Med. 391, 538–548 (2024).\nR1. Tze Pin Ng, Liang Feng, Ma Shwe Zin Nyunt, Lei Feng, Mathew Niti, Boon Yeow Tan,\nGribson Chan, Sue Anne Khoo, Sue Mei Chan, Philip Yap, Keng Bee Yap, Nutritional,\nPhysical, Cognitive, and Combination Interventions and Frailty Reversal Among Older\nAdults: A Randomized Controlled Trial, The American Journal of Medicine, Volume 128,\nIssue 11, 2015, Pages 1225-1236.e1, ISSN 0002-9343,\nhttps://doi.org/10.1016/j.amjmed.2015.06.017.\nR2. American Medical Association. Residency & Fellowship Programs Database, Banner-\nUniversity Medical Center Tucson, Banner-University Medical Center South.\nhttps://freida.ama-assn.org/institution/030506. Accessed 09/08/2025.\nR3. Sood A, Prasad K, Chhatwani L, Shinozaki E, Cha SS, Loehrer LL, Wahner-Roedler DL.\nPatients' attitudes and preferences about participation and recruitment strategies in clinical\ntrials. Mayo Clin Proc. 2009 Mar;84(3):243-7. doi: 10.4065/84.3.243. PMID: 19252111;\nPMCID: PMC2664601.\nR4. Teague, S., Youssef, G.J., Macdonald, J.A. et al. Retention strategies in longitudinal cohort\nstudies: a systematic review and meta-analysis. BMC Med Res Methodol 18, 151 (2018).\nhttps://doi.org/10.1186/s12874-018-0586-7\nPage 23 Line 443\nRealizing this clinical potential will require user acceptability studies and a multi-institution multi-\nsite clinical trial with a significantly larger, diverse cohort; longitudinal in-home tracking of frailty\nprogression is needed to create a generalizable, clinically relevant model. Nevertheless, Tthe\napproach demonstrated here has the potential to overcome current operational, clinical, user and\npayer challenges by enabling seamless diagnostics with minimal interruption of daily routine and\nclinical practice.\nComment 3\nAre there any flaws in the data analysis, interpretation and conclusions? Do these prohibit\npublication or require revision?\nNot that I could determine, although some more details on the specific gait and activity detection\nalgorithms would be helpful\nOur Response\nWe thank the reviewer for their comment and suggestions to improve clarity surrounding gait and\nactivity detection.\nActivity recognition (still/sitting, walk, run) is performed by the BEAD’s onboard inertial\nmeasurement unit (IMU). This IMU (Bosch, BM270) consists of a gyroscope, an accelerometer,\nand an internal controller that allows the user to enable or disable the gyroscope and/or\naccelerometer at any time. The internal controller embeds a proprietary decision tree model that\ncontinuously analyzes the accelerometer (which is always active for this use case) signal to\nclassify/recognize activities. Activity recognition happens internal to the IMU and utilizes very little\npower (~72 µW max), allowing the IMU to signal to the BEAD’s microcontroller when the wearer\nhas started or stopped walking.\nOnce step activity is detected, the microcontroller enables the IMU’s gyroscope (which consumes\nnontrivial amounts of power, ~600 µW average, and is normally disabled to conserve power when\nthe wearer is still) and buffers incoming angular velocity data for ten seconds while the wearer\nwalks around. Once the buffer is full, the microcontroller then performs peak finding algorithms,\nstep extraction, data normalization, feature extraction, and inference, storing the classification\nresults in non-volatile memory. This process continues until the IMU signals that the wearer has\nstopped moving, where the microcontroller then finishes analyzing any unprocessed steps,\ndisables the gyroscope, and enters a low power mode.\nWe have amended the manuscript to better elaborate on these algorithms.\nModifications to the Manuscript\nPage 10 Line 181\nUsing tThe BEAD’s onboard IMU’s (Bosch, BMI270) embeds a proprietary decision tree model\nthat continuously evaluates accelerometer signals to detect motion and perform walking/running\nactivity detection recognition, generating hardware-level interrupts on activity change. Using this\ninterrupt allows the wearable device to enter a low-power mode that consumes 10x less power\nwhile maintaining quick wake timing (< 35 ms from low-power standby to normal operation, fig.\nS45) to collect real time gait data immediately once the wearer begins walking, with no additional\neffort or interaction required to begin data logging.\nPage 18 Line 349\nDuring habitual gait, i.e., step activity recognized by the BEAD’s onboard IMU, the device wakes\nup, records continuous angular velocity of the leg, isolates individual steps, and performs\ninference, aggregating the results over time and transmitting them off-device when possible,\npotentially integrating them directly into EHRs / electronic patient health information (ePHI)28 or\ntransmitting them directly to the telehealth provider.\nModifications to the Supplementary Information (Methods)\nPage 29 Line 574\nOn-device inference\nWhen the BEAD’s onboard IMU recognizes walking/running activity (through its proprietary\nembedded decision tree model analyzing its accelerometer signals), the IMU sends a hardware-\nlevel interrupt to the onboard microcontroller to wake it up. The microcontroller then enables the\nIMU’s gyroscope and buffers ten seconds of 16-bit angular velocity data sampled at 200 Hz.\nIndividual step isolation is then performed by finding peaks in the angular velocity buffer (local\nmaxima with at least 50 degrees per second angular velocity and ~1.1 seconds inter-peak spacing,\nparameters determined from the training dataset) and copying 218-sample step snippets centered\nat the index of each peak to a new buffer.\nThe sample times and angular velocity from a step are normalized [0, 1] and transformed using\nthe minimized, embedded MINIROCKET to extract features. The features are then passed to the\nembedded random forest classifier, with the output class timestamped and stored in on-device non-\nvolatile memory. All steps detected in the buffer are analyzed sequentially. During this time, the\nIMU’s gyroscope remains active and continues to collect samples. Once the microcontroller has\nprocessed all steps, it retrieves any samples from the gyroscope and continues feature extraction\nand inference until the IMU detects that the wearer has stopped moving. The microcontroller then\nperforms any last inferences, disables the IMU’s gyroscope to save power, and enters a low power\nsleep until motion is detected again.\nComment 4\nIs the methodology sound? Does the work meet the expected standards in your field?\nThe methodology is sound, but does fall short in validating the proposed novelty of the research\nOur Response\nWe thank the reviewer for their comment and recognition of the methodology.\nWe want to emphasize that the primary novelty of this work lies primarily in the realization of on-\ndevice ML on a continuously wearable device and its demonstration as continuous diagnostic tool\nthat does not require interaction by the wearer. This demonstration is a result of holistic system-\nlevel engineering with low power design and soft electronics that enable this demonstration. To\nhighlight this novelty, we have added a table to the SI that compares existing technologies against\nthe demonstrated device, Supplementary Table S2. Comparison with existing gait-based frailty\nassessment wearables.\nModifications to the Manuscript\nPage 13 Line 233\nThe significance of this device compared to existing wearable technologies for frailty\ndetection3,5,7,12,27,30–35, see Supplementary Table S2, is clearly demonstrated by its ability to\nautonomously and efficiently evaluate a patient’s gait in real time without any external\ninfrastructure.\nModifications to the Supplementary Information (Figures)\nSupplementary Table S2\nSample Device Wearable Sensing ML On-device Continuous\nCategory Ref. Year\nImage type target type AI operation\n[This Biosymbiotic Thigh angular\n- RF Y Y\nWork] mesh velocity\nReal-time AI\nAnalysis\nBrick-and- Ankle angular\n[12] 2025 RF N N\nstrap velocity\nCommercial 6-axis wrists,\nSVM,\n[31] 2022 adhesive- ankles, waist N N\nDNN\nbased IMU\nBatch processing\nAI analysis\nCommercial 3-axis wrist\n[32] 2022 brick-and- and low back GNB N N\nstrap acceleration\nCommercial\n3-axis wrist\n[30] 2022 brick-and- None N/A N\nacceleration\nstrap\nBrick-and- 6-axis ankle\n[33] 2021 None N/A N\nstrap IMU\nStatistical\nanalysis only\nCommercial\n3-axis chest\n[34] 2018 pendant None N/A N\nacceleration\nnecklace\nCommercial 6-axis shanks,\n[35] 2015 brick-and- thighs, & None N/A N\nstrap torso IMU\nSupplementary Table S2. Comparison with existing gait-based frailty assessment wearables.\nComparison of BEADs with published literature utilizing wearable devices for gait-based frailty\nassessment. IMU = Inertial Measurement Unit (Accelerometer and Gyroscope). RF = Random\nForest, SVM = Support Vector Machine, DNN = Deep Neural Networks, GNB = Gaussian Naive\nBayes. [12] image reprinted with permission from Elsevier, license number 6122160243860. [31]\nimage reprinted under fair use. [32] image reprinted under fair use. [30] image reproduced with\npermission from PLOS One CC BY 4.0. [33] image reproduced with permission from MDPI CC\nBY 4.0. [34] image reprinted with permission from MDPI CC BY 4.0. [35] image reprinted from\n[36] with permission from MDPI CC BY 4.0.\nComment 5\nIs there enough detail provided in the methods for the work to be reproduced?\nNo, more details would be needed.\nOur Response\nWe thank the reviewer for their comment and suggestion to provide more details to enhance\nreproducibility. We have revised the manuscript to better detail step / feature detection algorithms\n(see response to Comment 3 above) and model training (see response to Reviewer 2 Comments\n3-5). We believe these revisions have significantly improved the reproducibility and clarity of the\nwork and have copied them below for reference.\nModifications to the Manuscript\nPage 10 Line 173\nOnboard wireless far-field power harvesting highlighted using commercially available, FCC-\napproved power casting systems enable wireless recharging at-distance, such as at a work desk or\nduring sleep, without wearer interaction.15,16 These power casting systems plug into any wall outlet\nand require only a quick initial setup for continuous operation: they are simply plugged in and\nrotated to face the BEAD, providing wireless power up to 2m away.15,16\nPage 10 Line 181\nUsing tThe BEAD’s onboard IMU’s (Bosch, BMI270) embeds a proprietary decision tree model\nthat continuously evaluates accelerometer signals to detect motion and perform walking/running\nactivity detection recognition, generating hardware-level interrupts on activity change. Using this\ninterrupt allows the wearable device to enter a low-power mode that consumes 10x less power\nwhile maintaining quick wake timing (< 35 ms from low-power standby to normal operation, fig.\nS45) to collect real time gait data immediately once the wearer begins walking, with no additional\neffort or interaction required to begin data logging.\nModifications to the Supplementary Information (Methods)\nPage 24 Line 470\nStep count was extracted from the angular velocity data of each thigh-located device (one gold-\nstandard LEGSys device and one biosymbiotic device per thigh, four devices total). Step count\nextraction was automated using a MATLAB script and validated by a member of the study team;\nthe MATLAB script isolated per-device all peaks in the angular velocity data with magnitude at\nleast 50 degrees per second and inter-peak spacing at least 1 second apart.\nPage 25 Line 477\nWalking speed was determined from this test by manual analysis of the angular velocity data of\neach thigh-located device (one gold-standard LEGSys device and one biosymbiotic device per\nthigh, four devices total): the distance covered during the walk (3m from chair to cone + 3m from\ncone to chair) was divided by the time from toe-off of the first step after standing up to heel-strike\nof the last step before turning around to sit down. These times were extracted by manual inspection\nof the angular velocity waveforms.\nPage 27 Line 537\nTraining dataset creation\nAngular velocity data from upper thigh placed devices were isolated from each original cohort\nsubject’s continuous 60-second walk dataset. Subject data were separated into individual steps and\nnormalized using an automated MATLAB script, with all single steps aggregated into the training\ndataset (n = 2057 steps). The individual step snippets were manually reviewed to remove any\nerroneously tagged peaks or step boundary detection errors. The dataset was split 70-30 for training\nand validation. Labels were generated per-subject according to their FFP status determined from\nin vivo studies.\nPage 28 Line 555\nDuring training, 5-fold stratified cross-validation was employed and model parameters adjusted\naccordingly to mitigate model overfitting on training data and improve generalizability.\nPage 28 Line 560\nTo mitigate the effect of class imbalance on the model, balanced class weights and randomly\nshuffled, stratified sampling of the training dataset were used during model training. Class weights\nwere set to be inversely proportional to frequency of the class in the training set. This imposed a\ngreater penalty on misclassification of the minority class (pre-frail) and encouraged better recall\nand f1-score.\nPage 29 Line 574\nOn-device inference\nWhen the BEAD’s onboard IMU recognizes walking/running activity (through its proprietary\nembedded decision tree model analyzing its accelerometer signals), the IMU sends a hardware-\nlevel interrupt to the onboard microcontroller to wake it up. The microcontroller then enables the\nIMU’s gyroscope and buffers ten seconds of 16-bit angular velocity data sampled at 200 Hz.\nIndividual step isolation is then performed by finding peaks in the angular velocity buffer (local\nmaxima with at least 50 degrees per second angular velocity and ~1.1 seconds inter-peak spacing,\nparameters determined from the training dataset) and copying 218-sample step snippets centered\nat the index of each peak to a new buffer.\nThe sample times and angular velocity from a step are normalized [0, 1] and transformed using\nthe minimized, embedded MINIROCKET to extract features. The features are then passed to the\non-device random forest classifier, with the output class timestamped and stored in on-device non-\nvolatile memory. All steps detected in the buffer are analyzed sequentially. During this time, the\nIMU’s gyroscope remains active and continues to collect samples. Once the microcontroller has\nprocessed all steps, it retrieves any samples from the gyroscope and continues feature extraction\nand inference until the IMU detects that the wearer has stopped moving. The microcontroller then\nperforms any last inferences, disables the IMU’s gyroscope to save power, and enters a low power\nsleep until motion is detected again.\nComment 6\nOther comments\nInclude number of participants and other quantitative data in the abstract. What were the\nresults? Did it work?\nOur Response\nWe thank the reviewer for their comment and suggestion to improve legibility of the abstract. We\nhave now added quantitative data and key results.\nModifications to the Manuscript (Abstract)\nPage 1 Line 22\nWe utilize this framework to perform gait-based frailty assessment during in vivo trials (N =20)\n1\nwith results that match gold standard diagnostic tools. Clinical utility, and model stability, and\non-device inference are validated through in vivo trials (N =14) and ten-day-long extended wear\n2\nexperiments, demonstrating continuous operation without wearer intervention and autonomous\nlongitudinal analysis of high sampling rate biosignals.\nComment 7\nIntro: Authors say “…requiring no interaction by the wearer at any point, including during\nrecharging and data collection” However recharging and data collection are examples of user\ninteraction. Where does the BLE go? How does that get back to the clinic? If the device is\nshipped how does the user setup the charger under their seat?\nOur Response\nWe thank the reviewer for their comment and questions and now highlight the autonomous\nfunctionality of this platform.\nTo clarify, by user interaction we mean steps that a user typically needs to perform when using a\nstandard brick-and-strap device. This includes taking the device off to shower, taking the device\noff to charge, either by plugging in a cord or using a near field charging dock, or using adhesives\nto attach the sensor (similar to a continuous glucose monitor) which need to be replaced every 3-\n5 days.\nWith the BEAD, the wearer does not need to take any actions outside their normal routine for the\ndevice to operate.\nWith our device, recharging is performed passively using far-field power harvesting: a\ncommercially available wireless power caster placed at a work desk or bedside transmits power\nto the wearable up to 2 meters away when the wearer sits or sleeps in front of it. This power caster\nplugs in to any wall outlet and requires little setup; it simply needs to be plugged in facing the\nwearable device.\nData collection is performed on the device as the wearer takes steps, they do not need to push\nany buttons or launch a phone app. The data can be transmitted off the BEAD over BLE via an\ninternet connected device (in most cases a smartphone) which can deposit the analyzed data (#\nof frail steps and normal steps) directly into the electronic health record.\nIt is important to note that with other devices data analysis typically has to be performed in the\ncloud. This requires sending all raw data with high resolution, stresses the connection point (i.e.,\ndrains the smartphone battery and consumes data), and requires computation in the cloud,\nwhich has its own associated resource limits and costs. Hence there is a substantial benefit in\ncomputing on the wearable itself.\nWe have amended the manuscript to more clearly make this distinction.\nModifications to the Manuscript\nPage 6 Line 99\nTherefore, wearable technologies capable of chronic operation22,23, e.g., biosymbiotic electronics,\nare critical for establishing a new standard for frailty determination. Biosymbiotic wearable\ndevicestechnology utilizes soft, conformal 3D-printed mesh bodies, far-field power harvesting,\nand autonomous ultralow-power electronics to create a sensing platforms that can operate\ncontinuously without any interaction required byfrom the wearer.15-20\nPage 10 Line 173\nOnboard wireless far-field power harvesting highlighted using commercially available, FCC-\napproved power casting systems enable wireless recharging at-distance, such as at a work desk or\nduring sleep, without wearer interaction.15,16 These power casting systems plug into any wall outlet\nand require only a quick initial setup for continuous operation: they are simply plugged in and\nrotated to face the BEAD, providing wireless power up to 2m away.15,16\nPage 10 Line 181\nUsing tThe BEAD’s onboard IMU’s (Bosch, BMI270) embeds a proprietary decision tree model\nthat continuously evaluates accelerometer signals to detect motion and perform walking/running\nactivity detection recognition, generating hardware-level interrupts on activity change. Using this\ninterrupt allows the wearable device to enter a low-power mode that consumes 10x less power\nwhile maintaining quick wake timing (< 35 ms from low-power standby to normal operation, fig.\nS45) to collect real time gait data immediately once the wearer begins walking, with no additional\neffort or interaction required to begin data logging.\nPage 11 Line 213\nAnalyzed data, i.e., timestamped inferences, can be offloaded via an internet connected device\nsuch as a smartphone, aggregated into total steps classified as frail versus healthy, and deposited\ndirectly into the electronic healthcare record (EHR), shown in fig. S7A.\nUnlike with traditional brick-and-strap wearable devices, a patient is not required to continuously\ninteract with the BEAD to perform data collection, see fig. S7B for the control flow that enables\nautonomous data collection. They also do not need to remove the device for charging or when\nbathing. Only a brief initial setup of the power caster is required for indefinite collection of high-\nfidelity biosignals, enhancing accessibility, user experience, and improving overall patient\ncompliance.10,15\nPage 18 Line 346\nSince the biosymbiotic wearable deviceBEAD requires no interaction from the patient beyond\ninitial placement of the powercaster at a desk or bedside, is recharged wirelessly, and can be worn\nwhile bathing, the patient would simply wear the device on their thigh and continue with their\nnormal routine. During habitual gait, i.e., step activity recognized by the BEAD’s onboard IMU,\nthe device wakes up, records continuous angular velocity of the leg, isolates individual steps, and\nperforms inference, aggregating the results over time and transmitting them off-device when\npossible, potentially integrating them directly into EHRs / electronic patient health information\n(ePHI)28 or transmitting them directly to the telehealth provider.\nPage 22 Line 423\nBiosymbiotic wearable technologies with on-device ML offer substantial advantages over existing\nwearable devices, allowing patients in low-resource settings to receive clinical-grade frailty\nassessment. The soft, breathable, lightweight form factor that defines biosymbiotic architecture is\nadhesive-free and tailored to conform to each patient for maximum comfort, allowing for weeks-\nlong wear without irritation. Far-field power harvesting enables continuous operation without\nremoval for charging (which is typically required by conventional brick-and-strap wearable\ndevices), critical for potentially frail individuals with limited mobility. Exclusively on-device\ninference removes the requirement for constant connectivity that is often limited or unavailable,\nespecially in rural communities, and eliminates the need for storing the large amounts of data on\ndevices or costly cloud infrastructure, and allows for straightforward integration into EHRs.\nComment 8\nPg 3. These statements have no references.. “On-device continuous step isolation and\ninference significantly reduces the amount of data transmitted by the device, conserving battery\nlife, enhancing data privacy, and providing quantifiable long-term metrics with an overall\naccuracy greater than 90% when classifying between healthy and pre-frail steps. Studies with\nhealthy and pre-frail subjects aged 65 and older demonstrate fidelity on-par with current clinical\ngold standard gait analysis devices, and showcase device ability to perform real time,\nexclusively on-device inference for continuous frailty status determination”\nOur Response\nWe thank the reviewer for their comment. These statements are our claims based on the results\npresented in the paper. Some of these statements have been altered in response to reviewer\ncomments, however, the new statements are supported by the data presented in this work. We\nhave modified the manuscript to more clearly indicate that these are our claims by including\nquantitative data and direct figure references.\nModifications to the Manuscript\nPage 3 Line 74\nOn-device continuous step isolation and inference significantly reduces the amount of data\ntransmitted by the device by nearly 99% (8 bytes for a timestamped step inference vs 436 bytes of\nraw data per step, Fig. 5D), conserving battery life with a 21% decrease in average power\nconsumption (Fig. 3C), enhancing data privacy by minimizing data management requirements,\nand providing quantifiable long-term metrics with an overall accuracy greater than 90% when\nclassifying between healthy and pre-frail steps (Fig. 4E). In vivo Sstudies with healthy and pre-\nfrail subjects aged 65 and older demonstrate gait measurement fidelity on-par with current clinical\ngold standard gait analysis devices (Fig. 2B and 2D), and showcase the BEAD’sdevice ability to\nperform real-time (<330ms from raw data to inference result, fig. S8), exclusively on-device\ninference for continuous frailty status determination.\nComment 9\nThe authors say “The current clinical standard for frailty diagnosis, illustrated Fig. 1A, is\nreactionary rather than preventive, with diagnoses often only occurring after incidental fall or\nhospitalization.” The new proposed method as per Figure 1 is also reactionary, as the gait\nassessment appears to happen after a fall and initial consultation. All this new method would do\nis remove one follow up with an in-clinic assessment, with an in-home assessment. If anything\nthis actually delays diagnosis as at home measurement takes longer than in clinic assessment,\nand will no doubt have some usability and setup issues with elderly folks.\nAuthors should add some downsides of wearables to provide a balance, e.g. skin attachment,\nslipping, rubbing/comfort, social stigma etc\nOur Response\nWe thank the reviewer for their comment and suggestions to improve manuscript clarity and\ncohesion. While Fig. 1A was primarily intended to portray the current state of the art, it clearly is\nconfusing for readers. The point of our device is that it can be used before adverse events\nbecause it simplifies diagnosis if there is a small probability of frailty in an accessible way. We\nhave added and modified text in Fig. 1 and Fig. 5B to indicate the current standard of care and\nbetter highlight the proposed preventive approach, which would occur before a fall or\nhospitalization.\nWe want to point out that biosymbiotic devices are designed to mitigate the issues that current-\ngeneration wearables face, with comfortable, irritation-free, conformal form factors that stay in\nplace and can easily be hidden under clothing, which we show in Fig. 5A.\nRegarding usability and setup issues, biosymbiotic devices operate autonomously and by limiting\nall user interaction to one-time, simple placement of a power caster, we can reduce the barriers\nto diagnosis.\nModifications to the Manuscript\nPage 9 Line 158\nCurrent generation wearable devices face issues with subject interaction through charging\nrequirements, technology management of smartphones or specialized devices, and secure\nplacement and skin attachment, with patient discomfort and irritation often arising from rigid\ndevice bodies, abrasive textile or non-breathable plastic attachment straps, and adhesives which\nneed to be replaced often.23,29 To avoid these common issues, BEADs employ biosymbiotic device\narchitecture, which ensures lightweight comfort and the ability to operate continuously for weeks\nwithout input from the wearer.15,20\nPage 18 Line 339\nThe device introduced in this work, showcased Fig. 5A, provides a new approach for home-based\nfrailty diagnostics in a low-profile, unobtrusive form factor which is easily hidden under clothing,\nshifting point of care away from medical clinics, and enhancing current telehealth offerings.\nModifications to the Manuscript (Figures)\nPage 5 Line 85\nFigure 1\nFigure 1. Continuously operating wearable AI for on-device frailty assessment. A. Illustration\nshowing the reactive current clinical standard for diagnosing frailty. B. Implementation of\nwearable performing on-device inference from IMU recorded gait, wireless far-field charging of\nthe wearable device, and Bluetooth Low Energy data output from wearable.\nPage 21 Line 402\nFigure 5\nFigure 5. Continuous gait monitoring for frailty detection. A. Photograph demonstrating the\nlow profile of BEADs that allows them to be worn discretely under clothing. B. New approach for\nhome-based frailty diagnostics and preventive care. C. Plots showing results of step inference\nduring 60s continuous walk from a healthy (left) subject and a pre-frail (right) subject, with insets\nhighlighting an individual step from each subject. D. Battery voltage and inference results from\nten-day continuous wear on healthy subject.\nComment 10\nResults section\nGold standard.\nWhen were results taken from Bio sensors? If the goal is to show they can last for long periods\nthen the data should be collected after say 60 days and/or with low charge, not when fully\ncharged.\nOur Response\nWe thank the reviewer for their comment and suggestions to enhance the impact of this work.\nWe would like to emphasize that there have been multiple sets of experiments that were\nperformed in this work.\nThe gold standard results were collected during in vivo studies (N =20) in a hospital setting.\nand angular velocity data from leg- and trunk-mounted biosymbiotic devices were recorded\ncontinuously. The purpose of these experiments was simply to 1. demonstrate the sensing fidelity\nof the biosymbiotic platform, not continuous operation (which was the purpose of the 10-day-long\nextended wear experiment) and 2. collect data against the gold standard method of frailty\ndetection to provide a training dataset for development of the on-device diagnostic prediction\nmodel. We have amended the manuscript to improve clarity on these points.\nFor chronic operation tests, the biosymbiotic devices are charged at distance using far-field power\ncasters. If the wearer routinely returns to a charging point (like the bed or the work desk) the\nBEAD remains at a stable battery voltage (~3.7 to 3.9 V), which we show in Fig. 5D.\nHowever, we agree that it is valuable to demonstrate reliable operation of the BEAD across\nvarying levels of charge and have amended the supplementary information to include a new\nfigure, fig. S12, which demonstrates sensing fidelity at different battery voltages, with results\nshowing that the gait sensing fidelity remains unaffected by battery voltage.\nModifications to the Manuscript\nPage 19 Line 383\nThe experiment shows continuous operationhigh fidelity digital gait assessment without impact by\nstate of charge (fig. S12), reliable recharging, and long-term model stability throughout, with only\n~8.6% false positive rate.\nModifications to the Supplementary Information (Figures)\nSupplemental Figure 12\nSupplemental Figure 12. Gait data fidelity is unaffected by battery voltage. A. Habitual gait\nangular velocity data logged over BLE at different system voltages. Samples plotted as discrete\npoints. B. Effective recording data rate of the BEAD (from on-device IMU to microcontroller to\nlaptop logging device over BLE) at varying system input voltages, plotted with standard\ndeviations. Effective sampling rate consistently approaches 200Hz at all system input voltages.\nBLE transmission overhead, which shares CPU resources, introduces minor deviations in effective\ndata rate.\nModifications to the Supplementary Information (Methods)\nPage 30 Line 607\nIMU data fidelity versus system voltage\nThe same source measurement unit, BEAD, and treadmill setup was used as during the walking\nspeed versus current consumption test. A member of the study team wore the BEAD above the\nknee while walking on the treadmill at 1.8mph. Continuous gait data rate was measured while the\noutput voltage of the SMU powering the wearable was adjusted to 1.8, 3.0, 3.3, and 3.6 Volts.\nComment 11\nMethods\nSeems like methods come after results section, doesn’t make sense? Also some methods are in\nresults section?\nOur Response\nWe thank the reviewer for their comment and suggestion to improve manuscript clarity and\nlegibility. The manuscript was submitted according to the Nature Communications style guide, its\nstandard format is to have the Methods at the end, for reference and convenience we have\nincluded below a device-focused articleR5 recently published in Nature Communications. In the\nmain text we sometimes mention methods, such as fabrication or data analysis methods, when it\nis critical to the discussion of the results. We have revised the manuscript to move more\nmethodology to the methods section and streamline discussions.\nR5. Zhou, M., Yan, B., Yang, F. et al. A wireless optogenetic stimulation system for long-term\nfunction evaluation of mice forelimb with sub-nerve resolution. Nat Commun 16, 8702\n(2025). https://doi.org/10.1038/s41467-025-63746-y\nModifications to the Manuscript\nPage 13 Line 237\nTo create the dataset for training a machine learning model, aAngular velocity from upper thigh\nlocatedplaced biosymbiotic devices, as sample data shown in Fig. 4A (left) and device placement\nshown in Fig. 2C, is normalized (Fig. 4A, middle) and used to create the training dataset Fig. 4A\n(right) is isolated from each subject’s continuous 60-second walk dataset.\nPage 13 Line 244\nEach subject’s data are separated into individual steps and normalized, with all single steps\naggregated into the training dataset (n = 2057 steps), shown Fig. 4A (right), and the dataset split\n70-30 for training and validation. Steps are labeled according to each subject’s FFP determined\nduring in vivo studies.\nIllustrated in Fig. 4B, a time series classifier is trained off-device using the open-source artificial\nneural network library Keras on a desktop workstation. Aa linear classifier model trained on a\nworkstation is readily quantized and optimized into memory-efficient C code for deployment on\nresource-constrained embedded devices.\nPage 20 Line 382\nNo step predictions are logged on day 3, as the wearer performed vigorous non-running sport\nduring most of the day and led a more sedentary lifestyle in the evening.\nModifications to the Supplementary Information (Methods)\nPage 27 Line 537\nTraining dataset creation\nAngular velocity data from upper thigh placed devices were isolated from each original cohort\nsubject’s continuous 60-second walk dataset. Subject data were separated into individual steps and\nnormalized using an automated MATLAB script, with all single steps aggregated into the training\ndataset (n = 2057 steps). The individual step snippets were manually reviewed to remove any\nerroneously tagged peaks or step boundary detection errors. The dataset was split 70-30 for training\nand validation. Labels were generated per-subject according to their FFP status determined from\nin vivo studies.\nPage 28 Line 557\nThe trained model was quantized using the open-source artificial neural network library Keras and\nconverted into ONNX (Open Neural Network Exchange) format, floating-point layers\ncompressed/optimized using STMicroelectronics’ X-Cube-AI toolset, and deployed onto a\nmicrocontroller.\nPage 32 Line 638\nThe wearable device was not recharged during the weekend (days 3 and 4) or when the team\nmember was out of office (day 6). No step predictions are logged on day 3, as the wearer performed\nvigorous non-running sport during most of the day and led a more sedentary lifestyle in the\nevening.\nComment 12\nWhat is the functionality of the mesh fabrication, not clear what that is for and who it fits into the\nsensor design, manufacture and operation.\nOur Response\nWe thank the reviewer for their comment and suggestion to provide more detail on the wearable\nbiosymbiotic mesh. The 3D-printed, flexible thermopolymer mesh is designed using 3D scans,\nphotogrammetry, direct measurements taken from the patient, or standard sizes in small, medium,\nand large. Each mesh can be 3D printed in under an hour and can be tailored to the individual,\ncreating a perfect fit which is very lightweight, comfortable, and does not slip off or constrict the\nwearer’s movements.\nWe have detailed biosymbiotic mesh fabrication in prior work we have now included this in our\nmanuscript\nModifications to the Manuscript\nPage 9 Line 158\nCurrent generation wearable devices face issues with subject interaction through charging\nrequirements, technology management of smartphones or specialized devices, and secure\nplacement and skin attachment, with patient discomfort and irritation often arising from rigid\ndevice bodies, abrasive textile or non-breathable plastic attachment straps, and adhesives which\nneed to be replaced often.23,29 To avoid these common issues, BEADs employ biosymbiotic device\narchitecture, which ensures lightweight comfort and the ability to operate continuously for weeks\nwithout input from the wearer.15,20\nPage 9 Line 165\nBiosymbiotic devices embed soft electronics in lightweight, breathable, water- and sweatproof,\npatient-tailored (using 3D scans, photogrammetry, or direct measurements) polymer meshes that\nare rapidly 3D-printed using FDA-approved skin-safe thermoplastic polyurethane (TPU)\nfilament.15 The thin, low mass, form factor of the device (6mm height max, weight 15g with 30\nmAh LiPo battery) and breathable mesh, 3D-printed using FDA-approved skin-safe thermoplastic\npolyurethane (TPU) filament, allow for nearly imperceptible wear without slippage, adhesives, or\nirritation.15,19\nComment 13\nNo clear description of electronics circuit and sensor design, i.e. controller specification/model,\npower storage, sensor architecture etc\nOur Response\nWe thank the reviewer for their comment and suggestion to improve clarity around electrical\ndesign. We have amended the supplementary information to include a new figure, fig. S3, with a\nfull electrical schematic. We want to note that Supplementary Figure S2 contains a labeled\ncomponent diagram that indicates the controller specifications / model and sensing architecture.\nModifications to the Manuscript\nPage 10 Line 171\nA functional block diagram, and a detailedlabeled component diagram, and an electricalsystem\nschematic are included in Fig. 3B, fig. S2, and fig. S23, respectively\nModifications to the Supplementary Information (Figures)\nSupplemental Figure 3\nSupplemental Figure 3. BEAD electrical schematic. Complete electrical schematic identifying\nsubsystems with component values. A. System control with dual-core BLE microcontroller B. 6-\naxis IMU. C. Voltage regulation circuitry. D. Battery & battery monitoring circuitry. E. Far-field\npower harvesting / rectification with dipole antenna. F. BLE Antenna with 2.4 GHz chip antenna.\nComment 14\nNo clear description of algorithms for step count, walking speed and sit to stand detection. I\nfound some description but it’s in the results section\nOur Response\nWe thank the reviewer for their comment and suggestions to improve clarity around step counting,\nwalking speed, and sit-to-stand detection algorithms.\nThese metrics were collected during in vivo gold standard frailty assessment experiments. To\ntrain the on-device diagnostic prediction model, class labels, i.e., frailty states (healthy/pre-\nfrail/frail), were required for subject data, which were generated using the Fried Frailty Phenotype\nand direct monitoring of in vivo subjects by members of the study team.\nWe have amended the text to explicitly detail the step counting and walking speed algorithms.\nSit-to-stand detection was observed and timed directly by members of the study team.\nModifications to the Manuscript\nPage 6 Line 110\nGait parameters from continuous 60-second walk test during in vivo trials (N = 20 subjects, 16\n1\nhealthy, 4 pre-frail) are shown in Fig. 2B. Step/stride time and step/stride variability are gait\nparameters typically assessed when performing frailty assessment in clinic, and can reliably\ndistinguish frailty severity.3,5\nPage 24 Line 470\nStep count was extracted from the angular velocity data of each thigh-located device (one gold-\nstandard LEGSys device and one biosymbiotic device per thigh, four devices total). Step count\nextraction was automated using a MATLAB script and validated by a member of the study team.\nThe MATLAB script isolated all peaks in the angular velocity data with magnitude at least 50\ndegrees per second and per-device inter-peak spacing at least 1 second apart.\nPage 25 Line 477\nWalking speed was determined from this test by manual analysis of the angular velocity data of\neach thigh-located device (one gold-standard LEGSys device and one biosymbiotic device per\nthigh, four devices total): the distance covered during the walk (3m from chair to cone + 3m from\ncone to chair) was divided by the time from toe-off of the first step after standing up to heel-strike\nof the last step before turning around to sit down. These times were extracted by manual inspection\nof the angular velocity waveforms.\nPage 25 Line 482\nSit-to-Stand: Subjects began seated and were asked to stand up and sit back down without using\ntheir hands. This was repeated five times with their total time recorded by a member of the study\nteam directly observing the subjects.\nComment 15\nSeems like the sensor parameters (BME rate) were adjusted from the gold standard testing to\n10-day tests, this doesn’t make the gold standard representative of at home use.\nOur Response\nWe thank the reviewer for their comment. We have detailed the purposes of the multiple\nexperiments performed in this work in response to Comment 10 above. While the BLE packet\nrate was indeed changed between the gold standard comparison experiments and the ten-day\ncontinuous wear tests, this change has no relation to the system’s sampling or inference rate,\nwhich occurs entirely on-device and does not rely on BLE, nor sensing fidelity. The 16-bit digital\nangular velocity data used during inference is collected entirely on the IMU at a fixed 200 Hz. This\ndata rate is configured once during system boot and does not change at any point.\nUnlike other existing wearables for gait-based frailty analysis that transmit raw data off-device\nover BLE for external or cloud processing, the BEAD performs gait data preprocessing and ML\ninference entirely on-device. During typical use the device only utilizes BLE to transmit small\ntimestamped per-step inference results off-device, not large amounts of raw data. Modifying the\nBLE packet rate for this typical use case allows us to reduce energy consumption without affecting\nperformance. We have amended the manuscript to clearly state this.\nIn the case that one needs to offload large amounts of raw data over BLE, such as during the\ngold standard comparison tests where in addition to the inference we also collect the raw IMU\ndata, the BLE packet rate can be increased to improve data offload rates and ensure responsive\nBLE behavior.\nTo improve clarity regarding data flow and BEAD system behavior, we have added an additional\nfigure to the supplementary information, fig. S7 Data and system control flow, that details how\nangular velocity data is collected and processed to become a clinically relevant diagnostic\nindicator. We have modified the manuscript accordingly.\nModifications to the Manuscript\nPage 11 Line 213\nAnalyzed data, i.e., timestamped inferences, can be offloaded via an internet connected device\nsuch as a smartphone, aggregated into total steps classified as frail versus healthy, and deposited\ndirectly into the electronic healthcare record (EHR), shown in fig. S7A.\nUnlike with traditional brick-and-strap wearable devices, a patient is not required to continuously\ninteract with the BEAD to perform data collection, see fig. S7B for the control flow that enables\nautonomous data collection. They also do not need to remove the device for charging or when\nbathing. Only a brief initial setup of the power caster is required for indefinite collection of high-\nfidelity biosignals, enhancing accessibility, user experience, and improving overall patient\ncompliance.10,15\nPage 19 Line 372\nDuring the experiment, a BEAD worn by a healthy subject monitors their gait continuously,\nwaking when step activity is detected, collecting angular velocity data and performing inference\non identified steps, and storing only timestamped step inference resultsclassifications in\nnonvolatile memory on-device until data can be transferred off. In this typical use case, the BLE\npacket rate can be decreased to conserve power without affecting sensing fidelity, since all\nprocessing and inference occurs on-device without BLE. System performance is likewise\nunaffected by decreasing the packet rate, since only small packets of timestamped inference data\nare offloaded only when a logging device connects over BLE.\nModifications to the Supplementary Information (Figures)\nSupplemental Figure 7\nSupplemental Figure 7. Data and system control flow. A. Data flow showing how angular\nvelocity data from the wearable device become clinically relevant diagnostic indicators. B. High-\nlevel control flow diagram detailing system behavior. Colors indicate the subsystem responsible\nfor the control step and correspond to the identically colored subsystem from panel A. Hexagonal\nblocks indicate control decision points. White text indicates a specific activity occurs on the IMU.\nResponse to comments of Reviewer 2:\nGeneral Comments:\n1. The manuscript highlights its main contribution as enabling a transition from reactive, post-\nhospitalization frailty diagnosis to preventive, home-based diagnostics. The topic should be of\ninterest to the readers of NC, and the paper is generally well written.\nSome comments are provided below for consideration.\nOur Response\nWe thank the reviewer for their comments and recognition of the manuscript quality. We have\nprovided detailed responses to the comments below.\nComment 1\nHowever, from Figure 1A it appears that patients are still prescribed the device only after a fall or\nhospitalization, following a clinical diagnosis. This raises a quick question: if such reactive clinical\nevents still trigger device usage, how can the approach be truly considered preventive rather than\nreactive? The authors should consider clarifying under what circumstances the device would be\ndeployed in practice, and whether there is a clear pathway for preventive adoption in at-risk but\nnot yet hospitalized individuals.\nOur Response\nWe thank the reviewer for their comment and suggestions to improve manuscript clarity and\nhighlight novelty. The intent of Fig. 1A was to illustrate the current state of the art, however it was\nclearly confusing as depicted. To improve clarity, we have amended Fig. 1A and 1B, adding titles\nand text edits. We have also modified the text in Fig. 5B and the figure caption to indicate use of\nthe BEAD for preventive care in addition to home-based monitoring.\nWe mention in our response to Reviewer 1 Comment 9 that the point of our device is for use\nbefore adverse events because it simplifies frailty diagnostics and makes attaining a diagnosis\nmore accessible. This is achieved by deploying the device in an autonomous, continuously\noperating biosymbiotic form factor and limiting all user interaction to one-time simple placement\nof a power caster.\nWe have amended the manuscript to identify use cases where this system approach provides\nsignificant advantages over existing practices.\nModifications to the Manuscript\nPage 23 Line 435\nBy reducing diagnostic barriers, these devices present compelling use cases in preventive\nmedicine. For instance, continuous ECG monitors identify over 40% of arrhythmias only after day\n3 of continuous recording,49 allowing clinicians to intervene before sudden cardiac events50 in\ncases that would otherwise be missed by short clinical assessments. This same principle could\nextend to ML-integrated biosymbiotic devices for frailty assessment in at-risk individuals,\npotentially reducing fall-related hospitalizations through early detection and intervention. BEADs\noffer a significant advantage over current approaches, which cannot provide scalable frailty\ndiagnostics, let alone continuous monitoring.\nModifications to the Manuscript (Figures)\nPage 5 Line 85\nFigure 1\nFigure 1. Continuously operating wearable AI for on-device frailty assessment. A. Illustration\nshowing the reactive current clinical standard for diagnosing frailty. B. Implementation of\nwearable performing on-device inference from IMU recorded gait, wireless far-field charging of\nthe wearable device, and Bluetooth Low Energy data output from wearable.\nPage 21 Line 402\nFigure 5\nFigure 5. Continuous gait monitoring for frailty detection. A. Photograph demonstrating the\nlow profile of BEADs that allows them to be worn discretely under clothing. B. New approach for\nhome-based frailty diagnostics and preventive care. C. Plots showing results of step inference\nduring 60s continuous walk from a healthy (left) subject and a pre-frail (right) subject, with insets\nhighlighting an individual step from each subject. D. Battery voltage and inference results from\nten-day continuous wear on healthy subject.\nComment 2\nIn the model training process, the dataset contains a strong imbalance between healthy and pre-\nfrail subjects (16 vs. 4). Such imbalance may risk biasing the classifier towards the majority class.\nIt is recommended that the authors explore strategies to mitigate this issue, such as stratified\nsampling, data resampling, or synthetic minority oversampling methods (e.g., SMOTE), to avoid\nskewing results and to better validate the robustness of the model.\nOur Response\nWe thank the reviewer for their comment and suggestions to validate model robustness. While\nexploring various machine learning approaches, randomly shuffled, stratified sampling was\nutilized to help mitigate the impact of the class imbalance. Additionally, the final classification\nalgorithm was implemented using a random forest classifier which took balanced class weights\ninto account during training, imposing a higher penalty on misclassification of the minority class.\nWe have amended the manuscript to indicate this.\nSubject scarcity is a limitation of this study, see our response to Reviewer 1 Comment 2. We want\nto highlight that the primary novelty of this work lies with the fully integrated BEAD platform itself,\nrather than the strength of the frailty assessment model, and that implementation of any edge AI\nwith clinically proven diagnostic accuracy would require generating a substantially larger training\nset and therefore substantial resources to facilitate a multi-site and metropolitan area trial to\ncollect relevant datasets.\nWe have amended the article to acknowledge subject scarcity and clarify our efforts in mitigating\nimpact of class imbalance, including stratified sampling and random forest classification with\nbalanced class weights.\nModifications to the Manuscript\nPage 16 Line 310\nWhile minor improvements may be gained through hyperparameter tuning or with a more\nadvanced classification algorithm, clinical deployment will require substantially larger, more\ndiverse training cohorts. Given local population dynamics (Tucson, AZ – 92,990 adults aged\n65+)39, the general incidence of frailty in adults age 65 and older (~10-15%)40,41, and the ability of\nthe local university-healthcare network to reach, recruit, and retain longitudinal study subjects,\nthere become fewer than 200 potentially recruitable subjects in a large city. Considering\nrecruitment rates of non-oncological clinical trials average as low as 0.85 subjects recruited per\nsite per month,42,43 statistically significant subject recruitment would require considerable\nresources, necessitating years-long, multi-institution, multi-site collaboration.\nModifications to the Supplementary Information (Methods)\nPage 28 Line 557\nThe trained model was quantized using the open-source artificial neural network library Keras and\nconverted into ONNX (Open Neural Network Exchange) format, floating-point layers\ncompressed/optimized using STMicroelectronics’ X-Cube-AI toolset, and deployed onto a\nmicrocontroller. To mitigate the effect of class imbalance on the model, balanced class weights\nand randomly shuffled, stratified sampling of the training dataset were used during model training.\nClass weights were set to be inversely proportional to frequency of the class in the training set.\nThis imposed a greater penalty on misclassification of the minority class (pre-frail) and encouraged\nbetter recall and f1-score.\nComment 3\nThe work relies on a single 70/30 train-test split for model evaluation. While this provides an initial\nperformance estimate, it does not fully rule out overfitting or ensure generalizability, especially\ngiven the small sample size. Cross-validation (e.g., leave-one-subject-out) or external validation\nwould strengthen the claims regarding model robustness and clinical applicability.\nOur Response\nWe thank the reviewer for their comment and suggestions to avoid overfitting and improve model\ngeneralizability. During training, stratified 5-fold cross-validation schemes were employed and\nmodel parameters were adjusted to mitigate overfitting to the training set. Adjustment of the\nMINIROCKET transform also took generalizability into consideration, with 420 convolutional\nkernels (decreased from 10,000 kernels) providing consistent performance on the test set without\ndegrading accuracy. We have amended the manuscript to include these methods.\nRegarding leave-one-subject-out cross-validation, please see our response to comment 5 below\nfor more in-depth discussion.\nModifications to the Manuscript\nPage 28 Line 553\nA time series RF classifier was trained off-device on a desktop workstation using the transformed\ntraining dataset, and validated using the transformed test set,. During training, 5-fold stratified\ncross-validation was employed to mitigate model overfitting on training data and improve\ngeneralizability.\nPage 29 Line 568\nFinal parameters (420 kernels, 16 max dilations per kernel) were chosen to maintain average\naccuracy above 90% across shuffled test sets., ensuring high generalizability while mitigating\noverfitting to the training set.\nComment 4\nBeyond cross-validation, the inclusion of learning curves would provide additional evidence\nregarding the model’s bias–variance behavior and its capacity to generalize with more data.\nOur Response\nWe thank the reviewer for their comment and suggestion to provide additional evidence toward\nmodel bias-variance behavior. We have amended the supplementary information to include a new\nfigure containing the model’s learning curves generated using two different cross-validation\nschemes, fig. S10. The results highlight the model’s ability to learn without overfitting as the\ntraining set increases in size, however they also reveal bias in classification and the model fitting\nto subject-specific characteristics. See our response to Comment 5 below.\nWe have amended the manuscript to discuss these results.\nModifications to the Manuscript\nPage 15 Line 300\nThe f1-scores of the classification pipeline are therefore assessed and shown in Supplementary\nTable S2, with scores of 0.92 and 0.90 when predicting healthy and pre-frail, respectively,\nindicating excellent precision, recall, and ability to distinguish between classes. Figure S10\ncontains learning curves generated using two different cross-validation schemes: stratified, 5-fold\n(fig. S10A) and leave-one-group-out (fig. S10B), showing the model’s ability to learn and\ngeneralize as the training set increases in size, although it is limited by the number of pre-frail\nsubjects in the training set.\nModification to the Supplementary Information (Figures)\nSupplemental Figure 10\nSupplemental Figure 10. Model learning curves. A. Plot of learning curve of the frailty\nclassification model using stratified 5-fold cross validation (CV), shaded region indicates standard\ndeviation. Test accuracy increases with the number of training samples, showing model learning\nand improved generalizability, while train accuracy decreases, showing that the model is not\noverfitting. B. Learning curve of the model using Leave One Group Out (LOGO) cross-validation,\nshaded region indicating standard deviation. The results indicate that the model is learning subject-\nspecific characteristics, since test accuracy decreases as the number of training samples increases.\nComment 5\nAlthough the dataset includes a relatively large number of gait segments, these segments are\nnot truly independent samples, as many originate from the same individuals. This could raise\nthe risk that the model may be overfit to subject-specific characteristics, thereby limiting its\ngeneralizability to new individuals. Please discuss.\nOur Response\nWe thank the reviewer for their comment and concerns about overfitting to subject-specific\ncharacteristics. Model training was performed using stratified cross-validation which did not take\ngrouping (data points originating from the same group, or test subject) into account.\nWe have generated the learning curve shown in fig. S10B (see our response to Comment 4\nabove) using leave-one-group-out (also known as leave-one-subject-out) cross validation. The\nmodel is fitting to subject-specific characteristics, since test accuracy is significantly worse and\ndiverges from training accuracy (rather than converges) as training sample size increases. We\nmention in response to Comment 2 above that subject scarcity is a significant limitation of this\nstudy and have modified the manuscript to discuss these limitations. To develop a truly\ngeneralizable model likely 500 to 1000 independent subjectsR6 are required, which would require\nat least 5+ sites of recruitment with excellent enrollment and adherence due to population\nstatistics (see our response to Reviewer 1 Comment 2). This would require a large-scale effort\nbeyond the scope of this proof of principle paper.\nR6. Amjad, A., Qaiser, S., Błaszczyszyn, M. & Szczęsna, A. The evolution of frailty assessment\nusing inertial measurement sensor‐based gait parameter measurements: A detailed analysis.\nWIREs Data Min &amp; Knowl 14, e1557 (2024).\nModifications to the Manuscript\nPage 16 Line 309\nThey also reveal subject-specific overfitting that limits generalizability. While minor\nimprovements may be gained through hyperparameter tuning or with a more advanced\nclassification algorithm, clinical deployment will require substantially larger, more diverse\ntraining cohorts. Given local population dynamics (Tucson, AZ – 92,990 adults aged 65+)39, the\ngeneral incidence of frailty in adults age 65 and older (~10-15%)40,41, and the ability of the local\nuniversity-healthcare network to reach, recruit, and retain longitudinal study subjects, there\nbecome fewer than 200 potentially recruitable subjects in a large city. Considering\nrecruitment rates of non-oncological clinical trials average as low as 0.85 subjects recruited per\nsite per month,42,43 statistically significant subject recruitment would require considerable\nresources, necessitating years-long, multi-institution, multi-site collaboration.\nComment 6\nWhile the manuscript provides some waveform-level interpretations, the discussion remains\nlargely algorithm-centric. A clearer articulation of how per-step inferences can be translated into\nclinically meaningful diagnostic indicators or longitudinal trend reports would strengthen the\nclinical relevance of the work. Please think about and discuss this.\nOur Response\nWe thank the reviewer for their comment and suggestions to enhance clinical relevance, which\nwe believe improve the impact of the article. We have amended the manuscript to emphasize the\nmerit of per-step inference across longitudinal datasets and its utility as a clinically relevant\ndiagnostic indicator.\nModifications to the Manuscript\nPage 3 Line 58\nSince conventional statistical analysis approaches for gait-based frailty assessment, such as\nexamining stance time, step length, or frequency component analysis,11 are difficult to automate\nand tailor to individuals,12,13 Tthese large datasets provide a strong use case for machine learning\n(ML)-based data analysis, which can rapidly and accurately identify features that may be difficult\nfor humans to recognize in a timely manner,14 especially repeated over longer time scales. ML\napproaches also capture complex, non-linear interactions which can be missed with conventional\napproaches, scale well and offer flexibility with the potential to learn from data.13 Performing oOn-\ndevice ML-based data analysis significantly condenses these large datasets, transforming weeks\nof continuous analysis into easily digested trends that can be transmitted with minimal bandwidth\nand data management requirements, shifting point-of-care from hospitals and clinics to homes\nthrough telehealth, reducing financial burdens on patients and decreasing strain on healthcare\nsystems.\nPage 20 Line 386\nCombined, these results demonstrate that the capacity of edge-AI on a continuously operating\ndevice to transform frailty diagnostics using gait-based assessment at home.power of per-step\ninference is realized through temporal ML analysis, i.e., statistical analysis of per-step inferences\nrecorded from routine life over weeks to months. Cross-sectional analysis, i.e., statistical analysis\nof per-step inference results from short 60-second walks in labs or clinics once a month, is less\nrepresentative and less accurate than temporal analysis.46 Extracted through temporal ML analysis,\nthe diagnostic indicator is the percentage of steps classified as frail (or pre-frail) (PSCF) relative\nto steps classified as healthy over the study duration. Ratios can be easily integrated into EHRs\nand read by a clinician at a glance, providing insight on a patient’s status.\nIn cases where a patient’s PSCF is not decisive, deeper analysis can generate longitudinal trend\nreports to enhance understanding. Since each inference result is timestamped, insightful metrics\nsuch as the number of frail steps per day providing a complete profile of when diagnosis occurs.\nCombining clinical history, patient lifestyle, and ML-based symptom analysis drives precision\nmedicine and personalized care, likely improving patient outcomes and reducing healthcare cost.47\nComment 7\nThe evaluation of the reported ~21% energy savings for on-device inference is confined to\nshort-term, laboratory-based measurements. In real-world conditions across varying walking\nspeeds, activity types, and usage durations, the device’s overall power consumption may differ\nsubstantially. A more comprehensive energy analysis under daily living conditions is needed.\nOur Response\nWe thank the reviewer for their comment and suggestion to provide more real-world results. We\nhave amended the supplementary information to include a new figure, fig. S6, showing energy\nconsumption using off/on-device inference across several different walking speeds. These results\nshow energy savings have a direct correlation with walking speed, i.e., as the number of steps\nperformed over a period increases, so does energy consumption, since the IMU activates the\ngyroscope more often. At or above 3.86 kph (2.4 mph), the gyroscope is practically enabled\ncontinuously and current consumption plateaus. Activity-based gyroscope activation can be\nmanually controlled to limit the number of inferences per day and further save power.\nRegarding activity types, the on-device IMU only enables the gyroscope when it recognizes\nwalking or running activity through its proprietary embedded decision tree, thus any other activities\nare ignored automatically without additional power waste. When the gyroscope is active for\nangular velocity data collection, its baseline current draw (600+ µW) dominates over the\nmicrocontroller, which idles under 250 µW. We have amended the manuscript to discuss these\nchanges.\nModifications to the Manuscript\nPage 10 Line 189\nFigure 3C illustrates current consumption for a BEAD performing on-device inference versus\ntransmitting raw step data for off-device analysis. Integrating over the entire 14 s duration reveals\na 21% decrease in energy consumption (19.68 milliJoule versus 24.97 milliJoule, respectively) for\nsingle-step inference. Shown fig. S6, system energy consumption depends on walking speed, with\nthe IMU’s motion detection interrupt triggering more reliably at higher step rates. In response to\nthe interrupt, the microcontroller activates the IMU’s gyroscope, which has high power demands\nrelative to the low-power idle state of the BEAD. Walking speeds below 2.89 kph (1.8 mph) did\nnot reliably trigger the IMU’s activity detection, thus no changes in system current consumption\nare noted for 0.97 and 1.93 kph (0.6 mph and 1.2 mph, respectively). At or above 3.86 kph (2.4\nmph), the gyroscope is practically enabled continuously and current consumption plateaus. By\nemploying more conservative inference approaches, e.g., limiting the number of steps classified\nfor a given duration, one can maintain low energy consumption. Since the BEAD is worn for\nextended durations andThis enhances the utility of far-field power harvesting (extended analysis\ntime for the same far-field charging duration) and reduces reliance on communication\ninfrastructure as the resource-constrained wearable can store inference results (<108 bytes/step)\nover extended durations when external logging or processing devices are not available, rate\nlimiting inference is not as prohibitive as during data collection from short, dedicated, in-clinic\nsessions. Additionally, this reduces the number of BLE connection intervals per step inference by\nat least two events. BLE can practically transmit up to 247 bytes/packet and directly outputting the\ninference result requires <10 bytes with timestamp and classification confidence, while raw\nangular velocity data for a single step approaches 500 bytes at 16-bit resolution and 218\nsamples/step, saving 0.079 mJ / inference generated. Practically this means thatResults from nearly\n27,000 events can be stored on device, enabling 7-14 days (depending on daily step count) without\nthe need to activate BLE, providing exceptional flexibility in firmware design and increased\nreliability without the need for large onboard memory.\nModification to the Supplementary Information (Figures)\nSupplemental Figure 6\nSupplemental Figure 6. Walking speed versus current consumption. A. Continuous BEAD\ncurrent consumption performing inference on-device (blue) and off-device (orange) at 0.97, 1.93,\n2.89, 3.86, and 4.83 kph (0.6, 1.2, 1.8, 2.4, and 3.0 mph, respectively). Off-device inference\nassumes consistent BLE connection. All plot X-axes are from 0 to 60s and Y-Axes from 0 to 20\nmA. B. Mean current draw for each of the walking speeds tested, showing current consumption\nincreases as walking speed increases. Walking speeds below 2.89 kph did not reliably trigger the\nonboard IMU’s activity detection. Walking at 3.86 kph or faster led to continuous activation of\nIMU activity detection, with higher speeds not increasing current draw.\nModification to the Supplementary Information (Methods)\nPage 30 Line 592\nWalking speed versus current consumption\nCurrent consumption by a BEAD was monitored using a source measurement unit (SMU)\n(STMicroelectronics, STLINK-V3PWR). The output voltage of the SMU was adjusted to 1.9V,\nthe 1.8V operating voltage of the BEAD with overhead for LDO dropout voltage. The SMU\nrecorded sourced current with 50 kHz sampling rate. A member of the study team wore a BEAD\nabove the knee and stood adjacent to a treadmill (ASUNA, 8730). The treadmill speed was set to\n0.6 mph and the study team member stepped on to the treadmill and walked at speed for 60 s. The\nteam member then stepped off the treadmill for ten seconds while the speed was adjusted to 1.2\nmph. The team member then stepped on to the treadmill and walked at speed for 60 s. This\nprocedure was repeated for 1.8, 2.4, and 3.0 mph.\nOn-device inference was triggered automatically by the BEAD IMU’s activity detection, with\nresults logged to on-device non-volatile memory. For off-device inference, the same setup was\nused with a BLE connection established before the initial 0.6 mph test. During off-device inference\nmeasurements, the BEAD transmitted continuous raw angular velocity data off-device to a laptop."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for the significant updates based on my comments.\nWhile accepting that further work sits with commercialization efforts, I still believe that the study design and results do not strongly support the claimed novelty.", "reviewer_2": "The authors have made satisfactory revisions."}, "author_response": ""}]

Decision letter