Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensory-Motor Neurostimulation to Enhance Exosuit Performance

Exosuits typically provide limited mechanical support and rely on a user's residual functional ability. However, people with neurological impairments often suffer from both motor and sensory deficits that limit the assistance an exosuit can provide. To overcome these limitations, we developed the REINFORCE system, that complements the mechanical assistance provided by an exosuit, the Myosuit, with (1) functional electrical stimulation to enhance the activities of leg muscles, and (2) transcutaneous electrical nerve stimulation to restore somatosensory information. It consists of a fully portable and highly modular system that can be easily adapted to the level of impairment and specific need of each participant. Technical verification with three healthy participants showed reliable synchronization between all modules of the systems in all phases of walking. Additionally, we tested the system's effectiveness in one participant with multiple sclerosis who walked overground with and without functional electrical stimulation. Results showed a slight increase in self-selected walking speed (approx. 18%) and in the peak hip flexion at late swing (approx. 12%) as well as reduced step-to-step variability of step length and step time when electrical stimulation was provided. Our findings push towards a clinical trial involving more patients to validate the effectiveness of the REINFORCE system on participants' mobility.
An Open-Source Micro-Watt 130-dB Delta-Sigma Modulator with 600mVpp Input Range for DC-Coupled Biosignal Acquisition

This paper presents the system-level design of a low-power continuous-time delta-sigma modulator (CT-DSM), demonstrates the circuit design of the input transconductor utilizing the SkyWater 130nm open-source PDK, and addresses circuit-level topologies for the remaining analog blocks. The CT-DSM is part of a dc-coupled data acquisition system for biosignals. This system aims to replace the conventional accoupled implementation comprised of a cascade of a pre-amplifier, antialiasing filter (AAF), and ADC. A CT loop filter was chosen over a discrete-time implementation due to the tight power budget (<15μW) and to avoid needing an AAF in front of the delta-sigma modulator. A Gm-C-based integrator is employed at the first stage due to the required input impedance (>100MΩ). High-level design exploration was performed by a custom framework, utilizing the python-deltasigma toolbox. The chosen sampling clock of 65536Hz limits the OSR to 256, which requires a fourth-order loop filter to reach the target SNR of 130dB. A system-level analysis highlights the feedforward topology as superior to its feedback counterpart regarding chip area and power consumption. Finally, the open-source circuit design is compared to state-of-art implementations.
Gamification of Physical Therapy Exercises Using Commercial Entertainment Content: A Safety and Feasibility Study

Independent physiotherapy at home is a crucial element of rehabilitative care for a wide range of conditions as it constitutes a large portion of the overall therapy dose. However, up to 80% of individuals who are prescribed at-home physiotherapy do not consistently adhere to their treatment schedule, resulting in poor treatment outcomes. This is likely due to a lack of motivation and progress tracking in the current standard of care. We have developed a novel software prototype that allows users to control commercial entertainment content, such as video games or interactive music videos, with their movements during physiotherapy. By connecting therapy to proven entertainment content, we aim to improve on the current motivational deficits. This study investigated the safety and feasibility of this concept in a controlled environment over four physical therapy sessions with seven patients suffering from musculoskeletal and neurological conditions. As a secondary outcome, patients were asked about their enjoyment, perceived competence and effort using the Intrinsic Motivation Inventory (IMI) questionnaire. All participants were able to interact with the presented entertainment content and completed the study with no adverse events. Despite the diversity in pathology, age and training scenarios, the entertainment content maintained the patients' enjoyment with a high average rate of 6/7 on the IMI scale. Interacting with commercial entertainment content by doing physical therapy exercises was feasible, safe, and well-received over the six-week study period.
Heat fluxes over the eastern tropical Pacific and aspects of the 1972 El Niño

Recently evaluated flux formulas were used with an edited ship-of-opportunity data set for the 1970's decade to derive estimates of the individual surface heat fluxes and the total or net surface energy exchange for the eastern tropical Pacific. Absorbed solar radiation is the dominant heat flux in the region, and latent heat exchange is the second most important component; total cloud cover and wind speed are generally the most significant atmospheric variables. Over much of the area, the annual excess of net surface flux appears to be balanced by heat advection in the South Equatorial Current. Although the seasonal cycles of sea surface temperature seem to be produced by variations in net surface flux over parts of the area, they do not appear to balance off Peru and near the equator. Instead, water temperature seems to be noticeably affected by the seasonal cycle of upwelling and advection along the Peru coast. This data set was also used to derive sea surface temperature and net surface heat flux departures from the mean for individual months during the 1972 El Nino. The magnitude and speed of the initial warming along the eastern ocean margin indicate changes in heat content at least an order of magnitude greater than any departures of net surface flux; this fact and the subsequent poleward and westward spreading of warm water support an advective origin, especially Wyrtki's hypothesis of an eastward propagating Kelvin wave. Subsequent changes during an El Nino episode also do not seem to be influenced appreciably by variations in surface heat exchange.
Use of Metamaterials to Reduce Acoustic Noise Emissions from Lower Limb Prostheses: An Experimental Validation

Recent human-centred design studies suggest that acoustic noise could affect the physical use and psychological acceptance of a biomedical device. These aspects are especially relevant in the prosthetic field, in which device loudness is often related to rejection. The aim of the study is to inquire on the possibility to reduce the acoustic noise emitted by a robotic leg prosthesis by improving its casing. First, acoustic noise emissions are characterized experimentally using an anechoic chamber, both for the whole prosthesis, and for its actuator (i.e., noise source) in isolation. The characterizations show that the whole prosthesis including its casing amplify the actuator noise, and that noise emissions are concentrated within a certain frequency range. Based on these findings, the prosthesis casing has been redesigned to include a panel of Helmholtz resonator-based acoustic metamaterials as proof of concept, which attenuate respective noise emissions. Experimental validations show that the use of such metamaterials in the prosthesis casing can significantly reduce noise emissions without compromising on prosthesis size and weight.
Influence of Robotic Therapy on Severe Stroke Patients

Robotic rehabilitation has emerged as a promising approach to enhance motor recovery after stroke, but there is limited knowledge about its efficacy in individuals who have experienced severe stroke. The study presented in this paper aims to analyze the effect of robotic therapy on the recovery of patients with severe stroke when combined with conventional rehabilitation therapies, and we want to observe whether there is a relationship between the clinical assessment provided by the therapist and the data recorded by the robotic device. Participants were divided into an experimental group and a control group, both receiving 15 sessions of conventional therapy in three consecutive weeks, but the experimental group underwent three out of five sessions per week with a robotic device. Both groups were evaluated using clinical scales, and in addition the experimental group was evaluated using an assessment game incorporated in the robotic device that provides session data such as the level of assistance needed by each user to complete the activity, or the score obtained in the game. These preliminary results showed that patients who received robot-assisted therapy had better motor function recovery compared to those who only received conventional therapy. In addition, it is also observed that the robot assistance needed by patients in the experimental group decreased as the sessions progressed, suggesting that robot-assisted therapy could be an effective tool for severe stroke patients.
Implementation of Shear Wave and Strain Elastography with Micro-Ultrasound

Quantitative tissue stiffness characterization can aid in diagnosing prostate cancer. Shear wave absolute vibro-elastography (S-WAVE) provides quantitative, multi-frequency shear wave imaging with external excitation, while strain elastography (SE) provides a map of relative tissue stiffness. In this work, we present a first-of-its-kind micro-ultrasound (microUS)-based system for prostate elastographic imaging. The system uses an ExactVu™ microUS with minimum hardware addition– making it compatible with the current clinical workflow. Bandpass sampling is utilized to effectively recreate phasors with a frame rate beneath the Nyquist sampling rate for tissue motion tracking in S-WAVE imaging. We characterize the methods (S-WAVE and SE) with a commercial quality assurance elasticity phantom having inclusions with different levels of stiffness. Promising results are achieved with both methods being able to identify all four inclusions with their expected contrast difference.
Customized Series Elastic Actuator for a Safe and Compliant Human-Robot Interaction: Design and Characterization

When it comes to robotic-mediated rehabilitation it is mandatory to design a system that guarantees a safe and compliant human-machine interaction. Dealing with rehabilitative upper limb exoskeletons, Series Elastic Actuators offer a potential solution for this purpose. This work proposes four different solutions for SEAs' spring design. After an analysis on the mechanical requirements, four different solutions are explored and presented. The performances of the proposed highly integrated SEAs are compared. An initial static characterization provided insights on the linearity and repeatability of each spring torque-angle performances. The dynamics of the springs and their frequency responses are then analysed to show how it is possible to exploit our system for human-robot interaction applications.
Skeleton Tracking Solutions for a Low-Cost Stroke Rehabilitation Support System

Computer systems based on motion assessment are promising solutions to support stroke survivors' autonomous rehabilitation exercises. In this regard, researchers keep trying to achieve engaging and low-cost solutions suitable mainly for home use. Aiming to achieve a system with a minimal technical setup, we compare Microsoft Kinect, OpenPose, and MediaPipe skeleton tracking approaches for upper extremity quality of movement assessment after stroke. We determine if classification models assess accurately exercise performance with OpenPose and MediaPipe data against Kinect, using a dataset of 15 stroke survivors. We compute Root Mean Squared Error to determine the alignment of trajectories and kinematic variables. MediaPipe World Landmarks revealed high alignment with Kinect, revealing to be a potential alternative method.
Hybrid of Deep Auto-Encoder and Maximum Mean Discrepancy for Cyber Reconnaissance Detection

Detection of cyber-reconnaissance attacks plays a vital role in planning to prevent further attack stages as well as incident response. Although much effort has been made in the industry and academic community, there are still many limitations over the accuracy that need to be improved. Two fundamental limitations contributing to these concerns are powerful, meaningful, latent representation learning of network data and determination of the borderline between normal and cyber reconnaissance attack regions at learned latent space. Recent studies have introduced a number of solutions to detect network reconnaissance attacks such as using machine learning (ML) classifiers and combining Principal Component Analysis (PCA) or Deep Auto-Encoder (DAE) with ML. However, such solutions are still affected by the aforementioned limitations. In addition, these methods inefficiently handled high dimensional network traffic data. In this work, we proposed a novel hybrid of DAE and Maximum Mean Discrepancy (MMD) to overcome such limitations and strongly support the attack detection efficiency of ML classifiers. The proposed model called MMDAE exploits the advantage of the latent representation capability of DAE combined with the efficiency to determine the distance between probability distributions of MMD. The learned latent representation of MMD strongly increases the classification performance of ML classifiers. In order to evaluate the performance of the proposed model, we use four benchmark network datasets: NSL-KDD, UNSW-NB15, CIDDS-001, and CIDDS-002. The comprehensive experimental outputs obtained clearly demonstrate that this novel approach has better performance in comparison with existing works for cyber reconnaissance detection.
An Alliance Confrontation Model of the UAVs Based on Improved Clustering Algorithm and Artificial Ant Colony Algorithm

In order to solve the problem of decomposing large scale unmanned aerial vehicle(UAV) group into small UAV groups and how to make alliance between small UAV groups is beneficial to the combat, a system countermeasure model of swarm UAV attack and defense is proposed. First, based on the characteristics of the small group of the attack and defense UAV, the objective function of the small group alliance of the attack and defense UAV is constructed, and then the improved clustering algorithm and the artificial ant colony algorithm are established respectively to calculate the objective function. Finally, the system confrontation scene is set, and the simulation verification of the model and algorithm is carried out. Through the analysis of simulation data, the validity of the model and algorithm is verified. The model provides an important reference for the decision-making of attack and defense of clustered UAVs.
Aero-Engines Remaining Useful Life Prognostics Based on Multi-Hierarchical Gated Recurrent Graph Convolutional Network

To address the problem that most deep learning-based aero-engine RUL prediction models only consider temporal feature extraction and ignore spatial feature extraction, GCN is introduced into LSTM, and the network information is layered by designing the main oblivious gate and the main input gate. The spatial features embedded in the sensor data species are processed using GCN, whereas the LSTM deals with features with apparent temporal trends. The hybrid layer simultaneously performs the extraction of spatio-temporal information in multi-sensor data is extracted. Finally, the quantile regression layer is utilized for RUL prediction and uncertainty quantification. The experimental results show that MHGRGCN can efficiently fuse the modeling process of temporal and spatial correlation to extract more accurate state degradation features for RUL prediction.
Reducing the Impact of Fundamental Frequency on the HFCC Parameters of the Speech Signal

The voiced parts of the speech signal are shaped by glottal pulse excitation, the vocal tract, and the speaker's lips. Semantic information contained in speech is shaped mainly by the vocal tract. Unfortunately, the quasiperiodicity of the glottal excitation, in the case of Human Factor Cepstral Coefficients (HFCC) parameterization, is one of the factors affecting the significant scatter of the feature vector values by introducing ripples into the amplitude spectrum. This paper proposes a method to reduce the effect of the quasiperiodicity of the excitation on the feature vector. For this purpose, blind deconvolution was used to determine the vocal tract transfer function estimator and the corrective function of the amplitude spectrum. Then, based on the obtained HFCC parameters, statistical models of individual Polish speech phonemes were developed in the form of mixtures of Gaussian distributions, and the influence of the correction on the quality of classification of speech frames containing Polish vowels was investigated.
A low-noise high-linear wide dynamic-range MTJ-based magnetic field sensor

Magnetic Tunnel Junction (MTJ) based sensors can function over a very wide range of magnetic fields, from pico tesla range to several milli-tesla. They have the potential to replace many currently used types of magnetic sensors in industrial and biomedical applications. This paper presents a readout channel using a double magneto-resistor (MR) bridge and a high-side current reference. The sensor system features offset cancellation and an intrinsic low-pass filter. The proposed magnetic sensor works with a linearity of better than 0.3% THD in a magnetic field range of ±0.5 mT and a bandwidth of 10 kHz. The measurement results show that the sensor input-referred noise power spectral density is 25.4 nVrms/√Hz at 100Hz equals 1.27 nT/√Hz.
Study on the Standard System Construction of Complex System under the Background of Big Data

This paper analyzes the principle of system engineering and the systematic characteristics of the standard system and points out that the standard system is characterized by systematisms and it is suitable to use the system theory to guide the construction of the standard system. The methods of standard system construction are summarized, including the construction method of enterprise standard system determined by GB/T 13016, comprehensive standardization method, model architecture method, etc. Through the research, it is found that these methods can provide theoretical basis for the construction of standard system architecture. Then, based on big data this paper analyzes the development status of China’s national standard system, intelligent manufacturing industry standard system and State Grid enterprise standard system from the three levels of country, industry and key enterprises, summarizes the evolution and structural characteristics of the complex system’s standard system, summed up interaction relationship between complex system and standard system. On this basis, the construction method of complex system standard system using system engineering method is put forward, including the definition of complex system requirements, the analysis of the status and requirements of the standard system, the design of the standard architecture, the determination of the standard list, the operation and maintenance of the standard system. Finally it takes an standard system as an example to evaluate the applicability of this method.
Dual-Angle Photoacoustic Depth Sensing of Congenital Melanocytic Nevi

The removal of giant congenital melanocytic nevi can be important in plastic surgeries. For aesthetic reasons, the precise and noninvasive detection of the depth of epidermal melanin infiltration before treatment is essential. Therefore, we proposed photoacoustic technology to provide both accurate and safe depth detection. It is novel that two ultrasonic transducers were applied to collect dual-angle photoacoustic signals. Also, the new indicator k-value was introduced to demonstrate the relationship between melanin depth and photoacoustic signals. Ex vivo experiments were conducted with human skin samples, and the results revealed that the deeper the melanin is, the larger k-value the sample has. It is safe to conclude that photoacoustic sensing for depth detection can be quite promising in clinical applications.
Convolutional Neural Network for Removal of Environmental Noises from Acoustic Signal

Environmental noises affects the quality and intelligibility of acoustic signals. Speech, audio, sound are common examples of acoustic signals. The noises present in our environment especially babble noise can affect the performances of applications such as speech and speaker recognition, hearing aids, mobile telephone etc. The research works presented earlier for the removal of these noises from the recorded signal were based on the estimation of noisy part from the signal to prepare a mask for the removal of the noises. But the recent developments in neural networks has provided a mapping based method that can directly map the noisy speech to clean form. In this paper, a comparative study of convolutional neural network architectures for the enhancement of acoustic signals such as speech is done. The NOIZEUS datasets used for the experimental work is an open source speech dataset. The evaluation metrics PESQ, improvement in SNR, STOI, Segmental SNR (SSNR), and Signal to Distortion Ratio (SDR) are used to compare the performances of these networks for the enhancement of noisy signals. The performance of Convolutional networks in auto-encoder framework performs better in terms of better PESQ, STOI, Segmental SNR, SNR Improvement, SDR for network trained with babble noise at 0dB SNR.
Electricity Cost Optimization for Large Loads through Energy Storage and Renewable Energy

Demand charge contributes to a significant portion of the electric bill for large electric loads. Shaving peak demand and adopting optimal demand management strategies can reduce this cost significantly. This paper deals with an optimization problem to reduce the electricity cost for a bulk load with the help of energy storage and renewable energy. An optimal demand control strategy is developed based on the formulated problem that aims to minimize the total electricity cost. The optimization problem was solved using the MATLAB based modeling system CVX. The system considered here is a real system that is situated in University of California, Riverside. Available renewable energy (solar) prediction data and lithium iron phosphate battery data has been used for the simulation purpose. Two different strategies, battery only strategy and battery with solar strategy, are tested with the proposed strategy. The results are validated with National Renewable Energy Laboratory (NREL) developed System Advisor Model (SAM). The results show that 14.5-15.4% peak reduction is possible which leads to a monthly savings of $6,410 to $38,820.