Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired Discrimination of Electrocutaneous Stimulation in the Paretic Hand of Stroke Survivors

Artificial sensory feedback via electrocutaneous stimulation can be used to assist or rehabilitate stroke survivors with sensory deficits. Conveying the magnitude of tactile stimuli is an important aspect of artificial sensory feedback. Here, we explore how stroke-related sensory deficits impact the ability of electrocutaneous stimulation to convey the magnitude of tactile stimuli. Using classical psychophysical methods, we quantified the threshold of detection and the just-noticeable difference of electrocutaneous stimulation current in five stroke survivors with unilateral sensory deficits. We show significantly greater (40%) stimulation currents are needed for initial perception on the paretic hand compared to the non-paretic hand. We also show significantly greater percent changes in stimulation current (140%) are needed for reliable incremental perception on the paretic hand compared to the non-paretic hand. Lastly, we show little correlation between electrocutaneous discrimination performance and clinical sensory assessments of light-touch and spatial mechanoperception. These findings can help guide the implementation of artificial sensory feedback as an assistive or rehabilitative intervention for individuals experiencing sensory loss after a stroke.Clinical Relevance— Our results can help guide the implementation of electrical stimulation as an assistive or rehabilitative intervention for individuals with sensory loss after stroke.
Identifying Gender Bias in Generative Models for Mental Health Synthetic Data

Natural language generation (NLG) systems have proven to be effective tools to create domain-specific synthetic data. The mental health research field could benefit from data augmentation techniques, given the challenges associated with obtaining and utilizing protected health information. Yet, NLG systems are often trained using datasets that are biased with respect to key demographic factors such as ethnicity, religion, and gender. This can perpetuate and propagate systematic human biases that exist and ultimately lead to inequitable treatment for marginalized groups. In this research we studied and characterized biases present in the Generative Pre-trained Transformer 3 (GPT-3), which is an autoregressive language model that produces human-like text. The prompts used to generate text via GPT-3 were based on the Brief Cognitive Behavioral Therapy framework, and each prompt also specified to write the answer as a female or male patient. By controlling the sex distributions within our prompts, we observed the impact of each trait in the generated text. The synthetic data was analysed using the Linguistic Inquiry and Word Count software (LIWC-22) and ccLDA for cross-collection topic modeling. LIWC-22 results show that stereotypical competence features such as money, work, and cognition are more present in the male’s synthetic text, whereas warmth features such as home, feeling, and emotion are highly present in female’s generated data. The ccLDA results also associate competence features with males and warmth features with females.
Quantitative Analyses for Early Tempo-spatial Patterning of Differentiated Human Induced Pluripotent Stem Cells on Micropatterns using Time-lapse Bright-field Microscopy Images

Three germ layer formation on micropatterns are extremely useful for quantitative analysis of hiPSC (human induced pluripotent stem cells) pluripotency. Spatial patterns of stem cells differentiated on the micropatterns will be formed from about 24 hours after differentiation induction and usually quantitated near 48 hours. To delineate the germ layer formation process, temporal changes in spatial patterning of germ layers should be analyzed by noninvasive microscopy. This study proposed a series of image processing methods combined with a U-net automatic segmentation to segment differentiated hiPSCs captured by bright-field microscopy. High segmentation accuracy (83.3%) for the test bright-field images compared with their concurrent Hoechst images (85%) was achieved. Tempo-spatial patterning and formation process of germ layers on the micropatterns can be visualized and quantified by segmenting time-lapse bright-field microscopy images using our method.
Learning Contact-Based State Estimation for Assembly Tasks

Robotic object manipulation requires knowledge of the environment's state. In particular, the object poses of fixed elements in the environment relative to the robot and the in-hand poses of grasped objects are of interest. For insertion tasks with tight tolerances, the accuracy of vision systems to estimate the object and in-hand pose is not high enough. This work proposes a state estimation system that delivers precise estimates for both estimation problems. It uses contact detections and the precise forward kinematics that robot arms provide thanks to their high-resolution joint encoders. We propose a reinforcement-learning-based exploration strategy that decides how the robot should engage with the environment to reduce state uncertainty. The system is evaluated in several simulation and hardware experiments. We show that the learned policy can propose meaningful actions for object localization. In hardware experiments with precision-milled objects, sub-millimeter accuracy is achieved for the in-hand pose estimation task. With objects relevant to industrial tasks, i.e., a melting fuse and a fuse box, millimeter-level accuracy can be reached for both in-hand pose estimation and fixed object localization. In an integrated experiment, we show how a robot grasps a fuse, estimates the in-hand pose, and inserts it into a fuse box.
Investigation of the Refractive Index Sensitivity of Hexagonally Arranged Gold Nanoparticles With Finite Element Method (FEM)

In biosensing, label-free detection techniques have the advantage of a shorter analysis time and can be more cost-effective. In this paper a localized surface plasmon resonance (LSPR) sensor based on hexagonally arranged ellipsoidal gold nanoparticles is designed and investigated by finite element method (FEM) via COMSOL Multiphysics. The wave optics module was used to investigate the relationship between the geometrical properties of the nanoparticle arrangements and their refractive index sensitivity. The effect of different linearly and circularly polarized light was further investigated. The purpose of this work is to optimize the arrangement of experimentally realizable ellipsoidal gold nanoparticle arrangements to maximize the performance of the LSPR-based sensor device and to compare the simulation results with experimentally obtained sensitivities, and present detailed geometrical optimizations.
Real-Time Epileptic Seizure Detection Based on Deep Learning*

Epilepsy is one of the most common neurological diseases, and video EEG is the most commonly used examination method for epilepsy diagnosis. However, since the video EEG examination lasts for hours, the escort has a heavy burden, and the large amount of video EEG data needs to be visually checked by the doctor. The real-time detection of epileptic seizures can reduce the stress of the escort and provide a mark for the doctor to check the EEG efficiently. In this paper, we propose a deep neural network with specified signal representation for real-time seizure detection and add a smoothing filter on the model output to enhance performance. First, we compare the performance of real-time epileptic seizure detection model under different signal representations. Then we use the best signal representation for further analysis in real-time scenario. In the experiment, the EEG data of 9 patients in the CHB-MIT public data set was used, and a patient-specific neural network was trained for each individual. The recall was 97%, the false alarm was 0.219 times per hour, and the latency time was 3.4s for real-time seizure event detection. The results show that this method can realize the real-time detection of epileptic seizures, which is of great significance to the subsequent system design combined with actual scenes.
Intermediate-term earthquake prediction using the modified time-to-failure method in southern California

Based on retrospective modeling of earthquakes from the southern California earthquake catalog, along with previously published evaluations from the New Madrid Seismic Zone, the modified time-to-failure method may be used as an intermediate-term earthquake prediction technique for locating and predicting the size and time of a future mainshock. Modeling previous mainshocks for hypothesis development indicates that the method predicts the actual magnitude of the mainshock to within approximately ±0.5 magnitude units. The error associated with the time-of-failure is approximately ±1.1 years assuming the last precursory event is known. When the last event in the precursory sequence is not known, the predicted magnitude remains similar, but the predicted time will require refinement as additional events are added, with time, to the sequence. The mainshock location can also be identified within a circular region with a radius on the order of tens of kilometers. Criteria are provided for defining acceleration sequences and mainshock locations. The criteria reduce the number of false predictions but also eliminate some mainshocks from our evaluation. Mainshocks as small as magnitude 5.5, occurring between 1980 and 1995, were evaluated from the Southern California earthquake Catalog (SCC). The results were used in association with previous studies to develop a method that can be used for practical (future prediction) applications. The modified time-to-failure method was used to search the SCC for future mainshocks occurring after 17 August 1998. One region satisfied all the criteria and may be modeled by the modified time-to-failure method. The region likely to have a mainshock is a 65-km-radius area centered at 31.43° N, 115.47° W (northern Baja California, Mexico). The predicted magnitude is 6.36, ±0.55, and the predicted time of failure is 1998.565 (7/25/98), ±1.127 years. The addition of future precursory events will allow refinement of the predicted values.
Smooth Stride Length Change of Rat Robot with a Compliant Actuated Spine Based on CPG Controller

The aim of this research is to investigate the relationship between spinal flexion and quadruped locomotion in a rat robot equipped with a compliant spine, controlled by a central pattern generator (CPG). The study reveals that spinal flexion can enhance limb stride length, but it may also cause significant and unexpected motion disturbances during stride length variations. To address this issue, this paper proposes a CPG model driven by spinal flexion and a novel oscillator that incorporates a circular limit cycle and accounts for the anticipated stride length transition process. This approach effectively matches the torque change with the dynamics of stride length changes, leading to lower energy consumption. Extensive simulations are conducted to evaluate the efficacy of the proposed oscillator and compare it with the original kinetic model and other CPG models. The results demonstrate that the designed CPG model with the proposed oscillator yields smoother gait transitions during stride length variations and reduces energy consumption.
Beacon-Assisted Wireless Powered Communications in Nakagami-m Fading with Multiple Interferers

In this paper, we derive analytical expressions for the outage probability and the ergodic capacity of a wireless powered communications (WPC) system operating in a Nakagami-m fading environment. The source node harnesses power from a dedicated power beacon (PB) with multiple antennas and uses the harvested power to transmit data to a destination node in the presence of co-channel interference. The analysis considers both fixed and random distance between the PB and the source node. The analytical results show the impact of link level parameters such as the path loss exponent, co-channel interference, number of antennas in the PB and density of PBs on the performance of the WPC system. Numerical and simulation results are presented to illustrate the analysis.
Blind Source Separation for Parameter Estimation Under Mixed Gaussian-Impulsive Noise: An U-net++ Based Method

In many practical applications, the communication system suffers from mixed Gaussian-impulsive noise consisting of both Gaussian white noise and non-Gaussian impulsive noise. Obtaining the optimal signal detection algorithm under these scenarios requires the estimate of the mixed Gaussian-impulsive noise parameters. Unfortunately, the estimation accuracy will deteriorate in the presence of the transmitted signal. To solve the problem, we propose a blind source separation method with a neural network, namely U-net++, to separate the transmitted signal from the mixed noise. Then, the parameter estimation can be implemented with the recovered noise samples. An adaptive clipping preprocessing module and a novel loss function of the network are designed based on the statistical property of the mixed noise. Results show that our algorithm outperforms existing baselines on both source separation and parameter estimation.
Disjoint Lookups in Kademlia for Random IDs

Kademlia is the de-facto standard in self-organizing overlay network protocols today. The data structures and procedures defined by the Kademlia protocol are the same for each node. The interaction between nodes Decision-making takes place at node-level with a network usable for data storage and lookup emerging as a result. One of the main tasks within Kademlia is the lookup of resources like a target node or stored data by a source node. In this paper we present a model for generationg network graphs that contain paths between two nodes that are usable by a Kademlia lookup procedure. We use these graphs to compare the lookup success of the original Kademlia lookup procedure with a parallel lookup using multiple node-disjoint paths in the face of stale routing table entries without the need for running network simulations or a real-life Kademlia network.
A Low-cost Waveguide Switch Using Glide-symmetric Holey Electromagnetic Band Gap Technology

This paper presents a novel double pole double through (DPDT) waveguide switch based on glide-symmetric holey electromagnetic band gap (EBG) structure. Applying the glide-symmetric holey (EBG) technology in the electromechanical switches prevents the wave leakage to the gap between the inner rotary part and the outer fixed ring of the switch. Construction of the holey EBG structure is easier and less expensive comparing the conventional bed of nails as the perfect magnetic conductor surface of an EBG structure, because drilling is more cost-effective comparing machining periodic tiny pins. The waveguide paths have been surrounded by one row of glide-symmetric holes. Simulation results show that the isolation between adjacent ports in the OFF state is more than 80 dB in the frequency range of 9-15GHz. The reflection coefficient and insertion loss of the switch ports are less than -35dB and 0.05dB, respectively. Comparing traditional gap waveguide switches, presented switch in this paper has wider bandwidth, higher isolation and lower reflection coefficient.
Using machine learning to improve bovine tuberculosis control in herd level outbreaks

Bovine tuberculosis (bTB), a chronic disease of cattle, is caused by the Mycobacterium bovis infection. Despite having a serious social and economic impact in the United Kingdom and Ireland, there is no antemortem gold standard diagnostic test. Tuberculin skin tests (CICT) are commonly used as a control measure with the interferon gamma (IFN-γ) assay being applied in certain circumstances. This paper utilizes data gathered describing tuberculin regression in reactors (test positive cattle) following the CICT at 72 ± 4 h post injection in herds with large bTB outbreaks. The work then applies machine learning techniques (Decision Trees, Bagging Trees and Random Forests, alongside several balancing approaches) to predict which cattle were likely to be truly infected with tuberculosis, enabling identification of atypical breakdowns that require extra investigation and providing a mechanism for quality assurance of the existing CICT bTB surveillance scheme. The analysis showed that Random Forests (RF) trained using SMOTE balancing had the joint best performance and accuracy (0.90). The importance of the two components of the interferon gamma assay within the RF model also indicated that varying the assay threshold for large outbreaks would be beneficial. Furthermore, the combined use of the RF and IFN- γ models could lead to the improved detection of infection within breakdown herds, reducing the scale and duration of outbreaks. An additional use of these models would be for quality assuring the current bTB surveillance based on CICT and post mortem inspection. Quality control is well recognized as an essential component of a disease surveillance/eradication programme.Clinical Relevance— Bovine tuberculosis remains a disease that is hard to control on a national level. The use of the machine learning model could lead to significant improved detection of infection within breakdown herds, reducing the scale and duration of outbreaks. Advanced modelling, such as this, has the potential to strengthen the efficacy of disease surveillance and the eradication strategy and can meaningfully contribute to animal disease national control plans.
Expanding the repertoire of intermuscular coordination patterns and modulating intermuscular connectivity in stroke-affected upper extremity through electromyogram-guided training: a pilot study

Abnormal intermuscular coordination is a major stroke-induced functional motor impairment in the upper extremity (UE). Previous studies have computationally identified the abnormalities in the intermuscular coordination in the stroke-affected UE and their negative impacts on motor outputs. Therefore, targeting the aberrant muscle synergies has the potential as an effective approach for stroke rehabilitation. Recently, we verified the modifiability of the naturally expressed muscle synergies of young able-bodied adults in UE through an electromyographic (EMG) signal-guided exercise protocol. This study tested if an EMG-guided exercise will induce new muscle synergies, alter the associated intermuscular connectivity, and improve UE motor outcome in stroke-affected UE with moderate-to-severe motor impairment. The study used the six-week isometric EMG signal-guided exercise protocol that focused on independently activating two specific muscles, the biceps and brachioradialis, to develop new muscle activation groups. The study found that both the stroke and age-matched, able-bodied groups were able to develop new muscle coordination patterns through the exercise while habitual muscle activation was still available, which led to improvements in the motor control of the trained arm. In addition, the results provided preliminary evidence of increased intermuscular connectivity between targeted muscles in the beta-band frequencies for stroke patients after training, suggesting a modulation of the common neural drive. These findings suggest that our isometric exercise protocol has the potential to improve stroke survivors' performance of UE in their activities in daily lives (ADLs) and, ultimately, their quality of life through expanding their repertoire of intermuscular coordination.Clinical Relevance— This study shows the feasibility of expanding the intermuscular coordination pattern in stroke-affected UE through an isometric EMG-guided exercise which positively affects task performance and intermuscular connectivity.
An Adaptive, Lightweight, Body-Powered System for Prosthetic Hands Equipped with a Selectively Lockable Differential Mechanism

The need for more practical, robust, and affordable prosthetic hands for amputees has led to significant advancements in their functionality. However, the challenge of designing prostheses that balance dexterity, functionality, and affordability still persists. The lack of prosthetic designs that can efficiently address the needs of amputees for both heavy manual labor and social interactions, while also being lightweight and dexterous, is still evident. This paper presents a design for an anthropomorphic, adaptive, lightweight, body-powered prosthetic hand built for performing Activities of Daily Living using a selectively lockable differential mechanism. The proposed differential mechanism allows the users to flex and lock the prosthesis’ fingers in a wide range of poses and grasps. The performance of the body-powered prosthesis is experimentally validated with three different types of experiments: i) object grasping, ii) total grasping strength, and iii) individual finger force exertion. The resultant prosthetic hand is lightweight and comfortable to wear and is adequately capable of grasping a wide range of objects, including items commonly used in cleaning and cooking applications where waterproofness is essential.
Unobtrusive Sleep Position Classification Using a Novel Optical Tactile Sensor

Unobtrusive sleep position classification is essential for sleep monitoring and closed-loop intervention systems that initiate position changes. In this paper, we present a novel unobtrusive under-mattress optical tactile sensor for sleep position classification. The sensor uses a camera to track particles embedded in a soft silicone layer, inferring the deformation of the silicone and therefore providing information about the pressure and shear distributions applied to its surface.We characterized the sensitivity of the sensor after placing it under a conventional mattress and applying different weights (258 g, 500 g, 5000 g) on top of the mattress in various predefined locations. Moreover, we collected multiple recordings from a person lying in supine, lateral left, lateral right, and prone positions. As a proof-of-concept, we trained a neural network based on convolutional layers and residual blocks that classified the lying positions based on the images from the tactile sensor.We observed a high sensitivity of the optical tactile sensor: Even after placing the sensor below a conventional mattress, we were able to detect our lowest test weight of 258 g. Using the neural network, we were able to classify the four sleep positions, lateral left, lateral right, prone, and supine with a classification accuracy of 91.2 %. The high sensitivity of the sensor, as well as the good performance in the classification task, demonstrate the feasibility of using such a sensor in a robotic bed setup.Clinical Relevance— Positional Obstructive Sleep Apnea is highly prevalent across the general population. Today’s gold standard treatment of using CPAP ventilation is often not accepted, leading to unwanted treatment cessations. Alternative treatments, such as positional interventions through robotic beds are highly promising. However, these beds require reliable detection of the lying position. In this paper, we present a novel, scalable and completely unobtrusive sensor that is concealed under the mattress while classifying sleeping positions with high accuracy.