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Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.Clinical relevance- Coughing is a ubiquitous symptom of pulmonary disease, especially for patients with COPD and asthma. This work explores the possibility and and presents the result of cough detection using an IMU sensor embedded in earables.This work presents a wireless time-scaling chaotic shift keying encryption system that can be used in wireless body area network applications. In wireless sensor nodes, the communication protocol being used provides some security measures and is implemented in software. selleckchem However, no additional security measures are usually implemented. This paper demonstrates a discrete level real time encryption system using analog circuitry on a printed circuit board. The encryption system uses op amps, multipliers and resistors to implement the encryption. To implement wireless capabilities, commercial wireless microcontrollers using Bluetooth Low Energy were added, and a custom Bluetooth Low Energy profile was created to stream the analog encrypted signal.Clinical relevance- This work demonstrates an encryption system for wireless sensor devices for improved protection of private health information.Motor imagery combining virtual reality (VR) technique has recently been reported to have an increasingly positive impact on post-stroke rehabilitation. However, there is a common problem that the engagement of patients cannot be confirmed during motor imagery training due to a lack of effective feedback control. This paper proposes a VR-based motor imagery training system for post-stroke rehabilitation, using surface electromyographic (EMG)-based real-time feedback to enable the personalized training and quantitative assessment of participation degree. Three different experiments including assessment experiment, action observation (AO), combined motor imagery and action observation (MI+AO) experiment were performed on 4 post-stroke patients to verify the system. The immersive scenario of the VR system provides a shooting basketball training for bilateral upper limbs. The EMG data of assessment of each participant was collected to calculate the thresholds, which was utilized in the subsequent experiments based on real-time feedback of EMG. The result reveals significant differences of the muscle strength between AO and MI+AO experiments. This demonstrates that the EMG-based feedback is effective to be of use in assessment of participation degree. The primary application shows that VR-assisted motor imagery system has potential to provide personalized and more engaged training for post-stroke rehabilitation.Sensor-based Human Activity Recognition (HAR) plays an important role in health care. However, great individual differences limit its application scenarios and affect its performance. Although general domain adaptation methods can alleviate individual differences to a certain extent, the performance of these methods is still not satisfactory, since the feature confusion caused by individual differences tends to be underestimated. In this paper, for the first time, we analyze the feature confusion problem in cross-subject HAR and summarize it into two aspects Confusion at Decision Boundaries (CDB) and Confusion at Overlapping (COL). The CDB represents the misclassification caused by the feature located near the decision boundary, while the COL represents the misclassification caused by the feature aliasing of different classes. In order to alleviate CDB and COL to improve the stability of trained model when processing the data from new subjects, we propose a novel Adversarial Cross-Subject (ACS) method. Specifically, we design a parallel network that can extract features from both image space and time series simultaneously. Then we train two classifiers adversarially, and consider both features and decision boundaries to optimize the distribution to alleviate CDB. In addition, we introduce Minimum Class Confusion loss to reduce the confusion between classes to alleviate COL. The experiment results on USC-HAD dataset show that our method outperforms other generally used cross-subject methods.One deadly aspect of COVID-19 is that those infected can often be contagious before exhibiting overt symptoms. While methods such as temperature checks and sinus swabs have aided with early detection, the former does not always provide a reliable indicator of COVID-19, and the latter is invasive and requires significant human and material resources to administer. This paper presents a non-invasive COVID-19 early screening system implementable with commercial off-the-shelf wireless communications devices. The system leverages the Doppler radar principle to monitor respiratory-related chest motion and identifies breathing rates that indicate COVID-19 infection. A prototype was developed from software-defined radios (SDRs) designed for 5G NR wireless communications and system performance was evaluated using a robotic mover simulating human breathing, and using actual breathing, resulting in a consistent respiratory rate accuracy better than one breath per minute, exceeding that used in common medical practice.Clinical Relevance-This establishes the potential efficacy of wireless communications based radar for recognizing respiratory disorders such as COVID-19.This paper presents the experimental findings towards developing carbonized microelectrodes using a Joule heating process within a temperature window that is compatible with CMOS. Bridge-on-pillars polymer structures have been 3D-printed using two-photon polymerization (2PP). They have been annealed in various processing conditions to increase the fraction of carbon in the precursor material and to achieve appreciable electric conductivity so that they can be used to drive current to enable Joule heating. To evaluate the outcome of the processing sequences, Raman spectroscopy has been performed to assess the degree of carbonization. Such CMOS-compatible carbon electrodes are important for monolithic, low-cost biosensor development.Clinical relevance- This establishes the potential of carbonized polymer electrode for low-cost, CMOS-compatible monolithic biosensor platform for implementation in medical diagnosis and treatment.Lower limb exoskeleton robots have shown great potential in assistive and rehabilitative applications, allowing individuals with motor impairment, such as spinal cord injury (SCI) patients, to perform overground gait. Most assistive lower limb exoskeletons require users to use crutches to balance themselves during standing and walking. However, long-term crutch usage has been demonstrated to be potentially harmful to the shoulder joints, due to the repetitive high shoulder reaction forces. Investigations into the shoulder loads experienced during exoskeleton use are needed to understand the extent of this harm and, if required, to reduce the risk of injury. In this preliminary study, the effects of different gait patterns on the shoulder load are investigated in an experiment involving three able-bodied individuals. Specifically, the differences in shoulder load during exoskeleton walking are studied with two commonlyobserved gait patterns (1) the four-point parallel crutch gait and (2) the four-point reciprocal crutch gait. Contact forces between the ground and the human-exoskeleton system were recorded and used to indicate shoulder reaction force. The results suggested no significant differences in maximum force and maximum rate of loading between the two crutch gait patterns, and only minor differences in force time integral. This indicates that shoulder reaction force may not be a significant factor when choosing between crutch gaits during exoskeleton use.Continuous real-time health monitoring in animals is essential for ensuring animal welfare. In ruminants like cows, rumen health is closely intertwined with overall animal health. Therefore, in-situ monitoring of rumen health is critical. However, this demands in-body to out-of-body communication of sensor data. In this paper, we devise a method of channel modeling for a cow using experiments and FEM based simulations at 400 MHz. This technique can be further employed across all frequencies to characterize the communication channel for the development of a channel architecture that efficiently exploits its properties.Wearables in the biomedical domain have been of extensive use in the current era. Given the importance of wearable computing, it has become necessary to innovate on enhancing hardware efficiency. The domain of approximate computing offers a conclusive method to lower area, power and delay in hardware in addition to a marginal loss in accuracy. In this paper, we investigate ApproxBioWear, a technique which enables the use of approximate computing for efficient biomedical wearable computing at the edge. The methodology involves approximating additions during the functional stages of an error-resilient biomedical signal processing algorithm and determining the application accuracy. Upon evaluating the Pan-Tompkins algorithm, which is used to detect QRS peaks in ECG signals, it is observed that the ApproxBioWear approach reduces the power consumption and chip area by 19.27% and 19.71% respectively on an average with a marginal loss in accuracy.Class-D half and full-bridge power amplifiers (PA) finds their usefulness in wireless power transfer (WPT) blocks for a biomedical implant. This brief presents a 13.56-MHz wireless power transfer system using an adaptive PA structure and digital control scheme for providing sufficient power during downlink data transfer. This scheme prevents efficiency degradation due to amplitude modulation. Simultaneously changing PA structure and operating frequency gives a higher degree of freedom for power modulation. The transmitter and receiver sides were designed in the 0.18-μm CMOS process using 1.8 V and 5 V devices.Physical therapy is important for the treatment and prevention of musculoskeletal injuries, as well as recovery from surgery. In this paper, we explore techniques for automatically determining whether an exercise was performed correctly or not, based on camera images and wearable sensors. Classifiers were tested on data collected from 30 patients during normally-scheduled physical therapy appointments. We considered two lower limb exercises, and asked how well classifiers could generalize to the assessment of individuals for whom no prior data were available. We found that our classifiers performed well relative to several metrics (mean accuracy 0.76, specificity 0.90), but often returned low sensitivity (mean 0.34). For one of the two exercises considered, these classifiers compared favorably with human performance.Techniques for 3D endoscopic systems have been widely studied for various reasons. Among them, active stereo based systems, in which structured-light patterns are projected to surfaces and endoscopic images of the pattern are analyzed to produce 3D depth images, are promising, because of robustness and simple system configurations. For those systems, finding correspondences between a projected pattern and an original pattern is an open problem. Recently, correspondence estimation by graph neural networks (GCN) using graph-based representation of the patterns were proposed for 3D endoscopic systems. One severe problem of the approach is that the graph matching by GCN is largely affected by the stability of the graph construction process using the detected patterns of a captured image. If the detected pattern is fragmented into small pieces, graph matching may fail and 3D shapes cannot be retrieved. In this paper, we propose a solution for those problems by applying deep-layered GCN and extended graph representations of the patterns, where proximity information is added.
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