The Use of Wearable Sensors for the Classification of Electromyographic Signal Patterns based on Changes in the Elbow Joint Angle

Rahman, Sabuktagin; Ali, Md. Asraf; Mamun, Md. Abdullah Al

doi:10.1016/j.procs.2021.05.043

Cited by 5 publications

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

2022

View full text Add to dashboard Cite

Analysis & Sensing www.analysis-sensing.org Review doi.org/10.1002/anse.202200062Wearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These biointegrated wearable systems can potentially fill gaps in conventional clinical practice by providing highly cost-effective health characterization and portable continuous health monitoring. Further, the physiological signals measured by wearables require post-processing to derive meaningful values, such as heart rate or blood oxygen saturation. This requirement, in conjunction with the smaller form factor and limited sensor count of the miniaturized systems, often necessitates robust signal processing and data analysis to approach the stringent performance specifications of conventional medical devices, and machine learning techniques have found success in filling this analytical role for their ability to learn complex functional relationships. Thus, this review outlines a systematic summary of the latest research on various wearable devices and their biosignal sensing and signal processing methods, emphasizing machine learning. We also discuss the developmental challenges and advantages of current machine-learning methods, while suggesting research directions for future studies.

show abstract