Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

Wearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These bio‐integrated 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. This review summarizes the latest research on various wearable devices, physiological signal monitoring, and data processing methods for cardiovascular, locomotive, and brain signals. Also, a discussion of device challenges and advantages of current machine learning methods is followed with the suggestion of research directions for future studies.