Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Abstract

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered precise recognition and practical applications of this technology. Recent advancements in artificial intelligence technology have made significant strides in extracting features from massive and intricate datasets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques to classify simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training datasets. Thereby, machine-learning techniques have endowed the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures, and machine-learning algorithms for hand gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

Latest developments and trends in electronic skin devices

“…ML methods can greatly enhance the intelligence level of an e-skin system, which can greatly improve the performance of human-machine interfaces (HMI), and show a broad application prospect in medical health, rehabilitation therapy and remote monitoring [201][202][203][204] . They can learn feature signals corresponding to a certain stimulus from a large amount of experimental data, which can recognize different types of stimuli (such as gesture, touch strength, texture, and shape) [205][206][207][208] .…”

Advanced Design of Soft Robots with Artificial Intelligence

“…It is noticeable that in real life, a diversity of hand motions can be realized by the fingers and wrists to accomplish fully interactions with the environment (Fig. 8c), and therefore the cognitive capabilities of humans to identify hand gestures have been simulated by a series of wearable sensors assisted with ML [134]. In addition to the recognition of hand motions, it is also essential to conduct the research of decoding the epicentral human motions for the reason that it can help to promote the motion tracking and health-monitoring (Fig.…”