Year: 2025
Type: conference-paper
Abstract: Wearable robotics has the capacity to assist and rehabilitate hand function in stroke survivors. Many devices that use surface electromyography (sEMG) for control rely on extrinsic hand muscle signals, since sEMG sensors are relatively easy to place on the forearm without interfering with hand function. In this work, we target the intrinsic muscles of the thumb, which are superficial to the skin and thus potentially more accessible via sEMG sensing. Traditional, rigid electrodes can not be placed on the hand without adding bulk and affecting hand functionality. We thus present a novel sensing sleeve that uses textile electrodes to measure sEMG activity of intrinsic thumb muscles. We evaluate the sleeve's performance on detecting thumb movements and muscle activity during both isolated and isometric muscle contractions of the thumb and fingers. This work highlights the potential of textile-based sensors as a low-cost, lightweight, and non-obtrusive alternative to conventional sEMG sensors for wearable robotics.
Authors: Katelyn Lee, Runsheng Wang, Ava Chen, Lauren Winterbottom, Ho Man Colman Leung, Lisa Maria DiSalvo, Iris Xu, Jingxi Xu, Dawn M. Nilsen, Joel Stein, Xia Zhou, Matei Ciocarlie
Institution: Columbia University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: SCH: INT: An Adaptive Robotic Hand Orthosis with Multimodal Sensing and Continuous Learning , Impact of biofeedback and task-specific training with a robotic hand orthosis on voluntary muscle modulation for rehabilitation post-stroke, A Scalable and Accessible System for Automated Coaching of Human Motion, G9000086069
OpenAlex ID: W4412345712