2026-10-11T22:34:37Z https://www.scidb.cn/oai

10.57760/sciencedb.24950 2025-05-12T11:08:10Z

2025-05-12 Ultrastretchable adaptive epidermal bracelet based on integrated crosstalk-free dual-mode sensors for accurate human-machine interactions doi:10.57760/sciencedb.24950 en The development of multi-mode human-machine interfaces is highly anticipated for various human-machine interaction (HMI) applications, yet the challenge of acquiring crosstalk-free multimodal physiological signals using a single compact device persists. Here, a self-powered integrated Electromyogram (EMG)-Forcemyography (FMG) dual-mode sensor (IEFDS) is designed to simultaneously capture these two signals without crosstalk. This sensor utilizes connective organohydrogel electrodes tailored for skin impedance matching. Muscle movements generate distinct EMG and FMG signals, which the IEFDS quantifies as potential differences within unique frequency bands, and the designed decoupling algorithm ensures the high-fidelity extraction of these signals. Subsequent decoding by artificial intelligence algorithms accurately interprets motion intentions. The sensor array is further monolithically integrated onto a continuous organohydrogel film with impressive self-adhesive and mechanical properties to construct an 8-channel integrated HMI bracelet using an electrochemical soldering strategy. The adaptive bracelet biointerface achieves a stretchability at least three times greater than the state-of-the-art (up to 100% strain), enabling it to conform to different individuals and body parts, and resulting in high movement recognition accuracy (97%). Furthermore, the wireless smart bracelet is further leveraged for the remote and accurate control of various execution terminals (active rehabilitation robots, robotic arms/hands, etc.), highlighting its potential for various advanced HMIs. lectromyogram (EMG)-Forcemyography (FMG) dual-mode sensor; Human-machine interaction; Hydrogel sensor Hao Wang Yubin Zhou Yibing Luo Mingxuan Xiao Ruijie Xie Binghua Zou Huizhi Chen Yan Wang Shaowu Pan Daohui Zhang Kai Tao Fei Liu Fengwei Huo Jin Wu PUBLIC https://creativecommons.org/publicdomain/zero/1.0/ dataset http://www.doi.org/10.21203/rs.3.rs-7611464/v1 Science Data Bank