A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation

• Published in: Nature communications Vol. 14; no. 1; pp. 7769 - 14
• Main Authors: Xu, Hongcheng, Zheng, Weihao, Zhang, Yang, Zhao, Daqing, Wang, Lu, Zhao, Yunlong, Wang, Weidong, Yuan, Yangbo, Zhang, Ji, Huo, Zimin, Wang, Yuejiao, Zhao, Ningjuan, Qin, Yuxin, Liu, Ke, Xi, Ruida, Chen, Gang, Zhang, Haiyan, Tang, Chu, Yan, Junyu, Ge, Qi, Cheng, Huanyu, Lu, Yang, Gao, Libo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/61; 639/166/985; 639/166/987; Accelerometers; Electric contacts; Equipment Design; Humanities and Social Sciences; Humans; Hydrogels; Learning algorithms; Machine learning; Monitoring; Motion; Movement; multidisciplinary; Muscles; Remote monitoring; Science; Science (multidisciplinary); Skin; Software; Vibrations; Wearable Electronic Devices
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-43664-7

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases. Methods for the wireless, continuous monitoring and analysis of activities directly from the throat skin have not been developed. Here, the authors present a stretchable device platform that provides wireless measurements and machine learning-based analysis of vibrations and muscle electrical activities from the throat.