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

Hongcheng Xu, Weihao Zheng, Yang Zhang, Daqing Zhao, Lu Wang, Yunlong Zhao, Weidong Wang (), Yangbo Yuan, Ji Zhang, Zimin Huo, Yuejiao Wang, Ningjuan Zhao, Yuxin Qin, Ke Liu, Ruida Xi, Gang Chen, Haiyan Zhang, Chu Tang, Junyu Yan, Qi Ge, Huanyu Cheng (), Yang Lu () and Libo Gao ()
Additional contact information
Hongcheng Xu: Xidian University
Weihao Zheng: Xidian University
Yang Zhang: School of Biomedical Engineering, Air Force Medical University
Daqing Zhao: The Second Affiliated Hospital of Air Force Medical University
Lu Wang: The Second Affiliated Hospital of Air Force Medical University
Yunlong Zhao: Xiamen University
Weidong Wang: Xidian University
Yangbo Yuan: Xidian University
Ji Zhang: Xidian University
Zimin Huo: Xidian University
Yuejiao Wang: Tsinghua University
Ningjuan Zhao: Xidian University
Yuxin Qin: Xidian University
Ke Liu: Xidian University
Ruida Xi: Xidian University
Gang Chen: Xidian University
Haiyan Zhang: Xidian University
Chu Tang: Ministry of Education, School of Life Science and Technology, Xidian University
Junyu Yan: Xidian University
Qi Ge: Southern University of Science and Technology
Huanyu Cheng: The Pennsylvania State University
Yang Lu: The University of Hong Kong, Pokfulam
Libo Gao: Xiamen University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-43664-7

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