Evaluation and Rehabilitation System for Ulnar-Innervated Muscles Facilitated by Rare Earth Oxide-Enhanced Triboelectric Sensor.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date: Jun 18, 2025

Abstract

Ulnar nerve injuries often lead to muscle atrophy and reduced hand function, necessitating precise monitoring and effective rehabilitation strategies. Current grip strength measurement tools rely on rigid mechanical equipment, which is inconvenient and requires frequent calibration. To address this, a muscle atrophy evaluation and rehabilitation system (MUERS) is presented, featuring a highly sensitive rare earth oxide-enhanced triboelectric sensor (RETS). Utilizing the unique electrochemical properties of rare earth oxides, RETS demonstrates a linear voltage-force response in the range of 8-80 kPa, with a maximum linear error of 1.5%. Integrated with a multi-channel STM32 signal collector, RETS enables real-time grip strength monitoring across all five fingers. Combining sensor output with an SVM algorithm, the system achieves 98.61% accuracy in identifying finger grip strength injuries and classifies damage into three levels with an average accuracy of 96.67%. MUERS evaluates rehabilitation progress by scoring grip strength and providing feedback to clinicians. Over a four-week cycle, it consistently captures improvements in muscle recovery, aiding individualized rehabilitation plans. This system offers fine-grained assessment capabilities for diagnosing and monitoring nerve injury-induced muscle atrophy, paving the way for advanced biomedical sensing and personalized rehabilitation.

Authors

  • Yijun Hao
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.
  • Keke Hong
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.
  • Jiayi Yang
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
  • Tianyu Jia
    Department of Mechanical Engineering, Division of Intelligent and Biomimetic Machinery, State Key Laboratory of Tribology, Tsinghua University, Beijing, China.
  • Xiangqian Lu
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.
  • Zhao Guo
    School of Power and Mechanical Engineering, Wuhan University, 430072, Wuhan, China.
  • Zhipeng Wang
    Department of Pharmacy, Changzheng Hospital, Second Military Medical University, Shanghai, 200003, PR China.
  • Yong Qin
    IBM Research, Beijing, China.
  • Wei Su
    Department of Science and Technology, Hebei Agricultural University, Huanghua, China.
  • Dong Yang
    College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology Xi'an 710021 China yangdong@sust.edu.cn.
  • Hongke Zhang
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.
  • Chuguo Zhang
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.
  • Zhong Lin Wang
    Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing China.
  • Xiuhan Li
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

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