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

Y Yijun Hao (School of Electronic and Information Engineering Beijing Jiaotong University Beijing China) K Keke Hong (School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 P. R. China) J Jiayi Yang T Tianyu Jia X Xiangqian Lu (School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan Shandong P. R. China) Z Zhao Guo (College of Chemistry and Chemical Engineering/Institute of Polymers and Energy Chemistry (IPEC) Nanchang University Nanchang China) Z Zhipeng Wang (Institute of Nuclear and New Energy Technology, Tsinghua University) Y Yong Qin (Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug, West China School of Pharmacy) W Wei Su (School of Energy and Environmental Engineering) D Dong Yang H Hongke Zhang (School of Electronic and Information Engineering Beijing Jiaotong University Beijing China) C Chuguo Zhang (School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 P. R. China) Z Zhong Lin Wang (Center for High-Entropy Energy and Systems) X Xiuhan Li

Abstract

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.

Article Details

Volume / Issue Vol. 37, Issue 35
Published September 01, 2025
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (14)

Y

Yijun Hao

School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

K

Keke Hong

School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 P. R. China

J

Jiayi Yang

T

Tianyu Jia

X

Xiangqian Lu

School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan Shandong P. R. China

Z

Zhao Guo

College of Chemistry and Chemical Engineering/Institute of Polymers and Energy Chemistry (IPEC) Nanchang University Nanchang China

Z

Zhipeng Wang

Institute of Nuclear and New Energy Technology, Tsinghua University

Y

Yong Qin

Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug, West China School of Pharmacy

W

Wei Su

School of Energy and Environmental Engineering

D

Dong Yang

H

Hongke Zhang

School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

C

Chuguo Zhang

School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 P. R. China

Z

Zhong Lin Wang

Center for High-Entropy Energy and Systems

X

Xiuhan Li