Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment  

作　　者：Chengyu Li Tingyu Wang Siyu Zhou Yanshuo Sun Zijie Xu Shuxing Xu Sheng Shu Yi Zhao Bing Jiang Shiwang Xie Zhuoran Sun Xiaowei Xu Weishi Li Baodong Chen Wei Tang 

机构地区：[1]Beijing Institute of Nanoenergy and Nanosystems,Chinese Academy of Sciences,Beijing 101400,China [2]School of Nanoscience and Technology,University of Chinese Academy of Sciences,Beijing 100049,China [3]Department of Orthopaedics,Peking University Third Hospital,Beijing 100191,China [4]Engineering Research Center of Bone and Joint Precision Medicine,Ministry of Education,Beijing,China [5]Beijing Key Laboratory of Spinal Disease Research,Beijing,China [6]Center on Nanoenergy Research,School of Physical Science and Technology,Guangxi University,Nanning 530004,China [7]Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences,Guangzhou,China

出　　处：《Research》2025年第1期215-229,共15页研究(英文)

基　　金：supported by The Youth Innovation Promotion Association,CAS,and Key Clinical Projects of Peking University Third Hospital(no.BYSYZD2023004);This study was conducted with approval from the Peking University Third Hospital Medical Science Research Ethics Committee(no.M2021091);all patients were fully informed and provided written consent prior to participation.

摘　　要：Muscle strength(MS)is related to our neural and muscle systems,essential for clinical diagnosis and rehabilitation evaluation.Although emerging wearable technology seems promising for MS assessment,problems still exist,including inaccuracy,spatiotemporal differences,and analyzing methods.In this study,we propose a wearable device consisting of myoelectric and strain sensors,synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities,and then employ a deep learning model based on temporal convolutional network(TCN)+Transformer(Tcnformer),achieving accurate grading and prediction of MS.Moreover,by combining with deep clustering,named Tcnformer deep cluster(TDC),we further obtain a 25-level classification for MS assessment,refining the conventional 5 levels.Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery.We anticipate that this system will importantly advance precise MS assessment,potentially improving relevant clinical diagnosis and rehabilitation outcomes.

关 键 词：surface electromyography mechanical signals myoelectric sensors muscle strength ms clinical diagnosis wearable devices strain sensors 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]