Junyun Fu;Chengyu Lin;Jinxin Sun;Kong Hoi Cheng;Yuquan Leng;Chenglong Fu
{"title":"基于hill型肌肉模型的功能电刺激指尖预测力控制。","authors":"Junyun Fu;Chengyu Lin;Jinxin Sun;Kong Hoi Cheng;Yuquan Leng;Chenglong Fu","doi":"10.1109/TNSRE.2025.3605816","DOIUrl":null,"url":null,"abstract":"Functional electrical stimulation (FES) is an effective technique for restoring or enhancing hand motor function in patients with neurological impairments, such as those recovering from stroke or spinal cord injuries. Although many studies have used phenomenological models to investigate the control of FES, few studies have simultaneously employed both methods to study finger output force. This study aims to accurately predict finger output force using the Hill model and a multi-joint finger model under different current conditions. The Hill model describes the mechanical properties of muscles and establishes the relationship between muscle activation and joint motion. In this study, personalized Hill models were customized for each participant, and the accuracy of the models was validated by comparing expected forces with actual force outputs. The experimental results show that under optimal conditions the normalized root mean square error between the measured force and the target force can be reduced to 5.1% of the maximum target force. Furthermore, this method enabled coordinated control of multiple fingers, facilitating a variety of grasping tasks. The proposed method offers significant potential for improving FES applications in hand rehabilitation and assistive robotics, providing a promising approach for precise force control in the rehabilitation of paralyzed patients.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3594-3604"},"PeriodicalIF":5.2000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150434","citationCount":"0","resultStr":"{\"title\":\"Predictive Force Control of Fingertip Induced by Functional Electrical Stimulation Based on a Hill-Type Muscle Model\",\"authors\":\"Junyun Fu;Chengyu Lin;Jinxin Sun;Kong Hoi Cheng;Yuquan Leng;Chenglong Fu\",\"doi\":\"10.1109/TNSRE.2025.3605816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Functional electrical stimulation (FES) is an effective technique for restoring or enhancing hand motor function in patients with neurological impairments, such as those recovering from stroke or spinal cord injuries. Although many studies have used phenomenological models to investigate the control of FES, few studies have simultaneously employed both methods to study finger output force. This study aims to accurately predict finger output force using the Hill model and a multi-joint finger model under different current conditions. The Hill model describes the mechanical properties of muscles and establishes the relationship between muscle activation and joint motion. In this study, personalized Hill models were customized for each participant, and the accuracy of the models was validated by comparing expected forces with actual force outputs. The experimental results show that under optimal conditions the normalized root mean square error between the measured force and the target force can be reduced to 5.1% of the maximum target force. Furthermore, this method enabled coordinated control of multiple fingers, facilitating a variety of grasping tasks. The proposed method offers significant potential for improving FES applications in hand rehabilitation and assistive robotics, providing a promising approach for precise force control in the rehabilitation of paralyzed patients.\",\"PeriodicalId\":13419,\"journal\":{\"name\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"volume\":\"33 \",\"pages\":\"3594-3604\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150434\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11150434/\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11150434/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Predictive Force Control of Fingertip Induced by Functional Electrical Stimulation Based on a Hill-Type Muscle Model
Functional electrical stimulation (FES) is an effective technique for restoring or enhancing hand motor function in patients with neurological impairments, such as those recovering from stroke or spinal cord injuries. Although many studies have used phenomenological models to investigate the control of FES, few studies have simultaneously employed both methods to study finger output force. This study aims to accurately predict finger output force using the Hill model and a multi-joint finger model under different current conditions. The Hill model describes the mechanical properties of muscles and establishes the relationship between muscle activation and joint motion. In this study, personalized Hill models were customized for each participant, and the accuracy of the models was validated by comparing expected forces with actual force outputs. The experimental results show that under optimal conditions the normalized root mean square error between the measured force and the target force can be reduced to 5.1% of the maximum target force. Furthermore, this method enabled coordinated control of multiple fingers, facilitating a variety of grasping tasks. The proposed method offers significant potential for improving FES applications in hand rehabilitation and assistive robotics, providing a promising approach for precise force control in the rehabilitation of paralyzed patients.
期刊介绍:
Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.