Affiliations: [a] CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China | [b] Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China | [c] Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, Guangdong, China | [d] Department of Rehabilitation Medicine, The 7th Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China | [e] State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Correspondence:
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Corresponding author: Peng Fang and Guanglin Li, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China. %****␣thc-31-thc220283_temp.tex␣Line␣50␣**** E-mail: peng.fang@siat.ac.cnandgl.li@siat.ac.cn.
Abstract: BACKGROUND: Grip force estimation is highly required in realizing flexible and accurate prosthetic control. OBJECTIVE: This study presents a method to accurately estimate continuous grip force from surface electromyography (sEMG) under three forearm postures for unilateral amputees. METHODS: Ten able-bodied subjects and a transradial amputee were recruited. sEMG signals were recorded from six forearm muscles on the dominant side of each able-bodied subject and the stump of amputee. Meanwhile, grip force was synchronously measured from the ipsilateral hands of able-bodied subjects and contralateral hand of amputee. Three force profiles (triangle, trapezoid, and fast triangle) were tested under three forearm postures (supination, neutral and pronation). Two algorithms (Generalized Regression Neural Network (GRNN) and Multilinear Regression Model (MLR)) were compared using several EMG features. The estimation performance was evaluated by coefficient of determination (R2) and mean absolute error (MAE). RESULTS: The optimal regressor combining TD and GRNN achieved R2= 96.33 ± 1.13% and MAE= 2.11 ± 0.52% for the intact subjects, and R2= 86.86% and MAE= 2.13% for the amputee. The results indicated that multiple grip force curves under three forearm postures could be accurately estimated for unilateral amputees using mirrored bilateral training. CONCLUSIONS: The proposed method has the potential for precise force control of prosthetic hands.
