抓住
计算机科学
运动(物理)
人工智能
非线性系统
假手
模式识别(心理学)
手腕
均方误差
计算机视觉
数学
统计
物理
放射科
程序设计语言
医学
量子力学
作者
Xiangxin Li,Yan Liu,Xiaomeng Zhou,Zijian Yang,Tian Lan,Fang Peng,Guanglin Li
标识
DOI:10.1016/j.bspc.2023.105044
摘要
Simultaneously and accurately estimating both grasp motion classes and force based on surface electromyogram (EMG) of residual arms is essential for precise grasping with a dexterous prosthetic hand. However, accurate grasping-mode prediction and grasping-force estimation often contradict each other. When performing hand grasping motion, different grasping forces will generate different EMG patterns, which would decay the grasp motion prediction accuracy. In this study, we proposed a framework of fusing motion classification and continuous grasp force estimation based on a new set of nonlinear Mel-Frequency Spectrum features of EMG signals. The performance of the proposed method was tested on eight able bodied (AB) subjects and four transradial amputees (TR) who are the end-users of dexterous prostheses. The experimental results showed that the newly proposed features obtained a significant reduction in the average classification error rate compared to other well-known feature sets, achieving improvements of about 5 % to 28 % in the average classification accuracy across all subjects and force levels. Simultaneously, the proposed features obtained the best continuous grasp force estimation accuracies with R2 (coefficient of determination) of 0.96 and 0.83, and root mean square of maximum voluntary contractions of grasp force (std%GFMVC) of 6.2 % and 10.5 % for the AB and TR subjects, respectively, compared to the previous EMG features. This study suggested that the proposed method would be helpful for the stable and fine manipulation of multifunctional prosthetic hand.
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