A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography

被引:3
作者
Xu, Pan [1 ]
Yang, Xudong [2 ]
Ma, Wei [1 ]
He, Wanting [2 ]
Vasic, Zeljka Lucev [3 ]
Cifrek, Mario [3 ]
Gao, Yueming [1 ]
机构
[1] Fuzhou Univ, Coll Phys & Informat Engn, Fuzhou 350108, Peoples R China
[2] Fuzhou Univ, Sch Adv Mfg, Fuzhou 350108, Peoples R China
[3] Univ Zagreb, Fac Elect Engn & Comp, HR-10000 Zagreb, Croatia
来源
IEEE JOURNAL OF ELECTROMAGNETICS RF AND MICROWAVES IN MEDICINE AND BIOLOGY | 2023年 / 7卷 / 01期
基金
中国国家自然科学基金;
关键词
Handgrip force prediction; electrical impedance myography; long short-term memory; prosthetic control; muscle rehabilitation; MUSCLE; IDENTIFICATION;
D O I
10.1109/JERM.2023.3241769
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
force prediction is widely used in the rehabilitation of the arm and prosthetic control. To investigate the effects of different measurement positions and feature parameters on the results of handgrip force prediction, a model based on electrical impedance myography (EIM) and long short-term memory (LSTM) networks was proposed to compare and determine a better scheme for handgrip force prediction. We conducted the signal acquisition experiments of impedance and handgrip force on the anterior forearm muscles and brachioradialis muscle. Afterwards, three evaluation metrics were introduced to compare the prediction results of various models, and the variability between models was analyzed using paired sample t-tests. The results showed that the model of handgrip force prediction based on anterior forearm muscles exhibited better performance in predicting. The evaluation metrics of R2, explained variance score (EVS) and normalized mean square error (NMSE) for the model fusing the feature parameters resistance (R) and reactance (X) were 0.9023, 0.9173 and 0.0114, respectively. Therefore, the feature parameters fusing R and X are the optimal input for the handgrip force prediction model. The anterior forearm muscles are the preferred position for impedance measurement over the brachioradialis muscle. This paper validated the feasibility of EIM for handgrip force prediction and provided a new reference and implementation scheme for muscle rehabilitation training and prosthetic control.
引用
收藏
页码:90 / 98
页数:9
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