Open AccessMuscle Synergy and Musculoskeletal Model-Based Continuous Multi-Dimensional Estimation of Wrist and Hand Motions
Author(s) -
Yeongdae Kim,
Sorawit Stapornchaisit,
Hiroyuki Kambara,
Natsue Yoshimura,
Yasuharu Koike
Publication year - 2020
Publication title -
journal of healthcare engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.509
H-Index - 29
eISSN - 2040-2309
pISSN - 2040-2295
DOI - 10.1155/2020/5451219
Subject(s) - wrist , electromyography , range of motion , linear regression , mean squared error , correlation coefficient , artificial intelligence , computer science , algorithm , mathematics , physical medicine and rehabilitation , medicine , physical therapy , machine learning , statistics , anatomy
In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 ± 0.0844 Pearson correlation coefficient ( r ) value in two-dimensional wrist motion estimation compared with 0.7608 ± 0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 ± 0.0503 r value with 0.2559 ± 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed.