TY  - JOUR
KW  - Bioimpedance
KW  -  multi-degree of freedom
(DoF)
KW  -  human?machine interaction (HMI)
KW  -  forearm motion
KW  - 
hand grasp
KW  -  robotic control
KW  -  regression model
KW  -  long
short-term memory (LSTM)
ID  - discovery10206105
N2  - This paper introduces a novel methodology for simultaneously predicting hand grasp and forearm motion using target muscle bioimpedance measurements and regression models. A total of six channels, formed by nine electrodes, are employed for this multi-degree of freedom (DoF) prediction. Given the time-dependent nature of bioimpedance variation, the long short-term memory (LSTM) regression model is more competent in multi-DoF prediction, compared to linear regression (LR), support vector regression (SVR) and multilayer perceptron (MLP). In intra-subject cross-validation, MLP yields an average coefficient of determination (R2) of 0.9256 for predicting hand grasping angle, while LSTM achieves an average R2 of 0.9483 for predicting random simultaneous forearm two-DoF motion. Operation by amputees without the need to train the regression models is possible by mapping muscle bioimpedance variation directly to the prediction angle, allowing for the approximate estimation of single-DoF motion. The efficacy of these prediction approaches is demonstrated in a real-time object grasping task.
PB  - IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
TI  - Forearm Motion and Hand Grasp Prediction Based on Target Muscle Bioimpedance for Human?Machine Interaction
EP  - 769
AV  - public
Y1  - 2025/02/04/
JF  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
A1  - Yao, Tianyang
A1  - Wu, Yu
A1  - Jiang, Dai
A1  - Bayford, Richard
A1  - Demosthenous, Andreas
SN  - 1534-4320
UR  - https://doi.org/10.1109/tnsre.2025.3538609
N1  - Copyright © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.
For more information, see https://creativecommons.org/licenses/by/4.0/.
SP  - 760
VL  - 33
ER  -