2024/10/31 by Farnaz Rahimi, Mohammad Ali Badamchizadeh, Rahimi, Farnaz +9
Computer Science · Engineering · #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Human-Computer Interaction (cs.HC) #Muscle activation and electromyography studies #Robot Manipulation and Learning
paper · pdf · doi:10.48550/arxiv.2410.23986
openalex publication_date 2024/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In myoelectric control, simultaneous control of multiple degrees of freedom can be challenging due to the dexterity of the human hand. Numerous studies have focused on hand functionality, however, they only focused on a few degrees of freedom. In this paper, a 3DCNN-MLP model is proposed that uses high-density sEMG signals to estimate 20 hand joint positions and grip force simultaneously. The deep learning model maps the muscle activity to the hand kinematics and kinetics. The proposed models' performance is also evaluated in estimating grip forces with real-time resolution. This paper investigated three individual dynamic hand movements (2pinch, 3pinch, and fist closing and opening) while applying forces in 10% and 30% of the maximum voluntary contraction (MVC). The results demonstrated significant accuracy in estimating kinetics and kinematics. The average Euclidean distance across all joints and subjects was 11.01 ± 2.22 mm and the mean absolute error for offline and real-time force estimation were found to be 0.8 ± 0.33 N and 2.09 ± 0.9 N respectively. The results demonstrate that by leveraging high-density sEMG and deep learning, it is possible to estimate human hand dynamics (kinematics and kinetics), which is a step forward to practical prosthetic hands.