A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings.

ABSTRACT:

Background

Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (θ), wrist joint horizontal (X) and vertical (Y) positions in a continuous and simultaneous manner.

Results

Ten subjects were examined from which we recorded arm kinematics and EMG signals of the bic

SUBMITTER: ElMohandes H 

PROVIDER: S-EPMC9440508 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature