Effects of Training and Calibration Data on Surface Electromyogram-Based Recognition for Upper Limb Amputees.

ABSTRACT: Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for

SUBMITTER: Yao P 

PROVIDER: S-EPMC10857392 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature