Selecting the optimal movement subset with different pattern recognition based EMG control algorithms
Pattern Recognition (PR) based EMG controllers of multi functional upper limb prostheses have been recently deployed on commercial state of the art prostheses, offering intuitive control with the ability to control large number of movements with fast reaction time. Current challenges with such PR systems include the lack of training and deployment protocols that can help optimize the system's performance based on amputees' needs. Selecting the best subset of movements that each individual amputee can perform will help to exclude movements that have poor performance so that a subject specific training can be achieved. In this paper, we propose to select the best set of movements that each amputee can perform as well as identifying the movements for which the PR system would have the worst performance and, therefore, would require further training. Unlike previous studies in this direction, different feature extraction and classification methods were utilized to examine if the choice of features/classifiers could affect the best movements subset selection. We performed our experiments on EMG signals collected from four transradial amputees with an accuracy 97.5% on average across all subjects for the selection of best subset of movements.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)