Robust feature sets for contraction level invariant control of upper limb myoelectric prosthesis. (May 2019) - Iqbal, Nisheena V.; Subramaniam, Kamalraj; Asmi P., Shaniba
Highlights: Feature sets robust against force level variations in pattern recognition based control of myoelectric upper limb prosthesis have been proposed. Significance of using non-linear techniques to analyze complex signals like EMG, which are both non-linear and non-stationary has been highlighted here. With a significant improvement in classification performance of about 7% to 9%, the proposed feature sets, FFS and PSEAR outperformed all the other well established feature sets for contraction level invariant myoelectric control, considered in this paper. Improvement in performance was observed not only for flexion movements but also for prehension and power grip movements. The proposed feature sets can be potential candidates to make the clinical implementation of intuitive PR-based myoelectric prosthetic control possible in the future. Abstract: In spite of the tremendous progress of upper limb myoelectric prosthetic control in the field of rehabilitation engineering, there still exist several real world challenges to be met, before realizing it as a good substitute for a natural arm. Incompetence of the system to accommodate variations in contraction levels of muscle movements has been identified as one of the significant challenges, as these variations have a subsequent impact on the performance of pattern recognition based myoelectric control. Non-linear techniques are more suited to characterize myoelectric signals since one of their major properties is nonlinearity. Based on this we propose two feature combinations which can lead to a reliable control scheme that is robust against contraction level variations. The performance of our proposed features when tested on nine transradial amputees for six motion classes at three different force levels outweighed other established feature extraction methods meant for contraction variation independent control. Significant improvement of around 8% in average classification performance was achieved across all subjects and force levels, subjected to training, both with all force levels and with unseen force levels. Moreover, these features achieved superior performance in classifying flexion as well as grip movements.