A Method for Locomotion Mode Identification Using Muscle Synergies

Active lower limb transfemoral prostheses have enabled amputees to perform different locomotion modes such as walking, stair ascent, stair descent, ramp ascent and ramp descent. To achieve seamless mode transitions, these devices either rely on neural information from the amputee’s residual limbs or sensors attached to the prosthesis to identify the intended locomotion modes or both. We present an approach for classification of locomotion modes based on the framework of muscle synergies underlying electromyography signals. Neural information at the critical instances (e.g., heel contact and toe-off) was decoded for this purpose. Non-negative matrix factorization was used to extract the muscles synergies from the muscle feature matrix. The estimation of the neural command was done using non-negative least squares. The muscle synergy approach was compared with linear discriminant analysis (LDA), support vector machine (SVM), and neural network (NN) and was tested on seven able-bodied subjects. There was no significant difference ( $p > 0.05$ ) in transitional and steady state classification errors during stance phase. The muscle synergy approach performed significantly better ( $p < 0.01$ ) than NN and LDA during swing phase while results were similar to SVM. These results suggest that the muscle synergy approach can be used to discriminate between locomotion modes involving transitions.