Neuromechanical Signal-Based Parallel and Scalable Model for Lower Limb Movement Recognition

Individual who have lost their lower limb because of amputation can use the prosthesis to restore daily living activities. The amputee intent recognition during locomotion modes can be used as source to control lower limb prosthesis. Due to continuous data recording from multiple sensors, the timely recognition of activities of daily living have become a challenging issue for traditional technology and conventional machine learning algorithms. This work hypothesize that parallel discriminant features can be learned from large amount of data generated by aggregating the neuromechanical signals from multiple subjects with parallel and distributed computing platform. Consequently, this paper apply three classifiers including support vector machine, decision tree and random forest on large data sets. The model performance is extensively evaluated in terms of different performance measurement parameters such as accuracy, efficiency, scalability and speedup in sequential and distributed environment. The experimental results show that the parallel approach achieved 3.9x computation speedup as compared to the sequential approach without affecting accuracy level. The parallel support vector machine algorithm demonstrated high speedup and scalability in comparison with random forest and decision tree algorithms. The outcome of this study could promote parallel based model for the unobtrusive recognition of lower limb locomotion modes and could promote the future design for the intelligent control of prostheses and exoskeleton.