Abstract:
Real time natural control of assistive, rehabilitation and prosthetic devices has gained significant
importance over the last few decades for the people suffering from motor disabilities due to
stroke, any spinal cord injury or amputation. Although surface electromyography (s-EMG) has
been used as a viable controlling interface for several robotic devices specifically designed for
post stroke therapeutic services. But these conventional controlling strategies are not feasible to
design the rehabilitation or HMI systems based on simultaneous movements of multiple degrees
of freedom (DOF). This paper presents a novel control strategy for HMIs which is based on the
coupled use of EMG and inertial sensors. EMG and kinematic data of healthy and stroke subjects
for commonly used daily life activities has been recorded. Multiple machine learning models
including LDA, QDA, LSVM, QSVM, Fine KNN, Ensembled discriminant, and ensembled
KNN have been applied. Besides this a tri-layered neural network classifier has also been
implemented. A comparative analysis has been performed for the classification outcomes of all
the applied models for EMG, IMU & EMG+IMU data. Overall, the KNN model performed well
for all types of datasets with an average accuracy of 98.5% but results clearly demonstrated that
average classification accuracies for all the applied models have significantly improved for
EMG+IMU data which indicates that sensor fusion based control strategy for prosthesis can
achieve higher performance than conventional control systems for each task. This study is an
effort to provide a new EMG+IMU based technique for fast, efficient, and reliable control of
robotic, rehabilitation and assistive devices for multiple movements with varying DOF.
