Intent based recognition of walking and ramp activities for amputee using sEMG based lower limb prostheses

The use of surface electromyogram (sEMG) has grown in the field of gait analysis, prostheses and exoskeleton. Surface electromyogram can directly reflect the human intention for locomotion modes and can be used as a source of control for lower limb prosthesis. The variations factors such as non-Gaussian nature of sEMG signal and mobility of amputees have been observed to degrade the activity recognition performance. This study investigates the properties of the sEMG signal with the purpose of determining the discriminant features to classify the feature space into various activities especially in the context of amputees. To address the variations in activity recognition performance, this study proposed the magnitude of bispectrum as a novel feature extraction method that is invariant to the variations factors and an unsupervised feature reduction method was used to extract the discriminant features. Furthermore, sEMG signals from eleven wearable sensors located on the lower limb muscles were recorded from six subjects including four able-bodies, one unilateral transtibial, and one unilateral transfemoral amputee during walking and ramp activities. Distinct muscles were selected using the L1-norm method. Effective classifier namely support vector machine and linear discriminant analysis were used to classify the multi-class sEMG signal patterns. The experimental results consistently showed an average accuracy of 99.7%. Further evaluation on three different types of prostheses revealed that the proposed method is more robust compared to the existing methods. The promising results of this study can be applied potentially in the control of lower limb wearable devices such as prostheses/exoskeletons.