Deep ensemble learning approach for lower limb movement recognition from multichannel sEMG signals

Walking is a complex task that requires consistent practice to master, and it involves the synchronisation between the lower limbs and the brain, making it challenging. While bipedal robots have been developed to mimic human walking, they must achieve an efficient gait due to structural differences and walking challenges. This study aims to produce a more human-like walk by analysing human lower extremity activities. To capture the bipedal robot locomotion learning process, an ensemble classifier based on deep learning is introduced to recognise human lower activities. A publicly available UC Irvine Machine Learning Repository (UCI) dataset on surface electromyography (sEMG) signal for the lower extremity of 11 fit participants and 11 participants with knee disorders for sitting while performing knee extension, walking, and standing while performing knee flexion is used. A hybrid ensemble of deep learning models comprising long short-term memory and convolution neural network is employed to classify activities, with reported average accuracies of 98.8%, 98.3%, and 99.3% for healthy subjects for sitting, standing and walking, respectively. Moreover, the ensemble model reported average accuracies of 98.2%, 98.1%, and 99.0% for individuals with knee pathology. Notably, this study holds promising significance, as it has yielded a considerable enhancement in performance as opposed to state-of-the-art work. The applications of this work are diverse and include improving postural stability in elderly subjects, aiding in the rehabilitation of patients recovering from stroke and trauma, generating walking trajectories for robots in complex environments, and reconstructing walking patterns in individuals with impairments.