Toward a Minimal sEMG Setup for Knee and Ankle Kinematic Estimation during Gait

Modern rehabilitation and assistive devices require the use of smart interfaces able to capture the subject's intent of motion and translate such intent to specific control strategies. The use of surface electromyography (sEMG) signals, in combination with data-driven models, constitutes a viable framework to solve the aforementioned problem. Although literature highlighted the tendency toward a multiple sensors approach, a minimal set-up may reduce costs and complexity of myoelectric interfaces. In this study, gastrocnemius lateralis (GAL) and tibialis anterior (TA) sEMG signals were used in order to investigate their single and combined role in the flexion-extension angles estimation of ankle and knee during gait. Least-square support vector machine (LS-SVM) with linear, polynomial, and radial basis function (RBF) kernel was employed to estimate the most suitable function that maps the myoelectric information from single muscle and from the combination of both, in lower limbs joint kinematics. LS-SVM with RBF outperformed the other kernels in the ankle and knee kinematics estimation for all the 6 subjects examined. Moreover, when using RBF with the only GAL data, the median root mean square error (RMSE) values were above 5 degrees for ankle and 8 degrees for knee angles whereas the combined information from GAL and TA showed slightly better results. Outcomes support a minimal electrodes set-up for the development of lower limb myoelectric interfaces for kinematic estimation.