Abstract-To control upper-limb exoskeletons and prostheses, surface electromyogram (sEMG) is widely used for estimation of joint angles. However, the variations in the load carried by the user can substantially change the recorded sEMG and consequently degrade the accuracy of joint angle estimation. In this paper, we aim to deal with this problem by training classification models using a pool of sEMG data recorded from all different loads. The classification models are trained as either subject-specific or subject-independent, and their results are compared with the performance of classification models that have information about the carried load. To evaluate the proposed system, the sEMG signals are recorded during elbow flexion and extension from three participants at four different loads (i.e. 1, 2, 4 and 6 Kg) and six different angles (i.e. 0, 30 , 60, 90, 120, 150 degrees). The results show while the loads were assumed unknown and the applied training data was relatively small, the proposed joint angle estimation model performed significantly above the chance level in both the subject-specific and subject-independent models. However, transferring from known to unknown load in the subject-specific classifiers leads to 20% to 32% loss in the average accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.