A comparative study of motion detection with FMG and sEMG methods for assistive applications. (November 2020)

• Record Type: Journal Article
• Authors: Islam, Muhammad Raza Ul; Waris, Asim; Kamavuako, Ernest Nlandu; Bai, Shaoping
• Is Part Of: Journal of rehabilitation and assistive technologies engineering. Volume 7(2020)
• Publication Date: 2020-11
• Subjects: Day-to-day performance comparison -- forcemyography -- human-machine interfaces -- neural network -- surface-electromyography -- assistive exoskeletons; Rehabilitation technology -- Periodicals; 617.03
• DOI: 10.1177/2055668320938588
• Languages: English
• ISSNs: 2055-6683

Abstract

Introduction: While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods: Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results: The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions: Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.