Optimal Channel-set and Feature-set Assessment for Foot Movement Based EMG Pattern Recognition. Issue 15 (15th December 2021)

• Record Type: Journal Article
• Authors: Hooda, Neha; Kumar, Neelesh
• Is Part Of: Applied artificial intelligence. Volume 35:Issue 15(2021)
• Page Start: 1685
• Page End: 1707
• Publication Date: 2021-12-15
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2021.1990525
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: Electromyography (EMG) -based control is the most convenient and robust way to classify body movements for controlling prosthetic as well as orthotic devices. Its translation from lab-based approach to assistive devices demands a problem-centric and cost-effective solution. This paper demonstrates its utility for the classification of four foot movements, viz Plantar flexion, Dorsi flexion, Eversion and Inversion. For the experimental study, four superficial muscles (viz. Tibialis Anterior, Extensor Hallucis Longus, Gastrocnemius Medial and Fibularis Longus) were identified as electrode positioning locations for the EMG data acquisition. This work is aimed to minimize the number of electrode locations without significantly affecting the classification performance. Channel-set CH2, 4 corresponding to the combination of Hallucis Longus and Fibularis Longus muscles is found to be the most optimal. The maximum classification accuracy obtained for the given set with the selected feature-set has been (91.85 ± 3.57)%. The classification performance has been assessed on the basis of parameters such as the type of classifier, window length, data sampling and also the body mass index of the participants. The developed technique can be applied for control of ankle exoskeletons for healthy as well as person with certain disabilities.