Analysis of High-Density Surface Electromyogram (HD-sEMG) signal for thumb posture classification from extrinsic forearm muscles. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Suhaimi, Muhammad Mukhlis; Ghazali, Aimi Shazwani; Jazlan, Ahmad; Sidek, Naim
• Editors: Jin, Zhongmin
• Is Part Of: Cogent engineering. Volume 9:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: thumb posture -- high density surface electromyogram (HD-semg) -- forearm anterior and posterior -- maximum voluntary contraction (MVC); Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2022.2055445
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: For amputees, the development of cybernetic hands that closely resembles the functions of real hands is essential for comfortability and functionality purposes. Controlled by intrinsic and extrinsic muscles, human thumb plays a major role in differentiating hand gestures. For those who have lost their intrinsic hand muscles, any information about muscle activities that can be obtained from the extrinsic muscles will be essential to control the thumb. Focusing on transradial amputees, this research investigates the relationship between extrinsic muscles to characterize the actual thumb posture. A High-Density Surface Electromyogram (known as HD-sEMG) recording device and a portable thumb force measurement system were used to collect forearm Electromyogram (EMG) signals from a total of 17 subjects. For the flexion motion, the subjects were asked to repetitively place their thumbs at rest before exerting 30% of their individual Maximum Voluntary Contraction (MVC) on a load cell by following a designated trajectory presented on a designated Graphical User Interface (GUI). The trajectory was set to four different postures, namely, zero-degree, thirty-degree, sixty-degree, and ninety-degrees. Feature extraction was then performed by extracting the Absolute Rectified Value (ARV) and Root Mean Square (RMS) values of the forearm HD-sEMG signals before being classified using Lazy.IBK. The results revealed that the ARV features, which were extracted from HD-sEMG from both posterior and anterior hand sides successfully achieved the highest correctly classified percentage of 99.48%. The findings of this study are significant for the development of a dedicated model-based control framework for prosthetic hand's development to be used by transradial amputees in the future.