Interpretations of Wrist/Grip Operations From SEMG Signals at Different Locations on Arm

• Published in: IEEE transactions on biomedical circuits and systems Vol. 4; no. 2; pp. 101 - 111
• Main Authors: Ryait, H.S., Arora, A.S., Agarwal, R.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data mining; Differential amplifiers; Elbow; Fourier transforms; Frequency domain analysis; Hardware; Muscles; Parameter estimation; Principal component analysis; Principal components analysis; SEMG interpretation; Signal analysis; Studies; Surface electromyogram (SEMG); Wrist; wrist/grip operations
• ISSN: 1932-4545; 1940-9990
• DOI: 10.1109/TBCAS.2009.2037604

Surface electromyogram (SEMG) is a common method of measurement of muscle activity. It is noninvasive and is measured with minimal risk to the subject. The analysis of SEMG signal depends on a number of factors, such as amplitude as well as time- and frequency-domain properties. In the present investigation, the study of SEMG signals at different below elbow muscles for four operations of the hand wrist/grip-like opening (op)/closing (cl)/down (d)/up (u) was carried out. Myoelectric signals were extracted by using a single-channel SEMG amplifier consisting of a differential amplifier, noninverting amplifier, and interface module. Matlab softscope was used to acquire the SEMG signal from the hardware. After acquiring the data from six selected locations, interpretations were made for the estimation of parameters of the SEMG using the Matlab-filter algorithm and the fast Fourier transform technique. An interpretation of wrist/grip operations using principal component analysis (PCA) was carried out. PCA was used to identify the best SEMG signal capturing system out of two-channel, three-channel, and four-channel systems. Two acupressure points (on wrist) were also selected for the analysis with other points on the arm. SEMG signal's study at different locations, including pressure points, will be a very helpful tool for the researchers in understanding the behavior of SEMG for the development of the prosthetic hand.