Electromyography
Electromyography is useful for those interested in the study of biological signals and their processing, identifying the characteristics of these signals is valuable for the design of prosthetic and robotic systems or in rehabilitation for patients with pathologies of the lower limb. In this case, in particular, the EMG signals of the biceps femoris generated during the gait cycle are intended to characterize the muscle activation signals during walking. For the development of this database, one hundred users were invited to participate.
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We present a sEMG signal database corresponding to the Indian population named “ElectroMyography Analysis of Human Activities - DataBase -2 (EMAHA-DB2).” This data set consists of two different weight training activities which involve isotonic and isometric contractions. Weight training activities are effective for improving muscle strength, overall health, and regaining limb functionality for people undergoing rehabilitation post stroke-related episodes. The EMG signals acquired during weight training can be used for muscle recruitment analysis.
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The signals of a recording belonging to the Frontside task are shown, the first two corresponding to the electromyography sensors while the others correspond to the infrared and red LED readings of the pulse oximetry sensor.
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The Research Paper "Detection of Bicep Form Using Myoware and Machine Learning" based on the novel dataset has been recently accepted in September 2022 and is being published in SCOPUS Indexed SPRINGER Book Series “Lecture Notes in Networks and Systems”
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— The Ionic Polymer Metal Composite (IPMC) actuator is a group of Electro-Active Polymer (EAP) which bends in response to a relatively low electrical voltage because of the motion of cations in the polymer network. IPMC has a wide range of applications in robotics, biomedical devices and artificial muscles. This paper presents a fuzzy logic approach to the electromyography (EMG) pattern recognition for an IPMC actuating system with the EMG signal.
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