@inproceedings{scholars6130,
          volume = {76},
            note = {cited By 9; Conference of IEEE International Symposium on Robotics and Intelligent Sensors, IEEE IRIS 2015 ; Conference Date: 18 October 2015 Through 20 October 2015; Conference Code:123218},
             doi = {10.1016/j.procs.2015.12.347},
           title = {Development of Electromyography Signal Signature for Forearm Muscle},
            year = {2015},
       publisher = {Elsevier B.V.},
         journal = {Procedia Computer Science},
           pages = {229--234},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962911161&doi=10.1016\%2fj.procs.2015.12.347&partnerID=40&md5=09c013052b372f8191de9bb16edfd5ce},
        keywords = {Activation analysis; Butterworth filters; Chebyshev filters; Electrodes; Filtration; Gait analysis; Intelligent control; Muscle; Needles; Patient rehabilitation; Research laboratories; Robotics; Smart sensors, Clinical settings; Electrical activities; Electromyography signals; Flexor carpi radialis; Forearm supinations; Muscle activation; Rehabilitation devices; Surface electromyography, Electromyography},
        abstract = {Electromyography (EMG) measures muscle response or electrical activity in response to a nerve's stimulation of the muscle. EMG is generally acquired through surface and needle or wire electrodes. The needle or wire electrodes are usually used by clinicians in a clinical setting. This paper concentrates on surface electromyography (sEMG) signal that is acquired in a research laboratory since sEMG is increasingly being recognized as the gold standard for the analysis of muscle activation. The sEMG can utilized for establishing signal signature for forearm muscles that becomes an important input in development of rehabilitative devices. This paper discusses the establishment of sEMG signal signature of female and male subjects for forearm muscles such as extensor carpi radialis, flexor carpi radialis, palmaris longus and pronator teres based on movements such as wrist extension and flexion, hand open and close, and forearm supination and pronation. This was achieved through the use of Butterworth Bessel, Elliptic and Chebyshev filters. The sEMG signal signature could be useful in the development of rehabilitation device of upper extremities.},
          author = {Elamvazuthi, I. and Zulkifli, Z. and Ali, Z. and Khan, M. K. A. A. and Parasuraman, S. and Balaji, M. and Chandrasekaran, M.},
            issn = {18770509}
}