Abstract
The characteristics to consider during the development of myoelectric prostheses are the cultural, socio-economic and climatic conditions in which they are used. The weather condition affects in particular the recording of muscular activation for the prosthesis control. The aim of this study was the development of an easy to use myoelectric control system that can be used for the myoelectric control of opening and closing of the hand through electrodes that are not inside the prosthesis socket. Two time-domain features and Channel Selection were used to identify the specific channels corresponding to the biceps and triceps muscles. Eleven healthy subjects were tested. Channel selection was correct in 80% of all trials ensuring that the myoelectric sensor system can be used in any position outside of the prosthesis socket. This study provides an essential step towards the development of a mechanism for grip control in a myoelectric-controlled prosthetic hand.
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References
B. Peerdeman et al., Myoelectric forearm prostheses: State of the art from a user-centered perspective. J. Rehabil. Res. Dev. 48(6), 719–738 (2011)
E. Junker, C. Disselhorst-Klug, S. Becker, Design of a portable 32-channel sEMG-IMU sensor system for the assessment of upper limbs’ movements in daily living, in XXVII Congress of the International Society of Biomechanics (ISB2019), 2019, p. 2178
D.L. Menkes, R. Pierce, Needle EMG muscle identification: A systematic approach to needle EMG examination. Clin. Neurophysiol. Pract. 4, 199–211 (2019)
T. Instruments, MSP430x55/MSP430x6xx Family User’s Guide, (SLAU208Q) (2018)
D. Stegeman, H. Hermens, Standards for Surface Electromyography: The European Project “Surface EMG for Non-invasive Assessment of Muscles (SENIAM)” (1999)
U. Côté-Allard et al., Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Trans. Neural Syst. Rehabil. Eng. (2018)
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Romero Avila, E., Junker, E., Disselhorst-Klug, C. (2022). Control of Servomotor Rotation in a Myoelectric Upper-Limb Prosthesis Using a 16-Channel sEMG Sensor System. In: Moreno, J.C., Masood, J., Schneider, U., Maufroy, C., Pons, J.L. (eds) Wearable Robotics: Challenges and Trends. WeRob 2020. Biosystems & Biorobotics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-030-69547-7_14
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DOI: https://doi.org/10.1007/978-3-030-69547-7_14
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