Abstract
Muscle rehabilitation is vitally important for patients suffering from muscle disorders, stroke, sports injuries, or atrophy due to serious illness. Physical therapy and other methods are typically used for the rehabilitation, but these methods lack a quantitative means for assessing the muscle performance as it responds to the therapy, which makes it difficult to optimize the procedures. To address this need, a noninvasive muscle activity (NMA) diagnostic is proposed in which laser light is sent through the skin into the muscle. Changes in the characteristics of the light passing through the muscle caused by physiologic tetanus are detected, thereby providing a direct, real-time measurement of the onset and degree of muscle contraction. Besides providing valuable data on the muscle performance during therapy, this diagnostic could provide feedback to a computer, which could use this to electrically stimulate the muscle tetanus in a controlled manner. This opens up the possibility of using this system to control muscle movement in paralyzed individuals. Using artificial intelligence, it is conceivable that the computer can learn the proper sequence for contracting multiple muscles in, say, the leg to enable walking. Other applications include using the diagnostic as part of a bio-feedback system where, for example, it can assist in muscle relaxation therapy, or for athletes it can help ensure balanced muscle strength to avoid injuries. By applying microelectronics and micro-photonics techniques, the NMA diagnostic could be designed as a compact, portable, and disposable adhesive unit attached to the skin.
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The authors would like to thank University of Washington - Bothell STEM division for the travel grant that allowed L. K. Lam to attend and present this paper at the HCII2020 conference.
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Lam, L.K., Kimura, W.D. (2020). Computer-Interfacing with Noninvasive Muscle Activity Diagnostic. In: Duffy, V. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health. HCII 2020. Lecture Notes in Computer Science(), vol 12198. Springer, Cham. https://doi.org/10.1007/978-3-030-49904-4_22
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