A Prototype of MyoWare (Electromyography Muscle Sensor) for Measuring People’s Muscle Strengths


  • Angga Rahagiyanto Politeknik Negeri Jember
  • Gandu Eko Julianto Suyoso Politeknik Negeri Jember
  • Veronika Vestine Politeknik Negeri Jember
  • Abdullah Iskandar Waseda University




HCI, Hand Gesture, MyoWare, Moment Invariant, Min-Max


Human-Computer Interaction (HCI) becomes a solution to help humans connect with computers. Research and tools related to HCI have been developed by many researchers. HCI is able to help humans connect between humans and computers and humans with humans at a considerable distance. One of HCI model is applied to the MyoWare tool that can capture hand muscle movements using an electromyograph (EMG) sensor. This article describes how to assemble and identify the raw data generated from the MyoWare tool. Using MyoWare on the hand could produce EMG data output. MyoWare only used the EMG sensor and generated data in the form of Envelope EMG and Raw EMG which differed in scale and size. This required a extraction features process to make the data uniform. This study uses the Moment Invariant method to extract features and min-max to normalize each data generated on the MyoWare sensor. Testing was done by doing simple hand movements. The test results showed that the differences in gestures were recognized well even though they were performed in different positions.


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How to Cite

Rahagiyanto, A., Suyoso, G. E. J., Vestine, V., & Iskandar, A. (2023). A Prototype of MyoWare (Electromyography Muscle Sensor) for Measuring People’s Muscle Strengths . International Journal of Health and Information System, 1(1), 19–26. https://doi.org/10.47134/ijhis.v1i1.9