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

Angga Rahagiyanto; Gandu Eko Julianto Suyoso; Veronika Vestine; Abdullah Iskandar

doi:10.47134/ijhis.v1i1.9

Authors

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

DOI:

https://doi.org/10.47134/ijhis.v1i1.9

Keywords:

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

Abstract

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.

References

M. Sathiyanarayanan and S. Rajan, “MYO Armband for physiotherapy healthcare: A case study using gesture recognition application,” 2016 8th Int. Conf. Commun. Syst. Networks, COMSNETS 2016, pp. 1–6, 2016, doi: 10.1109/COMSNETS.2016.7439933.

D. Iakovakis and L. Hadjileontiadis, “Standing hypotension prediction based on smartwatch heart rate variability data: A novel approach,” Proc. 18th Int. Conf. Human-Computer Interact. with Mob. Devices Serv. Adjunct, MobileHCI 2016, pp. 1109–1112, 2016, doi: 10.1145/2957265.2970370.

A. Rahagiyanto, A. Basuki, R. Sigit, A. Anwar, and M. Zikky, “Hand Gesture Classification for Sign Language Using Artificial Neural Network,” ICSEC 2017 - 21st Int. Comput. Sci. Eng. Conf. 2017, Proceeding, vol. 6, no. Mi, pp. 205–209, 2018, doi: 10.1109/ICSEC.2017.8443898.

G. E. J. Suyoso, V. Vestine, and A. Rahagiyanto, “A Review of Muscle Tone Alteration on Post Stroke Patient,” Proc. 2nd Int. Conf. Soc. Sci. Humanit. Public Heal. (icosh. 2021), vol. 645, no. Icoship 2021, pp. 249–253, 2022, doi: 10.2991/assehr.k.220207.042.

X. Zhang, X. Tang, X. Zhu, X. Gao, X. Chen, and X. Chen, “A regression-based framework for quantitative assessment of muscle spasticity using combined emg and inertial data from wearable sensors,” Front. Neurosci., vol. 13, no. MAY, 2019, doi: 10.3389/fnins.2019.00398.

D. M. Nareva, “Hemiparesis: Kelemahan Otot pada Salah Satu Sisi Tubuh,” ALODOKTER, 2021. https://www.alodokter.com/hemiparesis-kelemahan-otot-pada-salah-satu-sisi-tubuh (accessed Apr. 08, 2021).

A. F. Ruiz-Olaya, M. Callejas-Cuervo, and A. Milena Perez, “EMG-based Pattern Recognition with Kinematics Information for Hand Gesture Recognition,” 20th Symp. Signal Process. Images Comput. Vis. Conf. Proc. , Bogotá, Colomb.

R. Suhaimi et al., “Analysis of EMG-based muscles activity for stroke rehabilitation,” 2014 2nd Int. Conf. Electron. Des. ICED 2014, pp. 167–170, 2014, doi: 10.1109/ICED.2014.7015792.

C. S. Klein, S. Li, X. Hu, and X. Li, “Editorial: Electromyography (EMG) Techniques for the Assessment and Rehabilitation of Motor Impairment Following Stroke,” Front. Neurol., vol. 9, 2018, doi: 10.3389/fneur.2018.01122.

T. Bintang Dewantoro, R. Sigit, H. Yuniarti, Y. D. Prawitri, F. A. Pamudyaningrum, and M. Ilham Awal, “Implementation of Myo Armband on Mobile Application for Post-stroke Patient Hand Rehabilitation,” Proceeding Electr. Eng. Comput. Sci. Informatics, vol. 5, no. 5, pp. 455–450, 2018, doi: 10.11591/eecsi.v5i5.1585.

S. Sharma, V. Steuber, and F. Amirabdollahian, “Assessing the Impact of Muscular Fatigue on Myoelectric Signals Using Myo Armband,” ACHI 2019 Twelft Int. Conf. Adv. Comput. Interact., no. c, pp. 159–164, 2019.

C. K. Piyus, V. A. Cherian, and S. Nageswaran, “EMG based FES for post-stroke rehabilitation,” IOP Conf. Ser. Mater. Sci. Eng., vol. 263, no. 5, 2017, doi: 10.1088/1757-899X/263/5/052025.

M. Kumar, D. P. Garg, and R. A. Zachery, “A method for judicious fusion of inconsistent multiple sensor data,” IEEE Sens. J., vol. 7, no. 5, pp. 723–733, 2007, doi: 10.1109/JSEN.2007.894905.

Y. Makino, S. Ogawa, and H. Shinoda, “EMG sensor integration based on two dimensional communication,” Proc. INSS 2008 - 5th Int. Conf. Networked Sens. Syst., p. 257, 2008, doi: 10.1109/INSS.2008.4610851.

J. Flusser, T. Suk, and B. Zitová, Moments and Moment Invariants in Pattern Recognition. 2009.