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Article

Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments

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Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
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School of Engineering, Dedan Kimanthi University of Technology, Nyeri 657-10100, Kenya
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Industrial Engineering, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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Department of Electrical Engineering, National Central University, Zhongli, Taoyuan 32001, Taiwan
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Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, Poland
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Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
*
Authors to whom correspondence should be addressed.
Academic Editors: Tom Diethe and Niall Twomey
Signals 2021, 2(4), 834-851; https://doi.org/10.3390/signals2040048
Received: 30 June 2021 / Revised: 15 October 2021 / Accepted: 18 October 2021 / Published: 12 November 2021
(This article belongs to the Special Issue Machine Learning and Signal Processing)
In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities. View Full-Text
Keywords: disability and functional impairment; game control; human-machine interface; machine learning; sEMG disability and functional impairment; game control; human-machine interface; machine learning; sEMG
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MDPI and ACS Style

Muguro, J.K.; Laksono, P.W.; Rahmaniar, W.; Njeri, W.; Sasatake, Y.; Suhaimi, M.S.A.b.; Matsushita, K.; Sasaki, M.; Sulowicz, M.; Caesarendra, W. Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments. Signals 2021, 2, 834-851. https://doi.org/10.3390/signals2040048

AMA Style

Muguro JK, Laksono PW, Rahmaniar W, Njeri W, Sasatake Y, Suhaimi MSAb, Matsushita K, Sasaki M, Sulowicz M, Caesarendra W. Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments. Signals. 2021; 2(4):834-851. https://doi.org/10.3390/signals2040048

Chicago/Turabian Style

Muguro, Joseph K., Pringgo W. Laksono, Wahyu Rahmaniar, Waweru Njeri, Yuta Sasatake, Muhammad S.A.b. Suhaimi, Kojiro Matsushita, Minoru Sasaki, Maciej Sulowicz, and Wahyu Caesarendra. 2021. "Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments" Signals 2, no. 4: 834-851. https://doi.org/10.3390/signals2040048

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