Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments
Joseph K. Muguro,
Pringgo Widyo Laksono,
Wahyu Rahmaniar,
Waweru Njeri,
Yuta Sasatake,
Muhammad Syaiful Amri bin Suhaimi,
Kojiro Matsushita,
Minoru Sasaki,
Maciej Sulowicz,
Wahyu Caesarendra
Affiliations
Joseph K. Muguro
Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Pringgo Widyo Laksono
Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Wahyu Rahmaniar
Department of Electrical Engineering, National Central University, Zhongli, Taoyuan 32001, Taiwan
Waweru Njeri
School of Engineering, Dedan Kimanthi University of Technology, Nyeri 657-10100, Kenya
Yuta Sasatake
Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Muhammad Syaiful Amri bin Suhaimi
Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Kojiro Matsushita
Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Minoru Sasaki
Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Maciej Sulowicz
Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, Poland
Wahyu Caesarendra
Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
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.
