Статья

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

Joseph MuguroDepartment of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanPringgo Widyo LaksonoDepartment of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanWahyu RahmaniarDepartment of Electrical Engineering, National Central University, Zhongli, Taoyuan 32001, TaiwanWaweru NjeriSchool of Engineering, Dedan Kimanthi University of Technology, Nyeri 657-10100, KenyaYuta SasatakeDepartment of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanMuhammad Syaiful Amri bin SuhaimiDepartment of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanKojiro MatsushitaDepartment of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanMinoru SasakiDepartment of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanMaciej SułowiczDepartment of Electrical Engineering, Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, PolandWahyu CaesarendraFaculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei

2021en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.3390/signals2040048

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar