Development of neck surface electromyography gaming control interface for application in tetraplegic patients’ entertainment

Muguro, Joseph; Sasaki, Minoru; Matsushita, Kojiro; Njeri, Waweru; Laksono, Pringgo Widyo; Suhaimi, Muhammad Syaiful Amri bin

doi:10.1063/5.0000500

Cited by 2 publications

(3 citation statements)

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“…Signals 2021, 2 FOR PEER REVIEW 4 1 DoF (e.g., in left and right movement) [26,27], 2 DoF (e.g., in left, right, top and down movement) [14], or 3 or more DoF (input combination). This paper is a continuation of a previously published article [44], in which we explored the implementation of control interfaces in 1D. This work reports on further developments being made, notably on machine learning (ML) generalizations from trained models and multiple-dimensional controls or 2D controls, by incorporating additional EMG sensors.…”

Section: Setup and Data Acquisitionmentioning

confidence: 95%

“…The advantage of the proposed model is to point out the inverse relationship that exists between left and right SCM with head rotation. Further details can be found in [44]. Changing the position of the player is carried out by…”

Section: Equation Estimationmentioning

confidence: 99%

“…This paper is a continuation of a previously published article [44], in which we explored the implementation of control interfaces in 1D. This work reports on further developments being made, notably on machine learning (ML) generalizations from trained models and multiple-dimensional controls or 2D controls, by incorporating additional EMG sensors.…”

Section: Introductionmentioning

confidence: 95%

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

Muguro

Laksono

Rahmaniar

et al. 2021

Signals

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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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