Abstract

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.

Highlights

  • We propose to utilize the neck EMG of the left and right sternocleidomastoid (SCM) muscles and the facial EMG of the Masseter muscle to develop a HumanMachine Interface (HMI) for game control

  • We successfully developed a control interface using SCM and Masseter EMG and tested its performance as a game input methodology

  • We modeled the control interface using an equation and machine learning approaches

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Summary

Introduction

Video games have evolved into a more sophisticated ecosystem due to the advancement of technology in the 21st century. The same integration into society is on an upward uptrend. Edutainment, development games, and children’s games are some of the integration processes that exist to date [1,2,3]. Games have proven to be the central point of engagement, socialization, and connectivity in students’ lives [3,4]. Despite these developments, people with disabilities have not been adequately served by the industry. Accessibility of video games is especially a challenge for Signals 2021, 2, 834–851.

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