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.
