Remote Driving Control of a Mobile Vehicle using Electromyography (EMG) Signals Captured from Gesture-based Interactions

Electromyography (EMG) signals are widely used in human-computer interaction (HCI) due to their high signal-to-noise ratio and ability to provide early information about muscle contractions. In this study, we present a mobile vehicle remotely controlled by EMG signals captured from hand-arm gestures. Specifically, we investigated gripping and wrist gestures. A single EMG sensor placed on the right forearm captured signals during gripping gestures with varying grip forces. Two EMG sensors placed on the left forearm recorded signals during wrist gestures with varying wrist folding angles. The EMG signals from the gripping gestures were evaluated based on grip force. The root mean square (RMS) value of these signals was used to control the driving speed and direction of the mobile vehicle. Similarly, the EMG signals from wrist gestures at different folding angles were characterized and used for differential speed control of the vehicle's driving wheels. A series of reliable demonstrations showcased the remote control of the mobile vehicle prototype using grip force and wrist gestures. Our approach is simple, effective, and reliable, achieving omnidirectional mobile vehicle control with both gesture-to-motion and sensor-to-motion ratios of 1.67, thereby contributing to appropriate technology for mobile and intelligent vehicles and related engineering and scientific applications, with a cost-effective design and simple configuration and operation.