Human-Centered Design: A Haptic Robotic-Based Leader-Follower Driving Training System to Improve Driving Skills and Enhance Safety

Abstract

This study presents the design and implementation of a human-centered wearable robotic glove for driving training using a lead-follower control methodology and haptic stimuli. The glove captures and analyses biometric signals, including piezoelectric sensors on fingers, Electromyography (EMG) for muscle strength, and inertial measurement unit (IMU) for hand position, to monitor hand and forearm behaviour during operation. Real-time motor controllers generate guidance cues for leader-follower gloves based on feedback from sensors, which are wirelessly communicated through the ESP-Now WIFI protocol. An experimental test was conducted to evaluate the effectiveness of the haptic cues on hand pressure and motion. 2-way ANOVA statistical analysis is used to measure the significance of the relevance between haptic cues and driving training outcomes. While haptic cues did not affect hand trajectory, they have statistical significance to driving outcomes. The proposed system has the potential to enhance driving safety training and improve performance.