Evaluating a Refined Augmented Reality Eye-Gaze and Voice Control System for Electric Wheelchairs: A User Study

Self-determined mobility is fundamental for social participation, yet conventional electric wheelchair controls present significant usability challenges for individuals with severe motor impairments like tetraplegia or ALS. While alternative specialized controls exist, they may introduce issues such as fatigue or reduced intuitiveness. This paper reports on the user evaluation of a refined assistive control system, previously introduced as work-in-progress, designed to address these limitations. The system employs a combination of eye-gaze tracking and voice commands, mediated through a Magic Leap 2 Augmented Reality (AR) headset, to operate both a wheelchair simulator and a real electric wheelchair via a Raspberry Pi interface. Initial prototypes highlighted the need for enhancements focused on user experience, control stability, and intentionality, particularly for real-world deployment. The evaluated system incorporates several key refinements: (1) A robust activation mechanism requiring sustained gaze within the interface boundaries for a defined duration to enable control, complemented by a configurable grace period upon gaze exit to prevent unintended deactivation during brief glances away. (2) Advanced stability features, including Kalman filtering or iterative Slerp smoothing of gaze rotation data to mitigate input jitter, alongside head rotation handling that detects high angular velocity to trigger a brief, timed recentering of the joystick input upon cessation, preventing uncontrolled continuation of turns. (3) Fine-tuned control mapping, utilizing non-linear response curves, minimal dead zones, sensitivity scaling, and output signal rate limiting to ensure smooth command delivery, provide fine control near the center, and prevent overly sensitive physical responses. (4) Raycast-based interaction logic for axis-snapping guide cubes and focusable object detection, replacing previous methods.The primary objective of this study was to comprehensively evaluate the usability, effectiveness, perceived workload, and user acceptance of this refined control system. The evaluation was conducted with individuals with spinal cord injuries at the BG Klinikum Hamburg. We aimed to assess the system's potential as a viable supplement or alternative to existing specialized wheelchair controls.A two-phase methodology was employed: participants first engaged with the system in a VR wheelchair simulator for familiarization and baseline assessment, followed by operating a real Ottobock Juvo B5 wheelchair using the AR interface in a controlled clinical environment. Tasks included fundamental maneuvers (activation/deactivation, forward/backward driving, turning in place), navigation along a simple marked path, and precision approach tasks. Data collection utilized a mixed-methods approach, combining objective performance metrics (e.g., task time, errors), standardized subjective questionnaires (System Usability Scale - SUS; NASA Task Load Index - NASA-TLX), and qualitative feedback via semi-structured interviews and direct observation, adhering to approved ethical guidelines.This paper presents the detailed findings from these user tests, focusing on the performance differences between VR and AR conditions, the perceived usability and workload associated with the system, the effectiveness of the specific activation and stability refinements, and overall user acceptance. The results provide empirical insights into the practical application of AR-based eye-gaze and voice control for wheelchairs, identifying strengths, limitations, and crucial areas for future development to enhance autonomous mobility for individuals with severe motor impairments.