A Comparative and Evolutionary Study of Hands-Free Mouse Assistive Technologies : insights from head, eye and mouth-based interaction systems

Abstract

For individuals with upper limb disabilities, neuromuscular conditions, or high-level spinal cord injuries, the inability to use a standard computer mouse is a barrier to employment, education, and independence. Hands-free mouse assistive technologies address this through three commercially and clinically established paradigms: head-tracking, eye-tracking, and mouth and lip control. Despite their significance, no structured study has evaluated all three paradigms comparatively, situated them within their technological history, and incorporated the perspectives of the industry experts who develop and deploy them. This thesis addresses that gap by analyzing three devices, the Quha Zono X (head-tracking), the Tobii Eye Tracker 5 (eye-tracking), and the IntegraMouse Plus (mouth and lip control), through a mixed-method approach combining first-hand device evaluation, a structured questionnaire with six industry experts, and a systematic review of the published literature and manufacturer documentation. The IntegraMouse Plus, to which direct physical access was not available, is assessed through literature and industry expert accounts alone.

The personal evaluation found the Quha Zono X to be a capable cross-platform device (overall usability rated 5/5) whose principal cost is neck and shoulder fatigue, while the consumer Tobii Eye Tracker 5 proved to be a gaming-oriented peripheral not optimized for everyday computer access (overall usability rated 1/5 for productivity tasks), a finding corroborated by Tobii's own product positioning. The industry expert survey found near-unanimous agreement that device selection is currently driven by professional experience and trial rather than published evidence, directly corroborating the research gap, and surfaced the clinically significant view that eye-tracking should often be introduced early in progressive conditions to avoid requiring users to relearn an access method later. Across all three strands, no single paradigm offered universal superiority, each suited distinct user populations and diagnostic trajectories.

The combined findings provide AT specialists and industry experts with an evidence-based basis for device selection guidance, and identify what the current industry trajectory, toward AI integration, multimodal input, and mainstream platform convergence, means for the future relevance of each paradigm.