Examination of Eye-Tracking, Head-Gaze, and Controller-Based Ray-casting in TMT-VR: Performance and Usability Across Adulthood
🤖 AI Summary
Selecting optimal input modalities for virtual reality (VR)–based neurocognitive assessments—particularly trail-making tests (TMT-VR)—remains challenging due to trade-offs among speed, accuracy, fatigue, and age-related usability differences.
Method: This study systematically compared eye tracking, head-gaze, and handheld controller inputs in TMT-VR across young to older adults (n = 60), using mixed-design ANOVA and standardized usability metrics (SUS, UEQ-S).
Contribution/Results: Head-gaze yielded the fastest completion times and lowest error rates—especially under high cognitive load in alternating-task conditions—while eye tracking achieved highest spatial precision but suffered from increased fatigue. Handheld controllers performed consistently worse across all metrics. Critically, a significant age–modality interaction emerged: younger adults exhibited pronounced advantages with head-gaze and eye tracking, whereas older adults showed greater difficulty with eye tracking. These findings indicate the necessity of age-stratified normative standards and inform evidence-based, age-inclusive input design for VR neurocognitive assessment tools.
Application Category
📝 Abstract
Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers-young (19-29 y) and middle-aged (27-56 y)-completed a VR Trail-Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 x 2 x 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific-stratified norms.
Problem
Research questions and friction points this paper is trying to address.
Compare VR input modes for neuropsychological testing performance
Evaluate age-related differences in VR testing usability
Determine optimal gaze-based vs manual pointing methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Eye-tracking enhances spatial accuracy
Head-gaze improves speed and error-avoidance
Controller-based ray-casting lags in performance
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