Driving ability in older adults is essential for independent mobility and social participation, yet declines under high cognitive load or distraction often lead to visual attention failures such as "look-but-fail-to-see," increasing crash risk. Older adults and individuals with mild cognitive impairment (MCI) show impairments in visual attention, executive control, and visuomotor integration, which are not adequately captured by conventional assessments. Virtual reality (VR) integrated with eye-tracking and upper-limb motion analysis enables ecologically valid simulation of driving scenarios and precise quantification of visuomotor behavior. However, current studies are limited by single-scenario designs, unimodal AI models, and insufficient integration of action-related data.
This study proposes a multi-phase framework: Year 1 develops an eye-movement-based AI model for MCI identification; Year 2 integrates multimodal data in VR driving tasks; and Year 3 establishes an explainable AI system with longitudinal validation. The study aims to advance cognitive assessment and develop a digital tool for early MCI detection and driving risk prediction.
(1) a score of 23 or higher on the Montreal Cognitive Assessment; (2) a Clinical Dementia Rating score of 0 for cognitively healthy participants or 0.5 for participants with mild cognitive impairment (MCI); (3) healthy young adults aged between 30 and 39 years, and cognitively healthy older adults and participants with MCI aged between 65 and 85 years; (4) right-hand dominance; and (5) adequate visual function to complete VR and eye-tracking tasks, defined as corrected binocular visual acuity of at least 0.5 without severe visual field deficits.
(1) the presence or history of major psychiatric disorders or central nervous system diseases; (2) significant ocular diseases, such as untreated cataracts, active retinal diseases, moderate-to-severe or poorly controlled glaucoma, or marked visual field deficits beyond a specified level; (3) epilepsy; and (4) severe dizziness or VR-induced motion sickness.
The study population will include healthy young adults aged 30-39 years, as well as cognitively healthy older adults and individuals with mild cognitive impairment (MCI) aged 65-85 years.
Case-control
Prospective
Not listed
Time to First Fixation (TFF) within the Area of Interest
Time to First Fixation (TFF) is defined as the time interval from event onset (t₀) to the participant's first fixation within the predefined Area of Interest (AOI). A shorter TFF indicates faster attentional orienting. Units of Measure: milliseconds (ms) for each predefined AOI/event condition.
Number of Fixations on the Area of Interest
Number of Fixations is defined as the total count of fixations within the predefined Area of Interest (AOI) during task performance. A higher number of fixations indicates greater visual search activity toward target stimuli. Unit of Measure: count.
Total Fixation Duration (TFD) on the Area of Interest
Total Fixation Duration (TFD) is defined as the cumulative fixation time within the predefined Area of Interest (AOI) during the task. Longer fixation duration indicates greater attentional engagement with the target stimulus. Unit of measure: Milliseconds.
Steering Reaction Time
Steering Reaction Time is defined as the interval from event onset (t₀) to the initial steering wheel deviation of ≥5°. This measure reflects motor initiation speed during driving tasks. Steering Reaction Time will be calculated separately for each driving scenario or curve type. Unit of Measure: Milliseconds (ms).
Steering Reversal Rate (SRR)
Steering Reversal Rate (SRR) is defined as the number of steering direction changes per minute during curve driving, using a steering-wheel angle threshold of 0.5°. Higher SRR values indicate increased steering corrections and may reflect reduced motor control stability and increased driving workload. Unit of Measure: Reversals/minute.
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