Enhancing Orientation and Mobility Assessments: Integrating Visual and Auditory Factors Using Artificial Intelligence...

Area of Science:

• Rehabilitation research
• Human-computer interaction
• Sensory neuroscience

Background:

• Traditional orientation and mobility (O&M) assessments for visual impairments often lack sensitivity to environmental and psychological factors.
• Existing outcome measures may not fully capture the complexities of O&M performance in diverse conditions.

Purpose of the Study:

• To compare O&M performance and arousal levels under simulated visual and auditory conditions.
• To evaluate the utility of artificial intelligence (AI) tools for body tracking in O&M assessments.
• To investigate factors influencing O&M performance beyond traditional metrics.

Main Methods:

• Video-recorded O&M performance of 32 volunteers in a controlled StreetLab environment.
• Utilized AI tools for body tracking to measure path length and deviations.
• Recorded physiological parameters and subjective experiences alongside standard metrics like speed and collisions.

Main Results:

• AI-based analysis detected performance differences not captured by common O&M outcome measures.
• Both visual and auditory conditions significantly influenced O&M performance.
• Increased background sound levels were correlated with heightened arousal during the task.

Conclusions:

• AI-powered analysis provides superior insights into O&M performance compared to traditional methods.
• Comprehensive O&M evaluations for visual impairments must consider visual, auditory, and arousal factors.
• AI tools can significantly improve the assessment and care for individuals with visual impairments.