Enhancing Collision Prediction in Older Adults
Introduction
With age, the ability to anticipate collisions with moving objects declines, partly due to decreased sensitivity to cues of object expansion. This study investigated whether perceptual training focused on object expansion can improve collision prediction more effectively than mere repetition of the same collision task. It also tested if this training enhances prediction accuracy in a realistic virtual road-crossing environment.
Methods
Twenty older adults (average age 71.35 ± 6.04 years; 11 females) took part in the study. All tasks were performed in virtual reality (VR) from a first-person viewpoint. Evaluations before and after training included three tasks:
- An interception task assessing collision prediction ability
- A target-approach detection task measuring sensitivity to object expansion
- A road-crossing task simulating real-world scenarios
Participants were randomly divided into two groups for training:
- TE-group: Time-to-contact (TTC) estimation training
- IC-group: Interception task training
"The ability to predict collisions with moving objects declines with age, partly due to reduced sensitivity to object expansion cues."
Both the perceptual training targeting object expansion and repeated practice showed potential for improving collision prediction, with implications for safer mobility in older adults.
Author's summary: Perceptual training emphasizing object expansion cues in virtual reality can significantly enhance older adults' collision prediction skills, improving their real-world safety.