Many older adults perform collision-avoidance behavior either insufficiently (i.e., frequent collision) or inefficiently (i.e., exaggerated behavior to ensure collision-avoidance). The present study examined whether a training system using virtual reality (VR) simulation enhanced older adults' collision-avoidance behavior in response to a VR image of an aperture during real walking. Twenty-five (n = 13 intervention group and n = 12 control group) older individuals participated. During training, a VR image of walking through an aperture was projected onto a large screen. Participants in the intervention group tried to avoid virtual collision with the minimum body rotation required to walk on the spot through a variety of narrow apertures. Participants in the control group remained without body rotation while walking on the spot through a wide aperture. A comparison between pre-test and post-test performances in the real environment indicated that after the training, significantly smaller body rotation angles were observed in the intervention group. This suggests that the training led participants to modify their behavior to try to move efficiently during real walking. However, although not significant, collision rates also tended to be greater, suggesting that, at least for some participants, the modification required to avoid collision was too difficult. Transfer of the learned behavior using the VR environment to real walking is discussed.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.archger.2020.104265DOI Listing

Publication Analysis

Top Keywords

collision-avoidance behavior
12
real walking
12
intervention group
12
body rotation
12
older adults
8
virtual reality
8
walking aperture
8
control group
8
behavior
6
walking
6

Similar Publications

Driving behavior is crucial in shaping traffic dynamics and serves as the foundation for safe and efficient autonomous driving. Despite the widespread interest in driving behavior modeling, existing models often focus on specific behaviors and cannot describe all types of vehicle movements, while vehicle status and driving scenarios are dynamic and infinite. That means comprehending and modeling generalized driving behavior mechanisms is essential.

View Article and Find Full Text PDF
Article Synopsis
  • The study investigates how sport-specific training enhances athletes' collision avoidance skills in dynamic environments, particularly in a virtual reality setting.
  • It compares responses of trained athletes to untrained young adults while they navigate a path and avoid virtual opponents approaching at various speeds.
  • Results indicate that athletes excel in attention-demanding tasks and show adaptive behaviors, but no significant differences were found in their actual collision avoidance timing or clearance.
View Article and Find Full Text PDF

Freezing of movements and its correspondence with MLG1 neuron response to looming stimuli in the crab Neohelice.

J Exp Biol

November 2024

Depto. Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, IFIBYNE-CONICET, Pabellón 2 Ciudad Universitaria (1428), C1428EHA Buenos Aires, Argentina.

Upon visually detecting a moving predator, animals often freeze, i.e. stop moving, to minimize being uncovered and to gather detailed information of the object's movements and properties.

View Article and Find Full Text PDF

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker.

View Article and Find Full Text PDF

This study introduces a simulated active matter system, applying the pedestrian collision avoidance paradigm, which involves dynamically adjusting the desired velocity. We present a human-zombie game set within a closed geometry, combining predator-prey behavior with a one-way contagion process that transforms prey into predators. The system demonstrates varied responses in our implemented model: with agents having the same maximum speeds, a single zombie always captures a human, whereas two zombies never capture a single human agent.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!