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.
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http://dx.doi.org/10.1016/j.archger.2020.104265 | DOI Listing |
Accid Anal Prev
December 2024
School of Mathematical Sciences, Beihang University, Beijing 100191, China.
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 PDFPLoS One
October 2024
Department of Kinesiology & Physical Education, Wilfrid Laurier University, Waterloo, ON, Canada.
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 PDFSensors (Basel)
September 2024
Graduate Program in Informatics, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil.
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 PDFPhys Rev E
August 2024
Instituto Tecnológico de Buenos Aires (ITBA), CONICET, Lavardén 315, 1437 C. A. de Buenos Aires, Argentina.
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.
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