Virtual reality (VR) could be used to set up a training protocol to improve one's collision-avoidance behavior. In our previous study, we developed a VR system for training older individuals to walk through an aperture in a manner that is both safe (i.e., no collision) and efficient (i.e., no exaggerated behavior to ensure collision avoidance). In the present study, we made several modifications to the VR system in terms of enriched feedback (vibratory stimulation for virtual collisions and the addition of positive feedback for successful trials) and gradual increase in task difficulty during training to strengthen the skill transfer. Nineteen older adults (74.4 ± 5.3 years of age) and 21 younger adults (25.1 ± 5.0 years of age) participated. They were randomly assigned to one of two training groups: the intervention group (older: = 10; younger: = 10) or the control group (older: = 11; younger: = 9). The experiment consisted of pre- and post-training tests in a real environment and training in a VR environment. During training, participants held a horizontal bar while stepping in place as if a VR image on the screen were moving in response to their stepping. Participants in the intervention group tried to pass a narrow aperture without collision while attempting to minimize their body rotation to avoid collision as much as possible. The criterion upon which the collision-avoidance behavior was regarded as successful became incrementally more demanding as participants successfully met the previous criterion. Participants in the control group passed through a very wide aperture, so that collision-avoidance behavior was unnecessary. A comparison between pre- and post-training test performances showed that, for both older and younger adults in the intervention group, the spatial margins became significantly smaller, while the success rate remained unchanged. For those in the control group, neither the spatial margin nor the success rate was improved. These results suggest that the three modifications made for the VR system contributed to improvement of the system and helped participants transfer the behavior learned from the VR environment to real walking.
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http://dx.doi.org/10.3389/fspor.2022.844436 | 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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