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Related Concept Videos

Rolling Resistance: Problem Solving01:17

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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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AI-Driven Slip Detection for Smarter Footwear Testing using Vision Transformers.

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    This study introduces an AI-powered system for automated slip detection in footwear testing. The Multiscale Vision Transformers Version 2 (MViTv2) model significantly improves the accuracy and consistency of slip resistance assessments for winter footwear.

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    Area of Science:

    • Biomechanics and Human Movement Analysis
    • Artificial Intelligence in Safety Engineering
    • Materials Science and Footwear Technology

    Background:

    • Slips and falls are major health concerns, leading to injuries and increased healthcare costs, especially on icy surfaces.
    • Current footwear slip-resistance testing relies on human observation, introducing inconsistencies and subjective bias.
    • Developing reliable slip-resistant footwear is crucial for preventing falls in hazardous winter conditions.

    Purpose of the Study:

    • To enhance footwear slip-resistance testing protocols for greater accuracy, reliability, and consistency.
    • To introduce a novel automated slip detection system using Artificial Intelligence (AI) to mitigate human observational errors.
    • To evaluate the performance of AI models in assessing slip resistance, particularly for winter footwear.

    Main Methods:

    • Developed an automated slip detection system using AI, specifically transfer learning with pre-trained neural networks: Inflated 3D ConvNets (I3D) and Multiscale Vision Transformers Version 2 (MViTv2).
    • Trained models on 410 walking trials on a controlled icy walkway with varying slope angles, including both slip and non-slip events.
    • Evaluated model performance using 5-Fold and Leave-One-Subject-Out (LOSO) cross-validation techniques.

    Main Results:

    • The Multiscale Vision Transformers Version 2 (MViTv2) model demonstrated superior performance, achieving an average accuracy of 90.49% ± 2.93% (5-Fold) and 88.80% ± 4.82% (LOSO).
    • MViTv2 showed greater consistency in slip detection performance compared to the Inflated 3D ConvNets (I3D) model.
    • While performance differences between I3D and MViTv2 were not statistically significant, MViTv2 consistently outperformed I3D.

    Conclusions:

    • Video-based AI models, particularly vision transformers like MViTv2, show significant potential for precise automated slip detection in footwear.
    • The integration of AI-based slip detection into footwear testing protocols can lead to more reliable assessments of slip resistance.
    • This advancement can contribute to improved fall prevention strategies, reduced healthcare costs, and enhanced mobility in icy environments.