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Tac-Trainer: A Visual Analytics System for IoT-based Racket Sports Training.

Jiachen Wang, Ji Ma, Kangping Hu

    IEEE Transactions on Visualization and Computer Graphics
    |September 30, 2022
    PubMed
    Summary

    Smart wearable devices and the Tac-Trainer framework enhance racket sports training by integrating Internet of Things (IoT) sensor data with visual analytics (VA). This data-driven approach provides actionable insights, overcoming limitations of traditional coaching methods.

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    Area of Science:

    • Sports Science
    • Human-Computer Interaction
    • Internet of Things (IoT)

    Background:

    • Traditional racket sports training often suffers from subjective biases inherent in coach experience.
    • Smart wearable devices leveraging IoT technology offer potential for data-driven training but face challenges with data volume and dimensionality.
    • Existing methods for extracting information from sensor data lack actionable insights for coaches.

    Purpose of the Study:

    • To propose an integrated framework, Tac-Trainer, combining IoT data and visual analytics (VA) for effective racket sports training.
    • To address the limitations of large data volumes and high dimensions in sensor data for practical coaching applications.
    • To facilitate data-driven decision-making by coaches through actionable insights derived from kinematic data.

    Main Methods:

    • Development of the Tac-Trainer framework with four key components: device configuration, data interpretation, training optimization, and result visualization.
    • Collection of trainee kinematic data using IoT-enabled wearable devices.
    • Transformation of raw sensor data into meaningful attributes and indicators for analysis and suggestion generation.

    Main Results:

    • The Tac-Trainer framework successfully integrates IoT sensor data with VA to provide actionable training suggestions.
    • The system transforms complex kinematic data into understandable indicators, aiding coaches in optimizing training.
    • An interactive visualization interface allows for exploration of training data and results.

    Conclusions:

    • The proposed IoT + VA framework, Tac-Trainer, offers a novel solution for data-driven racket sports training.
    • This approach mitigates biases in conventional coaching by providing objective, data-informed guidance.
    • Future research directions include exploring VA for IoT data and IoT for VA applications in sports training.