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VO2 estimation using 6-axis motion sensor with sports activity classification.

Takashi Nagata, Naoteru Nakamura, Masato Miyatake

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary

    This study estimates oxygen consumption (VO2) using motion sensors for diverse sports intensities. A machine learning approach improves accuracy by classifying activities and using tailored models, outperforming generic methods.

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    Area of Science:

    • Sports Science
    • Biomechanical Engineering
    • Wearable Technology

    Background:

    • Accurate estimation of oxygen consumption (VO2) is crucial for calculating energy expenditure during physical activity.
    • Existing methods using motion sensors face challenges in maintaining accuracy across diverse exercise intensities and types.
    • Small, wearable motion sensors offer a convenient way to monitor physical activity, but their VO2 estimation accuracy needs improvement.

    Purpose of the Study:

    • To develop and evaluate an improved framework for estimating oxygen consumption (VO2) using 6-axis motion sensors.
    • To enhance the accuracy of VO2 estimation across a wide range of sports activities and intensities.
    • To compare the proposed framework's performance against a reference method using a single regression model.

    Main Methods:

    • Utilized a 6-axis motion sensor (3-axis accelerometer and 3-axis gyroscope) to collect movement data.
    • Implemented a machine learning-based classification algorithm to categorize different types of physical activities.
    • Employed activity-specific linear regression models, with coefficients determined through experimental training data, for VO2 estimation.

    Main Results:

    • The proposed framework demonstrated improved VO2 estimation accuracy compared to a reference method using a common regression model.
    • The accuracy improvement is attributed to the framework's ability to adapt estimation models based on classified activity types.
    • Numerical results validated the framework's effectiveness across a range of exercise intensities using experimental VO2 and motion data.

    Conclusions:

    • The activity classification and tailored modeling approach significantly enhances the accuracy of wearable motion sensor-based VO2 estimation.
    • This framework offers a more reliable method for assessing energy expenditure during varied physical activities.
    • Future research can explore optimizing the trade-off between classification accuracy and VO2 estimation precision.