Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks

Terry Taewoong Um; Vahid Babakeshizadeh; Dana Kulić

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arXiv · 1610.07031

Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks

Terry Taewoong Um·Vahid Babakeshizadeh·Dana Kulić

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Abstract

The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.

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Terry Taewoong Um, Vahid Babakeshizadeh, Dana Kulić. 2016-10-22. Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks. https://arxiv.org/abs/1610.07031

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