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

2016/10/22 by Terry Taewoong Um, Um, Terry Taewoong, Vahid Babakeshizadeh +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.CV #cs.LG

paper · pdf · doi:10.48550/arxiv.1610.07031

will appear in IROS2017

arxiv created 2017/07/22 · arxiv updated 2017/07/25

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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