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

Terry Taewoong Um, Vahid Babakeshizadeh, Dana Kulic

Published 2016-10-22Version 1

The ability to accurately observe human motion and 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. The resulting CNN classifies 50 gym exercises with 92.14% accuracy. A comparative study on the effects of image formatting and different CNN architectures is also presented.