Exercise Motion Classification from Large-Scale Wearable Sensor Data   Using Convolutional Neural Networks
  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. 
Terry Taewoong Um, Vahid Babakeshizadeh and Dana Kuli\'c
cs.CV cs.LG