Exercise motion classification from large-scale wearable sensor data using convolutional neural networks

Terry Taewoong Um; Vahid Babakeshizadeh; Dana Kulić

IROS 2017poster130 citations

Exercise motion classification from large-scale wearable sensor data using convolutional neural networks

Terry Taewoong Um, Vahid Babakeshizadeh, Dana Kulić

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.

BibTeX

@inproceedings{iros2017_exercisemotioncl,
  title = {Exercise motion classification from large-scale wearable sensor data using convolutional neural networks},
  author = {Terry Taewoong Um and Vahid Babakeshizadeh and Dana Kulić},
  booktitle = {IROS 2017},
  year = {2017}
}