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. loo, ON, N2L 3G1, Canada. (
Um et al. uses deep learning to classify exercise motion from large-scale wearable sensor data achieving 92.14% accuracy with a 3-layered Convolutional Neural Network (CNN) REF .
Exercise motion classification from large-scale wearable sensor data using convolutional neural networks
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science