This article presents and evaluates a novel algorithm for learning a physical activity classifier for a low-power embedded wrist-located device. The overall system is designed for real-time execution and it is implemented in the commercial low-power System-on-Chips nRF51 and nRF52. Results were obtained using a database composed of 140 users containing more than 340 hours of labeled raw acceleration data. The final precision achieved for the most important classes, (Rest, Walk, and Run), was of 96%, 94%, and 99% and it generalizes to compound activities such as XC skiing or Housework. We conclude with a benchmarking of the system in terms of memory footprint and power consumption.
@article{arxiv.1711.02387,
title = {Learning a Physical Activity Classifier for a Low-power Embedded Wrist-located Device},
author = {Ricard Delgado-Gonzalo and Philippe Renevey and Adrian Tarniceriu and Jakub Parak and Mattia Bertschi},
journal= {arXiv preprint arXiv:1711.02387},
year = {2018}
}
Comments
Submitted to the 2018 IEEE International Conference on Biomedical and Health Informatics