Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.
@article{arxiv.1707.03502,
title = {Deep Learning for Sensor-based Activity Recognition: A Survey},
author = {Jindong Wang and Yiqiang Chen and Shuji Hao and Xiaohui Peng and Lisha Hu},
journal= {arXiv preprint arXiv:1707.03502},
year = {2018}
}
Comments
10 pages, 2 figures, and 5 tables; submitted to Pattern Recognition Letters (second revision)