Deep learning for sensor-based activity recognition: A survey. Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. Pattern Recognition Letters, 119:3–11, March, 2019. ![Deep learning for sensor-based activity recognition: A survey [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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{wang_deep_2019,
series = {Deep {Learning} for {Pattern} {Recognition}},
title = {Deep learning for sensor-based activity recognition: {A} survey},
volume = {119},
issn = {0167-8655},
shorttitle = {Deep learning for sensor-based activity recognition},
url = {https://www.sciencedirect.com/science/article/pii/S016786551830045X},
doi = {10.1016/j.patrec.2018.02.010},
abstract = {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.},
language = {en},
urldate = {2022-03-17},
journal = {Pattern Recognition Letters},
author = {Wang, Jindong and Chen, Yiqiang and Hao, Shuji and Peng, Xiaohui and Hu, Lisha},
month = mar,
year = {2019},
keywords = {Activity recognition, Deep learning, Pattern recognition, Pervasive computing},
pages = {3--11},
}
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