@inproceedings{2e3f5a178ea442d89773b1c3698cb1a6,
title = "Deep activity recognition models with triaxial accelerometers",
abstract = "Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms. This paper shows that deep activity recognition models (a) provide better recognition accuracy of human activities, (b) avoid the expensive design of handcrafted features in existing systems, and (c) utilize the massive unlabeled acceleration samples for unsupervised feature extraction. We show substantial recognition improvement on real world datasets over state-of-the-art methods of human activity recognition using triaxial accelerometers.",
keywords = "Activity recognition, deep learning, feature learning, accelerometers",
author = "{Abu Alsheikh}, Mohammad and Ahmed Selim and Dusit Niyato and Linda Doyle and Shaowei Lin and Tan, {Hwee Pink}",
year = "2016",
language = "English",
isbn = "9781577357599",
volume = "WS-16-01 - WS-16-15",
series = "AAAI Workshop - Technical Report",
publisher = "AI Access Foundation",
pages = "8--13",
booktitle = "AAAI Conference on Artificial Intelligence",
address = "United States",
note = "30th AAAI Conference on Artificial Intelligence, AAAI 2016 ; Conference date: 12-02-2016 Through 13-02-2016",
}