@article {1114,
	title = {Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.},
	journal = {Sensors (Basel, Switzerland)},
	volume = {18},
	year = {2018},
	month = {2018 Feb 24},
	abstract = {Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.},
	keywords = {Human Activities, Humans, Machine Learning, Neural Networks (Computer), Wearable Electronic Devices},
	issn = {1424-8220},
	doi = {10.3390/s18020679},
	author = {Li, Fr{\'e}d{\'e}ric and Shirahama, Kimiaki and Nisar, Muhammad Adeel and K{\"o}ping, Lukas and Grzegorzek, Marcin}
}
