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Article

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

1
Research Group for Pattern Recognition, University of Siegen, Hölderlinstr 3, 57076 Siegen, Germany
2
Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-226 Katowice, Poland
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 679; https://doi.org/10.3390/s18020679
Received: 16 January 2018 / Revised: 16 February 2018 / Accepted: 22 February 2018 / Published: 24 February 2018
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: human activity recognition; multimodal time series processing; feature learning; deep neural networks; evaluation framework human activity recognition; multimodal time series processing; feature learning; deep neural networks; evaluation framework
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MDPI and ACS Style

Li, F.; Shirahama, K.; Nisar, M.A.; Köping, L.; Grzegorzek, M. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors 2018, 18, 679. https://doi.org/10.3390/s18020679

AMA Style

Li F, Shirahama K, Nisar MA, Köping L, Grzegorzek M. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors. 2018; 18(2):679. https://doi.org/10.3390/s18020679

Chicago/Turabian Style

Li, Frédéric, Kimiaki Shirahama, Muhammad A. Nisar, Lukas Köping, and Marcin Grzegorzek. 2018. "Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors" Sensors 18, no. 2: 679. https://doi.org/10.3390/s18020679

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