Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
AbstractGetting 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
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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.
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.Chicago/Turabian Style
Li, Frédéric; Shirahama, Kimiaki; Nisar, Muhammad A.; Köping, Lukas; Grzegorzek, Marcin. 2018. "Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors." Sensors 18, no. 2: 679.
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