Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.
Titel | Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Li F., Shirahama K., Nisar M.A., Köping L., Grzegorzek M. |
Journal | Sensors (Basel, Switzerland) |
Volume | 18 |
Issue | 2 |
Date Published | 2018 Feb 24 |
Publication Language | eng |
ISSN | 1424-8220 |
Schlüsselwörter | Human Activities, Humans, Machine Learning, Neural Networks (Computer), Wearable Electronic Devices |
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. |
DOI | 10.3390/s18020679 |
PubMed Link | |
Alternate Journal | Sensors (Basel) |