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Unsupervised Learning for Human Activity Recognition Using Smartphone Sensors
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- Authors
- Yongjin Kwon, Kyuchang Kang, Changseok Bae
- Issue Date
- 2014-10
- Citation
- Expert Systems with Applications, v.41, no.14, pp.6067-6074
- ISSN
- 0957-4174
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Cali흦ski-Harabasz index, or by choosing appropriate values for and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand. © 2014 Elsevier Ltd. All rights reserved.
- KSP Keywords
- Data collected, Experiment results, Healthcare Services, Hierarchical Clustering, Human activity recognition(HAR), Learning methods, Meaningful information, New approach, Smartphone sensors, Supervised Learning Techniques, mixture of Gaussian