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Unsupervised learning for human activity recognition using smartphone sensors
- Source :
- Expert Systems with Applications. 41:6067-6074
- Publication Year :
- 2014
- Publisher :
- Elsevier BV, 2014.
-
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 Calinski–Harabasz index, or by choosing appropriate values for ɛ 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.
- Subjects :
- DBSCAN
Computer science
business.industry
Gaussian
Supervised learning
General Engineering
Machine learning
computer.software_genre
Computer Science Applications
Hierarchical clustering
Activity recognition
symbols.namesake
Index (publishing)
Artificial Intelligence
symbols
Unsupervised learning
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 41
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........94f8fd8651fb244ad962ac182dc774e1