Sensors 2013, 13(5), 5460-5477; doi:10.3390/s130505460
Article

Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

Computer Science Department, University Carlos III of Madrid, Legan´es, Madrid 28911, Spain
* Author to whom correspondence should be addressed.
Received: 27 February 2013; in revised form: 18 April 2013 / Accepted: 22 April 2013 / Published: 24 April 2013
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Abstract: Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0:05, proving that the hybrid approach is better suited for the addressed domain.
Keywords: activity recognition; hidden Markov model; hybrid schemes; wireless sensor networks

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MDPI and ACS Style

Ordóñez, F.J.; de Toledo, P.; Sanchis, A. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors. Sensors 2013, 13, 5460-5477.

AMA Style

Ordóñez FJ, de Toledo P, Sanchis A. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors. Sensors. 2013; 13(5):5460-5477.

Chicago/Turabian Style

Ordóñez, Fco. Javier; de Toledo, Paula; Sanchis, Araceli. 2013. "Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors." Sensors 13, no. 5: 5460-5477.

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