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.
For example, Javier Ordónez et al. REF used artificial neural networks and a support vector machine within the framework of a hidden Markov model to take advantage of both a discriminant model and a generative model.
Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Engineering Medicine Computer Science