Human Activity Recognition with an HMM-Based Generative Model
Open Access
Abstract
Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded–scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.Keywords
This publication has 97 references indexed in Scilit:
- Deep learning for healthcare: review, opportunities and challengesBriefings in Bioinformatics, 2017
- Statistical Anomaly Detection in Human Dynamics Monitoring Using a Hierarchical Dirichlet Process Hidden Markov ModelIEEE Transactions on Intelligent Transportation Systems, 2017
- Unsupervised learning of finite mixtures using scaled dirichlet distribution and its application to software modules categorization2017
- Variational Inference: A Review for StatisticiansJournal of the American Statistical Association, 2017
- A Heterogeneous Clustering Approach for Human Activity Recognition2016
- On the Design of Smart Homes: A Framework for Activity Recognition in Home EnvironmentJournal of Medical Systems, 2016
- Comparing variational Bayes with Markov chain Monte Carlo for Bayesian computation in neuroimagingStatistical Methods in Medical Research, 2012
- Collecting complex activity datasets in highly rich networked sensor environments2010
- Variational inference for Dirichlet process mixturesBayesian Analysis, 2006
- An introduction to hidden Markov modelsIEEE ASSP Magazine, 1986