Human Activity Recognition with an HMM-Based Generative Model

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023, Vol.23 (3), p.1390
Main Authors: Manouchehri, Narges, Bouguila, Nizar
Format: Article
Language:English
Subjects:
Aged
Algorithms
Approximation
Continuous flow
Deep learning
Dirichlet problem
Hidden Markov models
Human Activities
Human activity recognition
Human acts
Human behavior
Humans
Markov Chains
medical applications
Medicin och hälsovetenskap
Models, Statistical
Object recognition (Computers)
Parameter estimation
Patient monitoring
Pattern recognition
Probability
proportional data
scaled Dirichlet distribution
Sensors
Statistical models
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23031390