A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework

This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. The CNN-GRU successfully captures spatial and temporal dependencie...

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Bibliographic Details
Published inPloS one Vol. 20; no. 8; p. e0328181
Main Authors Sohaib, Sarmad, Bokhari, Syed Mohsin, Shafi, Muhammad, Alhashmi, Anas
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 07.08.2025
Public Library of Science (PLoS)
Subjects
Accuracy
Activity recognition
Algorithms
Artificial neural networks
Biology and Life Sciences
Classification
Computer and Information Sciences
Deep learning
Engineering and Technology
Fields (mathematics)
Human-computer interaction
Humans
Indoor environments
Integrated approach
Internet of Things
Kalman filters
Localization
Location-based systems
Markov Chains
Markov processes
Medicine and Health Sciences
Neural networks
Neural Networks, Computer
Performance evaluation
Physical Sciences
Probability distribution
Sensors
Smart buildings
Smart houses
Social Sciences
Walking
Saudi Arabia
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Summary:This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. The CNN-GRU successfully captures spatial and temporal dependencies, while the MRF models the mutual relations of activities and locations by estimating their joint probability distribution. The new system was tested on a public smart home dataset with four activities (sitting, lying, walking, and standing) and four indoor locations (kitchen, bedroom, living room, and stairs). The hybrid framework obtained an accuracy of 95% for activity recognition and 93% for indoor localization with a combined activity-location classification accuracy of 81%. Such results confirm the ability of the system to provide robust predictions in real-world smart environments, make it highly suitable for healthcare and intelligent living applications, and is efficient and deployable in real-world scenarios, addressing the critical challenges of noisy and dynamic indoor environments.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0328181