An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition

This paper presents a human activity recognition framework tailored for healthcare applications, emphasizing the essential balance between accuracy and interpretability required for medical monitoring. The model utilizes MediaPipe to capture the complex dynamics of human movements and introduce an i...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.187947-187963
Main Authors: Dutt, Micheal, Goodwin, Morten, Omlin, Christian W.
Format: Article
Language:English
Subjects:
Accuracy
Adaptation models
Artificial intelligence
computational intelligence
computer vision
Data models
deep learning
Feature extraction
healthcare
Human activity recognition
Human motion
intelligent systems
machine learning
Medical services
Monitoring
Permutations
Principal components analysis
Telemedicine
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Summary:This paper presents a human activity recognition framework tailored for healthcare applications, emphasizing the essential balance between accuracy and interpretability required for medical monitoring. The model utilizes MediaPipe to capture the complex dynamics of human movements and introduce an interpretable feature reduction function. This method improves traditional dimensionality reduction techniques like principal component analysis. Our feature engineering is based on the importance of feature permutations; it selectively retains salient features, thus enhancing the interpretability essential for the medical domain. We validated our method on the "NTU RGB+D" dataset; it improves the recognition accuracy for a range of human activities that may be relevant for elderly care. However, the recognition of subtler activities like neck pain and headaches requires further investigation. This study underscores the potential to advance patient monitoring and sets the stage for its expanded application in various medical contexts.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3432776