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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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10606472/ |
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| author | Micheal Dutt Morten Goodwin Christian W. Omlin |
| author_facet | Micheal Dutt Morten Goodwin Christian W. Omlin |
| author_sort | Micheal Dutt |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4f7585bbdcb04eecafcdf147492722dc |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4f7585bbdcb04eecafcdf147492722dc2025-08-20T02:49:09ZengIEEEIEEE Access2169-35362024-01-011218794718796310.1109/ACCESS.2024.343277610606472An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity RecognitionMicheal Dutt0https://orcid.org/0000-0001-8800-0977Morten Goodwin1https://orcid.org/0000-0001-6331-702XChristian W. Omlin2https://orcid.org/0000-0003-0299-171XCentre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, NorwayCentre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, NorwayCentre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, NorwayThis 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.https://ieeexplore.ieee.org/document/10606472/Artificial intelligencemachine learningdeep learninghuman activity recognitionhealthcarecomputer vision |
| spellingShingle | Micheal Dutt Morten Goodwin Christian W. Omlin An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition IEEE Access Artificial intelligence machine learning deep learning human activity recognition healthcare computer vision |
| title | An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition |
| title_full | An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition |
| title_fullStr | An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition |
| title_full_unstemmed | An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition |
| title_short | An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition |
| title_sort | interpretable deep learning based feature reduction in video based human activity recognition |
| topic | Artificial intelligence machine learning deep learning human activity recognition healthcare computer vision |
| url | https://ieeexplore.ieee.org/document/10606472/ |
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