| DOI | Resolve DOI: https://doi.org/10.1109/LSENS.2024.3425760 |
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| Author | Search for: Valdés, Julio J.1ORCID identifier: https://orcid.org/0000-0003-2930-0325; Search for: Cook, Zara1ORCID identifier: https://orcid.org/0009-0009-6059-4377; Search for: Wang, JackORCID identifier: https://orcid.org/0009-0009-1621-2298; Search for: Wallace, BruceORCID identifier: https://orcid.org/0000-0003-4379-2717; Search for: Laska, BradyORCID identifier: https://orcid.org/0000-0003-4336-0228; Search for: Green, JamesORCID identifier: https://orcid.org/0000-0002-6039-2355; Search for: Goubran, RafikORCID identifier: https://orcid.org/0000-0003-4087-416X; Search for: Xi, Pengcheng1ORCID identifier: https://orcid.org/0000-0003-3236-5234 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | sensor applications; aging in place; explainable artificial intelligence (XAI); home care; human activity recognition; multimodal feature learning; sensor data; visualization; sensors; representation learning; feature extraction; explainable ai; manifolds; data models |
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| Abstract | This letter presents an approach to enhance the well-being and safety of aging populations through better understanding their everyday activities. Our approach uses ambient sensor data and analyzes them with multimodal feature learning and explainable artificial intelligence (XAI) through the ImageBind framework. By integrating the SHapley Additive exPlanations (SHAP) method, our system uncovers intricate patterns within human daily activities. Experimental results reveal significant improvements in activity classification accuracy, particularly with the XGBoost model applied to the Kinetics dataset. Moreover, by utilizing a subset of the most influential features identified through SHAP analysis, our method achieves notable reductions in predictors without sacrificing performance. |
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| Publication date | 2024-07-18 |
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| Publisher | Institute of Electrical and Electronics Engineers |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | a433307b-1ca3-4940-92da-c8b210ff4dbf |
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| Record created | 2024-08-21 |
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| Record modified | 2024-08-22 |
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