Toward explainable AI-empowered cognitive health assessment
<div><p>Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513526454812672 |
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| author | Abdul Rehman Javed (14764111) |
| author2 | Habib Ullah Khan (12024579) Mohammad Kamel Bader Alomari (14764114) Muhammad Usman Sarwar (14764117) Muhammad Asim (2235472) Ahmad S. Almadhor (14764120) Muhammad Zahid Khan (9533008) |
| author2_role | author author author author author author |
| author_facet | Abdul Rehman Javed (14764111) Habib Ullah Khan (12024579) Mohammad Kamel Bader Alomari (14764114) Muhammad Usman Sarwar (14764117) Muhammad Asim (2235472) Ahmad S. Almadhor (14764120) Muhammad Zahid Khan (9533008) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdul Rehman Javed (14764111) Habib Ullah Khan (12024579) Mohammad Kamel Bader Alomari (14764114) Muhammad Usman Sarwar (14764117) Muhammad Asim (2235472) Ahmad S. Almadhor (14764120) Muhammad Zahid Khan (9533008) |
| dc.date.none.fl_str_mv | 2023-03-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fpubh.2023.1024195 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Toward_explainable_AI-empowered_cognitive_health_assessment/25285075 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Public health explainable AI advanced sensors assistive technology key feature extraction human activity recognition Internet of Things healthcare |
| dc.title.none.fl_str_mv | Toward explainable AI-empowered cognitive health assessment |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Public Health<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fpubh.2023.1024195" target="_blank">https://dx.doi.org/10.3389/fpubh.2023.1024195</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3f801181c2e585df603805d36c31c6d8 |
| identifier_str_mv | 10.3389/fpubh.2023.1024195 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25285075 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Toward explainable AI-empowered cognitive health assessmentAbdul Rehman Javed (14764111)Habib Ullah Khan (12024579)Mohammad Kamel Bader Alomari (14764114)Muhammad Usman Sarwar (14764117)Muhammad Asim (2235472)Ahmad S. Almadhor (14764120)Muhammad Zahid Khan (9533008)Health sciencesPublic healthexplainable AIadvanced sensorsassistive technologykey feature extractionhuman activity recognitionInternet of Thingshealthcare<div><p>Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Public Health<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fpubh.2023.1024195" target="_blank">https://dx.doi.org/10.3389/fpubh.2023.1024195</a></p>2023-03-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fpubh.2023.1024195https://figshare.com/articles/journal_contribution/Toward_explainable_AI-empowered_cognitive_health_assessment/25285075CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252850752023-03-09T03:00:00Z |
| spellingShingle | Toward explainable AI-empowered cognitive health assessment Abdul Rehman Javed (14764111) Health sciences Public health explainable AI advanced sensors assistive technology key feature extraction human activity recognition Internet of Things healthcare |
| status_str | publishedVersion |
| title | Toward explainable AI-empowered cognitive health assessment |
| title_full | Toward explainable AI-empowered cognitive health assessment |
| title_fullStr | Toward explainable AI-empowered cognitive health assessment |
| title_full_unstemmed | Toward explainable AI-empowered cognitive health assessment |
| title_short | Toward explainable AI-empowered cognitive health assessment |
| title_sort | Toward explainable AI-empowered cognitive health assessment |
| topic | Health sciences Public health explainable AI advanced sensors assistive technology key feature extraction human activity recognition Internet of Things healthcare |