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AuthorJaved, Abdul Rehman
AuthorKhan, Habib Ullah
AuthorAlomari, Mohammad Kamel Bader
AuthorSarwar, Muhammad Usman
AuthorAsim, Muhammad
AuthorAlmadhor, Ahmad S.
AuthorKhan, Muhammad Zahid
Available date2023-08-09T06:06:12Z
Publication Date2023-03-09
Publication NameFrontiers in Public Health
Identifierhttp://dx.doi.org/10.3389/fpubh.2023.1024195
CitationJaved, A. R., Khan, H. U., Alomari, M. K. B., Sarwar, M. U., Asim, M., Almadhor, A. S., & Khan, M. Z. (2023). Toward explainable AI-empowered cognitive health assessment. Frontiers in Public Health, 11, 1024195.
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85150733319&origin=inward
URIhttp://hdl.handle.net/10576/46556
AbstractExplainable 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.
SponsorThis research was supported by Qatar National Library and Qatar University's Internal Grant IRCC-2021-010.
Languageen
PublisherFrontiers Media S.A.
Subjectadvanced sensors
assistive technology
explainable AI
healthcare
human activity recognition
Internet of Things
key feature extraction
TitleToward explainable AI-empowered cognitive health assessment
TypeArticle
Volume Number11
ESSN2296-2565
dc.accessType Open Access


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