Toward explainable AI-empowered cognitive health assessment

Bibliographic Data

ID22088799
AuthorsAbdul Rehman Javed (0000-0002-0570-1813, Lebanese American University, corresponding author), Habib Ullah Khan (0000-0001-8373-2781, Qatar University), Mohammad Kamel Bader Alomari, Mohammad Kamel Alomari (0000-0002-7585-2772, Qatar University, corresponding author), Muhammad Usman Sarwar (0000-0001-7494-3413, Air University), Muhammad Asim (0000-0002-0905-0026, National University of Computer and Emerging Sciences), Ahmad S Almadhor, Ahmad Almadhor (0000-0002-8665-1669, Jouf University), Muhammad Zahid Khan (0000-0002-1171-1794, University of Malakand)
Year2023
Volume11
Pages1024195-1024195
Publication date2023-03-09
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1024195
PMID36969684
OpenAlexW4323666669
LanguageEN
References cited55

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

Cognition · Cognitive Assessment System · Cognitive impairment · Psychiatry · Artificial Intelligence in Healthcare · Computer Science · Context-Aware Activity Recognition Systems · Machine Learning in Healthcare · Medicine · Psychology

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