Toward explainable AI-empowered cognitive health assessment - Javed, Abdul Rehman; Khan, Habib Ullah; Alomari, Mohammad Kamel Bader; Sarwar, Muhammad Usman; Asim, Muhammad; Almadhor, Ahmad S.; Khan, Muhammad Zahid (Frontiers in Public Health, 2023)
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 Machine Learning (ML) model. Smart homes embedded with smart devices and sensors enable many context-aware applications to recognize physical activities. This paper presents XAI-HAR, a novel XAI empowered Human Activity Recognition (HAR) and cognitive health assessment approach based on key features identified from the data collected from smart sensors located at different places of a sustainable 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. A set of extensive experiments demonstrates the superior performance of XAI-HAR over existing techniques. For explainability, XAI-HAR uses local interpretable model agnostic (LIME) with Random Forest (RF) classifier. XAI-HAR achieves 0.96% f-score for health and dementia classification, 0.95%, 0.97% for activity recognition of dementia and healthy individuals, respectively.