Automated cognitive health assessment in smart homes using machine learning

•Identification and assessment of healthy, MCI and dementia individuals.•Automated tasks assessment performed by the participants using supervised learning.•Temporal feature analysis to efficiently classify impaired individuals.•Enhance the identification rate of Ensemble Adaboost compared to the literature. The Internet of Things (IoT) provides smart solutions for future urban communities to address key benefits with the least human intercession. A smart home offers the necessary capabilities to promote efficiency and sustainability to a resident with their healthcare-related, social, and emotional needs. In particular, it provides an opportunity to assess the functional health ability of the elderly or individuals with cognitive impairment in performing daily life activities. This work proposes an approach named Cognitive Assessment of Smart Home Resident (CA-SHR) to measure the ability of smart home residents in executing simple to complex activities of daily living using pre-defined scores assigned by a neuropsychologist. CA-SHR also measures the quality of tasks performed by the participants using supervised classification. Furthermore, CA-SHR provides a temporal feature analysis to estimate if the temporal features help to detect impaired individuals effectively. The goal of this study is to detect cognitively impaired individuals in their early stages. CA-SHR assess the health condition of individuals through significant features and improving the representation of dementia patients. For the classification of individuals into healthy, Mild Cognitive Impaired (MCI), and dementia categories, we use ensemble AdaBoost. This results in improving the reliability of the CA-SHR through the correct assignment of labels to the smart home resident compared with existing techniques.