The ADLs gathered via sensors in a smart home are divided into simple and complex activities. Sensors in a smart home environment for cognitive health assessment. Smart homes have multiple networks of sensors that can gather an overview of residents’ activity patterns in terms of their health, security, safety, independent activities, and their social lives as can be seen in Figure 1. Smart homes with multiple sensors are a promising tool for HAR to gather data regarding ADL. For the said reason, HAR (Human Activity Recognition) is emerging as an effective method to monitor an individual’s movements and activities and has gained special focus in the field of research to improve healthcare systems. It has been observed that very subtle signs and symptoms first appear in the daily activities of individuals suffering from cognitive decline, which clinicians can easily miss in a physical exam. This can result in an inaccurate assessment. The information regarding a patient’s ADLs is gathered via a questionnaire filled by the patient himself or his/her guardian, hence making the entire assessment process subjective. At present cognitive health is analyzed in the clinic using a cognitive function test like MMSE (Mini-Mental State Examination) and MoCA (Montreal Cognitive Assessment) and other neurological exams like CDR (Clinical Dementia Rating) and assessment of ADL. Early intervention can reduce the healthcare system’s burden globally and eventually reduce the associated mortality rate. This figure is expected to rise to $6 trillion by 2030 at the current rate. In 2010 reduced productivity and poor health owing to poor mental health resulted in a $2.5 trillion loss worldwide. WHO (World Health Organization) has concluded that the investment in mental health has not matched the awareness scale of mental health problems. Over 1 billion people suffer from one mental disease, addiction, dementia, or schizophrenia. There has been a rapid increase in mental disorders and the people suffering from them in the last few years. healthy individuals when evaluating their complex interwoven activities. It is observed that deep neural networks and multilayer perceptron show the best results for classifying dementia vs. Their results and performances are compared to determine the best classifier. decision tree, Naive Bayes, support vector, multilayer perceptron classifiers, and deep neural networks have been used for classification. We use the subset of the CASAS (Centre of Advanced Studies in Adaptive Systems) dataset for eight complex activities performed by 179 individuals in a smart home setting. Our proposed work uses machine and deep learning classifiers to classify dementia and healthy individuals by analyzing complex interwoven activity data. When applied to ADL data, machine learning and deep learning algorithms can conveniently and accurately analyze activity patterns and predict the first signs of cognitive decline. Artificial Intelligence has been one of health-care’s most promising techniques for prediction and diagnosis. Therefore, we analyze individuals’ performance while performing complex activities as opposed to Simple ADL. First signs of cognitive decline appear when a cognitively impaired individual tries to perform complex activities involving planning, analyzing, calculating, and decision making. ADLs are categorized as activities of daily life and complex interwoven activities. They can give a comprehensive view of the ADL (Activities of Daily Living) of dementia patients. Smart homes have been most influential in detecting and managing cognitive diseases like dementia. Smart homes and the IoT (Internet of Things) have given hope to the health industry to monitor and manage the elderly and the less-abled in the comfort of their homes. With the prevalence of cognitive diseases, the health industry is facing newer challenges since cognitive health deteriorates gradually over time, and clear signs and symptoms appear when it is too late.
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