Progressive assessment system for dementia care through smart home

Dementia is an age-related memory loss. It is a long-term and often the gradual decrease of thinking ability that affects the patient's daily living. Constant monitoring and support from caretaker is required to carry out routine activities. The overhead incurred in caretaking is high in terms of money, time and energy. Thus, assistive health care system for dementia is essential and feasible through the smart home. Activity recognition, decision support, and clinical score assessment are the various phases concerned in modeling dementia care system. This research work focuses specifically on clinical score assessment that measures the performance of the activities in terms of cognitive and mobility traits of the dementia patient. This progressive estimation provides decision support system to the doctors so as to offer appropriate treatment based on patient's performance in daily activities. The usual procedure for clinical assessment is a questionnaire session which is prone to errors. Therefore, the proposed system models a progressive assessment framework for dementia care through the smart home that integrates supervised machine learning and context-based reasoning to perform context-based clinical assessment. Thus, the experimental results suggest that each diagnosis dement occupant reached 80% of classification accuracy.