Activities of Daily Living(ADL) refer to the activities carried out by individuals in their everyday living. ADL’s are good indicators of the health status of individuals. Proper monitoring of these activities can be achieved by attaching state-change sensors to objects in the home which gives a reflection of the object interaction, usage and subsequently the ongoing activity. The increase in the elderly Population nowadays will lead to an increase in the cost of elderly care.Also there is increase decline in sleep durations nowadays short sleep durations (≤6h) could have Cardiovascular disease risk than long sleep durations(≥7h).In this thesis by using PIR sensors and other Binary sensors from Uci binary data set and our proposed fuzzy logic model expert system we efficiently predicted activities of daily living and Sleeping duration in the home based on the acquired binary sensor data. We obtained 96.5% accuracy for Daily Living Activity Prediction and 100% accuracy Sleep duration based on our Fuzzy logic model and binary sensors data. Conclusively, with our method we can effectively predict daily living activity in the home and the sleeping duration class of the elderly home user as a means of improving the care situation of the user.
