Human Activity Patterns Prediction (HAPP) System for Smart Healthcare Applications

Human services administration is a standout amongst the most testing angles that is significantly influenced by the immense flood of individuals to downtown areas. Thusly, urban communities around the globe are putting intensely in advanced change with an end goal to give more advantageous biological community to individuals. In such change, a large number of homes are being furnished with brilliant gadgets (for example shrewd meters, sensors and so on. ) which produce gigantic volumes of fine-grained and indexical information that can be investigated to help savvy city administrations. In this paper, we propose a HAPP model that uses keen home enormous information as a methods for learning and finding human movement designs for medicinal services applications. We propose the utilization of successive example mining, group examination and expectation to quantify and dissect vitality use changes started by tenants' conduct. Since individuals' propensities are for the most part distinguished by regular schedules, finding these schedules enables us to perceive irregular exercises that may demonstrate individuals' challenges in taking consideration for themselves, for example, not planning nourishment or not utilizing shower/shower.