MINING HUMAN ACTIVITY PATTERNS FROM SMART HOME BIG DATA FOR HEALTH CARE APPLICATIONS

Abstract

In recent years, there is associate ever-increasing migration of people to urban areas. Health care service
is one amongst the foremost troublesome aspects that greatly littered with the immense flow of individuals to city centres.
Consequently, cities around the world area unit finance heavily in digital transformation in an exceedingly shot to
provide healthier eco systems for people. In such a modification, innumerous homes area unit being equipped with
sensible devices (e.g., sensible meters, sensors), that generate large volumes of fine-grained and reality information that
perhaps analyzed to support sensible city services. throughout this project, we tend to tend to propose a model that
utilizes sensible home large information as a technique of learning and discovering act patterns for health care
applications. we tend to tend to propose the utilization of frequent pattern mining, cluster analysis, and prediction to
measure and analyze energy usage changes sparked by occupant’s behaviour. Since people’s habits area unit in the main
known by everyday routines, discovering these routines permits US to acknowledge abnormal activities that may indicate
people’s difficulties in taking take care of themselves, like not preparing food or not using a shower/bath. This project
addresses the necessity to research temporal energy consumption patterns at the appliance level, that's directly related to
human activities.