Paper Details

In recent years, there is associate ever-increasing migration of people to urban areas. Health care serviceis 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 toprovide healthier eco systems for people. In such a modification, innumerous homes area unit being equipped withsensible devices (e.g., sensible meters, sensors), that generate large volumes of fine-grained and reality information thatperhaps analyzed to support sensible city services. throughout this project, we tend to tend to propose a model thatutilizes sensible home large information as a technique of learning and discovering act patterns for health careapplications. we tend to tend to propose the utilization of frequent pattern mining, cluster analysis, and prediction tomeasure and analyze energy usage changes sparked by occupant’s behaviour. Since people’s habits area unit in the mainknown by everyday routines, discovering these routines permits US to acknowledge abnormal activities that may indicatepeople’s difficulties in taking take care of themselves, like not preparing food or not using a shower/bath. This projectaddresses the necessity to research temporal energy consumption patterns at the appliance level, that's directly related tohuman activities.

Sayali Inamdar, “MINING HUMAN ACTIVITY PATTERNS FROM SMART HOME BIG DATA FOR HEALTH CARE APPLICATIONS”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 7, pp. 55-59, July 2018.