Nowadays, there is an ever-increasing migration of people to urban areas. Health care services are one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystem for people. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis and prediction to measure and analyze energy usage changes sparked by occupants’ behavior.
Big data, smart cities, smart homes, smart cities, health care applications, Behavioral Analytics, Frequent Pattern, Cluster Analysis, Incremental Data-Mining, Association Rules, Prediction
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