Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications - 2017


These days, there is an ever-increasing migration of folks to urban areas. Health care service is one in every of the foremost challenging aspects that's greatly laid low with the vast influx of individuals to city centers. Consequently, cities around the globe are investing heavily in digital transformation in an effort to supply healthier ecosystems for individuals. In such a change, voluminous homes are being equipped with smart devices (e.g., good meters, sensors, and therefore on), that generate massive volumes of fine-grained and indexical information that can be analyzed to support sensible town services. In this paper, we propose a model that utilizes sensible home huge information as a means of learning and discovering human activity patterns for health care applications. We propose the employment of frequent pattern mining, cluster analysis, and prediction to live and analyze energy usage changes sparked by occupants' behavior. Since individuals's habits are principally identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate individuals's difficulties in taking look after themselves, such as not getting ready food or not using a shower/bath. This paper addresses the necessity to investigate temporal energy consumption patterns at the appliance level, which is directly related to human activities. For the analysis of the proposed mechanism, this paper uses the U.K. Domestic Appliance Level Electricity knowledge set-time series knowledge of power consumption collected from 2012 to 2015 with the time resolution of 6 s for 5 houses with 109 appliances from Southern England. The information from smart meters are recursively mined in the quantum/data slice of twenty four h, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from appliance usage are presented intimately during this paper together with the accuracy of shortand long-term predictions.

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