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Healthcare Analysis using Smart Meter Data


Yanala Sai Gowtham , CMR Technical Campus; Nelli Sowmya, CMR Technical Campus; Mr. G. Vinesh, CMR Technical Campus


Big Data, Smart Home, Smart Meters, Smart City, Frequent Pattern Mining, Cluster Analysis, Prediction, Health Care Applications


As we know that, there is a rapid raise in the count of the population relocating to urban areas. Health care services is one of the most challenging features that is greatly affected by the vast inrush of people to city centres. Cities are currently adopting massive digital transformation in an effort and support to provide a healthier environment. Most of homes are being furnished with smart devices (e.g. smart meters, sensors etc.) which generate massive volumes of fine-grained and indexical data that can be analysed to support smart city services. This paper proposes a model that exploits smart home big data in order to analyse and discover the changes in energy usage in house holders behaviour for health care application to detect health problems. Here we used cluster analysis, frequent pattern and prediction process. Since peoples habits are mostly identified by everyday routines, evaluating these routines makes us to recognise abnormal activities that may indicate peoples difficulties in taking care for themselves, such as not preparing food or not using shower/bath. The human activity datasets which are generated by the smart meters are mined using the Big Data algorithms. The results of identifying human activity patterns from appliance usage are presented in details in this paper along with accuracy of short and long term predictions.

Other Details

Paper ID: IJSRDV7I21458
Published in: Volume : 7, Issue : 2
Publication Date: 01/05/2019
Page(s): 2084-2087

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