Online Health Monitoring using Household Activity Patterns from Smart Meter Data

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K. Salomy, D. Eswaraiah


In recent years, people are migrating from rural areas to urban areas which became common. The people whoever suffering from ill-health must require health care services and providing those services to them is the most challenging aspect. Technological enhancements led to construct smart homes, which are equipped several sensor or smart meter for process automation of another electronic device. In addition to these smart meters are able to capture the patient�s routine activities and also monitors their health situations by frequent patterns mining and association rules formed from smart meters. We introduced a model in this work which is able to monitor the patient�s activities in home and could send routine activities to the respected doctor. We can retrieve frequent patterns and association rules from log data and can estimate the patient�s health situations and suggest them based on this prediction. Our work is partitioned into three stages. Initially we record the patients� routine activities by allocating particular time period with three regular intervals. In second stage, we applied the growth of frequent pattern in order to extract the association rules from log file. In final stage, we applied k-means clustering for input and applied Bayesian network model to guess the patient�s health behavior and suggest precautions accordingly.

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How to Cite
, K. S. D. E. (2018). Online Health Monitoring using Household Activity Patterns from Smart Meter Data. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(5), 06–11. Retrieved from