A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Abstract

With the revolution in smart infrastructure in the recent past, the smart healthcare system has been paid more considerable attention. The continuous upgradation of electricity meters to smart electricity devices has probed into a new market of intelligent data analysis services, providing aid to the health care systems. This paper presents a unified framework for extracting user behaviour patterns from home-based smart electricity meter data. The structure allows exploration and integration of frequent pattern growth algorithm for pattern mining and application of a variety of machine learning algorithms for categorizing the activities into manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. To evaluate the proposed framework, the work is implemented on the smart electricity dataset from the United Kingdom by separating the data into four distinct data files meant for the morning, afternoon, evening, and night energy utilization records. The results show a remarkable performance of Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers with kappa statics greater than 0.95 for all time slots data. The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time slots, requiring the immediate attention of concerned healthcare authorities.

Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

“…With the revolution of the medical infrastructure in the recent years, the smart healthcare system has been paid more considerable attention [ 1 ]. Smart healthcare is a novel concept that refers to a set of rules that integrate prevention, diagnosis, treatment, and management.…”

Activity Detection from Electricity Consumption and Communication Usage Data for Monitoring Lonely Deaths

“…IoT Device Utility or Communication Data Activity detection [1,2,4,[11][12][13][14], [6][7][8][9] * [16][17][18][19][20][21] Anomaly detection [3,5,10], [6][7][8][9] * [22][23][24][25][26][27][28][29] * Studies that used both analysis methods.…”

“…Providing an app for a smartphone, as in [13,14], belongs to the method of using an IoT device based on activity detection; • Anomaly detection-based: Abnormal conditions are analyzed by collecting physical health information (heart rate, electrocardiogram, etc.) [3,5,10]. Some cases use both activity detection and anomaly detection.…”

“…Anomaly detection-based: Abnormal conditions are analyzed by collecting physical health information (heart rate, electrocardiogram, etc.) [ 3 , 5 , 10 ].…”

“…Thus, attempts have been made to provide information communication technology (ICT) based services to alleviate this problem. Most of these services monitor the condition of single persons in real-time through the internet of things (IoT) sensors or smartphone apps [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Furthermore, the methods of capturing abnormal states are mainstream.…”