Fuzzy association rule mining for recognising daily activities using Kinect sensors and a single power meter

Document Type

Journal Article

Publication Title

Journal of Ambient Intelligence and Humanized Computing



Place of Publication



School of Engineering


Pazhoumand-Dar, H. (2018). Fuzzy association rule mining for recognising daily activities using Kinect sensors and a single power meter. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1497-1515. Available here.


The recognition of activities of daily living (ADLs) by home monitoring systems can be helpful in order to objectively assess the health-related living behaviour and functional ability of older adults. Many ADLs involve human interactions with household electrical appliances (HEAs) such as toasters and hair dryers. Advances in sensor technology have prompted the development of intelligent algorithms to recognise ADLs via inferential information provided from the use of HEAs. The use of robust unsupervised machine learning techniques with inexpensive and retrofittable sensors is an ongoing focus in the ADL recognition research. This paper presents a novel unsupervised activity recognition method for elderly people living alone. This approach exploits a fuzzy-based association rule-mining algorithm to identify the home occupant’s interactions with HEAs using a power sensor, retrofitted at the house electricity panel, and a few Kinect sensors deployed at various locations within the home. A set of fuzzy rules is learned automatically from unlabelled sensor data to map the occupant’s locations during ADLs to the power signatures of HEAs. The fuzzy rules are then used to classify ADLs in new sensor data. Evaluations in real-world settings in this study demonstrated the potential of using Kinect sensors in conjunction with a power meter for the recognition of ADLs. This method was found to be significantly more accurate than just using power consumption data. In addition, the evaluation results confirmed that, owing to the use of fuzzy logic, the proposed method tolerates real-life variations in ADLs where the feature values in new sensor data differ slightly from those in the learning patterns.



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