Publication: A machine learning approach to predict the activity of smart home inhabitant
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Date
2021
Authors
Marufuzzaman M.
Tumbraegel T.
Rahman L.F.
Sidek L.M.
Journal Title
Journal ISSN
Volume Title
Publisher
IOS Press BV
Abstract
A smart home inhabitant performs a unique pattern or sequence of tasks repeatedly. Thus, a machine learning approach will be required to build an intelligent network of home appliances, and the algorithm should respond quickly to execute the decision. This study proposes a decision tree-based machine learning approach for predicting the activities using different appliances such as state, locations and time. A noise filter is employed to remove unwanted data and generate task sequences, and dual state properties of a home appliance are utilized to extract episodes from the sequence. An incremental decision tree approach was taken to reduce execution time. The algorithm was tested using a well-known smart home dataset from MavLab. The experimental results showed that the algorithm successfully extracted 689 predictions and their location at 90% accuracy, and the total execution time was 94 s, which is less than that of existing methods. A hardware prototype was designed using Raspberry Pi 2 B to validate the proposed prediction system. The general-purpose input-output (GPIO) interfaces of Raspberry Pi 2 B were used to communicate with the prototype testbed and showed that the algorithm successfully predicted the next activities. � 2021-IOS Press. All rights reserved.
Description
Ambient intelligence; Automation; Decision trees; Domestic appliances; Forecasting; Turing machines; Hardware prototype; Input-output; Machine learning approaches; Noise filters; Prediction systems; Smart homes; Machine learning