A Comparison Analysis of Conventional Classifiers and Deep Learning Model for Activity Recognition in Smart Homes based on Multi-label Classification
Subject Areas : Machine learning
John
Kasubi
1
(The Local Government Training Institute, Dodoma, Tanzania)
Manjaiah D.
Huchaiah
2
(Mangalore University)
Ibrahim
Gad
3
(Tanta Univeristy)
Mohammad Kazim
Hooshmand
4
(Mangalore University)
Keywords: Conventional Classifiers, Deep Learning Model, Activity Recognition, Smart Homes, Multi-label classification,
Abstract :
Activity Recognition is essential for exploring the various activities that humans engage in within Smart Homes in the presence of multiple sensors as residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to track changes in human behavior as the humans interact with the home's equipment, which may improve healthcare and security issues for the residents. This study presents a research work that compares conventional classifiers such as DT, LDA, Adaboost, GB, XGBoost, MPL, KNN, and DL, focusing on recognizing human activities in Smart Homes using Activity Recognizing Ambient Sensing (ARAS). The experimental results demonstrated that DL Model outperformed with excellent accuracy compared to conventional classifiers in recognizing human activities in Smart Homes. This work proves that DL Models perform best in analyzing ARAS datasets compared to traditional machine learning algorithms.
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