Citation

Luwe, Yee Jia and Lee, Chin Poo and Lim, Kian Ming (2023) Wearable sensor-based human activity recognition with ensemble learning: a comparison study. International Journal of Electrical and Computer Engineering (IJECE), 13 (4). p. 4029. ISSN 2088-8708

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Abstract

The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.

Item Type:	Article
Uncontrolled Keywords:	Ensemble learning; human activity recognition; light gradient boosting machine; machine learning; random forest
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 May 2023 00:51
Last Modified:	03 May 2023 00:51
URII:	http://shdl.mmu.edu.my/id/eprint/11404

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