| Issue |
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
|
|
|---|---|---|
| Article Number | 03023 | |
| Number of page(s) | 9 | |
| Section | Health Development | |
| DOI | https://doi.org/10.1051/e3sconf/202449103023 | |
| Published online | 21 February 2024 | |
Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Naive Bayes Algorithm
1 Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University
2 Department of Mechanical Engineering Papua New Guinea University of Technology, Lae, Papua New Guinea University, Chennai, Tamil Nadu, India, Pincode: 602105
* Corresponding author: smartgen2021@gmail.com.
The objective of this study is to compare the Naive Bayes algorithm with Innovative Logistic Regression in order to enhance human activity identification for sitting and walking. To predict human activity, Naive Bayes and Innovative Logistic Regression are used with different training and testing splits. From each group, ten sets of samples are selected, yielding a total of twenty samples. About 80% of the data from an independent sample T test were utilized in the Gpower test (g power setup parameters: α = 0.05 and power = 0.80, β = 0.2). Compared to Naive Bayes (90.7210%), Innovative Logistic Regression (95.5680%) has higher accuracy, with a statistical significance value of P = 0.003 (p < 0.05). When compared to Naive Bayes, Innovative Logistic Regression has higher accuracy.
Key words: Innovative Logistic Regression / Naive Bayes / Machine Learning / Physical abuse / Smartphone / Multiple Cameras
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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