| Issue |
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
|
|
|---|---|---|
| Article Number | 03024 | |
| Number of page(s) | 10 | |
| Section | Health Development | |
| DOI | https://doi.org/10.1051/e3sconf/202449103024 | |
| Published online | 21 February 2024 | |
Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Extra Gradient Boost Algorithm
Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India, Pincode: 602105
This work uses innovative logistic regression and extra gradient boost to compare and enhance human activity recognition for walking and sitting.Novel logistic regression and Extra Gradient Boost are applied with distinct training and testing splits to predict human activity identification.From each group, ten sets of samples are selected, yielding a total of twenty samples. About 85% of the Gpower test (g power setup parameters: α=0.05 and power=0.85, ß=0.2) comes from a T test on an independent sample.Compared to Extra Gradient Boost (90.1850%), Innovative logistic regression (95.5680%) has higher accuracy, with a statistically significant value of p = 0.001 (p < 0.05). When compared to Extra Gradient Boost, Innovative logistic regression has higher accuracy.
Key words: Innovative Logistic Regression / Extra Gradient Boost / Machine Learning / Physical abuse / Smartphones / Multiple Cameras
© The Authors, published by EDP Sciences, 2024
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