Identification of Different Human Actions through Smart Phone Data

Deep Kumar Bangotra¹

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.9 , pp.80-84, Sep-2022

Online published on Sep 30, 2022

Copyright © Deep Kumar Bangotra . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The identification of various human activities utilising data generated from a user`s smart phone is presented in this study. This study uses data from the University of California Machine Learning Repository to identify six human activities. These actions include lying down, sitting down, standing up, walking, and walking both upstairs and downstairs. The Samsung Galaxy S II smart phone`s inbuilt gyroscope, accelerometer, and other sensors are used to gather the data. To arrange the training and testing data sets, the data is randomly split into 7:3 ratios. The Principal Component Analysis method is used to reduce the dimensions of the data. Different Machine Learning models, such the Artificial Neural Network, Random Forest, K-Nearest Neighbor, and Support Vector Machine, are used to categorise activity. Using a confusion matrix and random simulation, a comparative examination of these models` performance and accuracy has been presented in this research paper.

Key-Words / Index Term :
Random Forests, Artificial Neural Networks, k-Nearest Neighbor, Human Activity Recognition, Support Vector Machine, Principal Component Analysis.

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