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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Domain Human Recognition Techniques using Deep Learning

Author 1: Seshaiah Merikapudi
Author 2: Murthy SVN
Author 3: Manjunatha. S
Author 4: R. V. Gandhi

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130684

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 6, 2022.

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Abstract: As a key research subject in the fields of health and human-machine interaction, human activity recognition (HAR) has emerged as a major research focus over the past few decades. Many artificial intelligence-based models are being created for activity recognition. However, these algorithms are failing to extract spatial and temporal properties, resulting in poor performance on real-world long-term HAR. A drawback in the literature is that there are only a small number of publicly available datasets for physical activity recognition that contain a small number of activities, owing to the scarcity of publicly available datasets. In this paper, a hybrid model for activity recognition that incorporates both convolutional neural networks (CNN) are developed. The CNN network is used for extracting spatial characteristics, while the LSTM network is used for learning time-related information. Using a variety of traditional and deep machine learning models, an extensive ablation investigation is carried out in order to find the best possible HAR solution. The CNN approach can achieve a precision of 90.89%, indicating that the model is suitable for HAR applications.

Keywords: Human recognition; deep learning; hybrid model; CNN; HAR

Seshaiah Merikapudi, Murthy SVN, Manjunatha. S and R. V. Gandhi, “Domain Human Recognition Techniques using Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130684

@article{Merikapudi2022,
title = {Domain Human Recognition Techniques using Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130684},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130684},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Seshaiah Merikapudi and Murthy SVN and Manjunatha. S and R. V. Gandhi}
}


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