Robust Human Activity Recognition using Improved Heuristic Search Algorithm with Deep Autoencoder Model

Robust Human Activity Recognition using Improved Heuristic Search Algorithm with Deep Autoencoder Model
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© 2023 by IJETT Journal
Volume-71 Issue-8
Year of Publication : 2023
Author : L. Maria Anthony Kumar, S. Murugan
DOI : 10.14445/22315381/IJETT-V71I8P213

How to Cite?

L. Maria Anthony Kumar, S. Murugan, "Robust Human Activity Recognition using Improved Heuristic Search Algorithm with Deep Autoencoder Model," International Journal of Engineering Trends and Technology, vol. 71, no. 8, pp. 152-160, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I8P213

Abstract
Human Activity Recognition (HAR) by employing smart home sensors is a topic that is undergoing intense research in the area of ambient-supported living and the basis of ubiquitous computing in smart environments. Recently, HAR majorly utilizes Deep Learning (DL) method since they employ representation learning methods that could automatically produce maximum factors from raw input datasets resulting from the sensor in the absence of human interference and could detect Hidden Layer (HL) patterns in a dataset. The study presents an Improved Manta-Ray Foraging Optimization with Deep Autoencoder (IMRFO-DAE) model for HAR. The presented algorithm majorly recognizes the diverse types of human activities. The presented IMRFO-DAE model pre-processes the human activity data via a standardization approach to obtain this. Next, the IMRFO-DAE method uses the DAE model to perform the Activity Recognition (AR) process. To advance the AR accomplishment of the DAE model, the IMRFO method is applied as a hyperparameter optimizer. Moreover, the IMRFO model is derived by modifying the initialization of the MRFO model by implementing the chaotic concept. An extensive range of simulations was performed to depict the enhanced efficiency of the IMRFO-DAE approach. The simulation results assured the improved outcomes of the IMRFO-DAE approach compared to existing approaches.

Keywords
Human activity recognition, Deep learning, Metaheuristics, MRFO algorithm, Chaotic concept.

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