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All Journal Bulletin of Electrical Engineering and Informatics
Panathula, Ganesh
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Electrical & Electronics Engineering

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An optimization based deep learning approach for human activity recognition in healthcare monitoring Kalyanasundaram, Aparna; Panathula, Ganesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8000

Abstract

Medical images are comprised of sensor measurements which help detect the characteristics of diseases. Computer-based analysis results in the early detection of diseases and suitable medications. Human activity recognition (HAR) is highly useful in applications related to medical care, fitness tracking, and patient data archiving. There are two kinds of data fed into the HAR system which are, image data and time series data of physical movements through accelerometers and gyroscopes present in smart devices. This study introduced crayfish optimization algorithm with long short term memory (COA-LSTM). The raw data is obtained from three datasets namely, WISDM, UCI-HAR, and PAMAP2 datasets; then, pre-processing helps in removal of unwanted information. The features from pre-processed data are reduced using principal component analysis and linear discriminant analysis (PCA-LDA). Finally, classification is performed using COA-LSTM where, the hyperparameters are fine-tuned with the help of COA. The suggested method achieves a classification accuracy of 98.23% for UCI-HAR dataset, whereas the existing techniques like convolutional neural network (CNN), multi-branch CNN-bidirectional LSTM, CNN with gated recurrent unit (GRU), ST-deep HAR, and Ensem-HAR obtain a classification accuracy of 91.98%, 96.37%, 96.20%, 97.7%, and 95.05%, respectively.
Co-Authors Kalyanasundaram, Aparna