IEEE Access (Jan 2024)
GNet-FHO: A Light Weight Deep Neural Network for Monitoring Human Health and Activities
Abstract
Regular monitoring of physical activities is essential in mitigating the risks associated with diseases like heart problems, obesity, and diabetes. Recent studies have emphasized the significance of Human Activity Recognition (HAR) in tracking physical movements, which aids in enhancing healthcare. The detection of activities could be done by analyzing different patterns and interpreting the signal trends, and variations in the individual performance of activities can lead to inconsistent sensor signals. To address these challenges, this work utilizes Inertial Measurement Unit (IMU) data, converts it into spectrograms through time and frequency analysis, and primarily employs the short-time Fourier transform (STFT) technique. This strategy specifically implements Ghost Neural Network with Fire-Hawk Optimizer (GNet-FHO) to analyze both time-related characteristics from the spectrograms and distinct spatial attributes of each spectrogram. It also effectively identifies spatial correlations among various spectrogram types. Through this method, there is a remarkable improvement in the feature extraction and thereby enhances the accuracy in identifying human activities. Feature selection is done with the help of the FHO, and it is better than the Adam optimizer in context of robust global optimization, effective handling of complex landscapes also the performance is analyzed using the evaluation metrics. Experimental results exhibit that GNet-FHO outperforms other existing algorithms, establishing its efficacy as a lightweight model for human activity recognition. According to our findings, the algorithms demonstrated a success rate of 99.01% and 98.97% when applied to the WISDM smartwatch and smartphone dataset. Additionally, achieved a success rate of 97.6% on the MOTION SENSE dataset and 95.21% on the UCI-HAR dataset. Notably, these findings outperformed those published in previous research that used the identical datasets.
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