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An advanced deep learning structure for accurate student activity recognition and health monitoring using smartphone accelerometer data | ||
| Health Management & Information Science | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 13 فروردین 1403 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.30476/jhmi.2024.103265.1226 | ||
| نویسنده | ||
| Khosro Rezaee* | ||
| Department of Biomedical Engineering, Meybod University, Meybod, Iran | ||
| چکیده | ||
| Background: The rise of smartphone sensors, especially accelerometers, has expanded the scope of Human Activity Recognition (HAR). HAR plays a key role in monitoring student health by offering real-time insights into physical activity and promoting healthier behaviors. Objective: The aim of this study is to develop an optimized deep learning model to monitor and classify student activities, using accelerometer data for real-time health monitoring. Methods: This study developed and optimized a novel deep learning framework using modified version of Bidirectional Long Short-Term Memory (BiLSTM) networks, enhanced by the Grey Wolf Optimizer (GWO). The BiLSTM framework automates the feature learning process from raw accelerometer data, while GWO optimizes the hyperparameters to improve sequence processing and overall model performance. We employed public datasets, UCI-HAR and WISDM, for validation, using cross-validation to ensure model robustness. The edge computing approach was implemented to enable real-time processing. Results: The proposed BiLSTM-GWO framework achieved a classification accuracy of 97.68%, outperforming existing methods in recognizing student activities. The model showed enhanced performance in distinguishing between activities such as walking, sitting, and stair climbing, significantly reducing misclassification errors. In addition to accuracy, metrics such as precision, recall, and F1 score were evaluated, all showing improvement. GWO optimization also accelerated convergence, enhancing suitability for real-time applications. Conclusions: The integration of edge computing into the framework provides real-time analysis and resource efficiency, making it highly suitable for health monitoring applications in educational settings. | ||
| کلیدواژه‌ها | ||
| Deep Learning؛ Student Activity Recognition؛ Health Monitoring؛ Smartphone Accelerometer؛ BiLSTM Optimization | ||
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آمار تعداد مشاهده مقاله: 45 |
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