Alsubaei Faisal S, Alshdadi Abdulrahman A, Rizwanullah Mohammed
Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, 21959, Jeddah, Saudi Arabia.
Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
Sci Rep. 2025 May 8;15(1):16087. doi: 10.1038/s41598-025-00374-y.
Indoor activity monitoring methods assurance the well-being and security of disabled and aging individuals living in their homes. These models utilize numerous technologies and sensors to monitor day-to-day work like movement, medication adherence, and sleep patterns, and provide valued perceptions of the user's everyday life and entire health. Internet of Things (IoT) based health systems have an important part in medical assistance and help in enhancing data processing and its prediction. Communicating data or reports requires more time and energy, in addition to causing energy problems and greater latency. Currently, numerous studies focus on human activity recognition (HAR) using deep learning (DL) and machine learning (ML) methods, but more effort is needed to enhance HAR models for disabled individuals. Therefore, this article presents a Smart Indoor Monitoring for Disabled People Using an Ensemble of Deep Learning Models in an Internet of Things Environment (SIMDP-EDLIoT) technique. The SIMDP-EDLIoT model is designed to monitor and detect various conditions and activities within indoor spaces for disabled people. Initially, the SIMDP-EDLIoT approach uses linear scaling normalization (LSN) to ensure that the input data is scaled appropriately. Besides, the Improved Osprey Optimization Algorithm (IOOA)-based feature selection is employed to classify the most relevant features, enhancing the efficiency of the system by reducing dimensionality. For monitoring indoor activities, an ensemble of three DL techniques such as bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and conditional variational autoencoder (CVAE) are employed. Experimental study is performed to underscore the importance of the SIMDP-EDLIoT method under the HAR dataset. The comparative analysis of the SIMDP-EDLIoT method demonstrated a superior performance with an accuracy of 98.85%, precision of 97.71%, sensitivity of 97.70%, specificity of 99.24%, and F-measure of 97.70%, outperforming existing approaches across all metrics.
室内活动监测方法保障了居家残疾人和老年人的福祉与安全。这些模型利用多种技术和传感器来监测日常活动，如运动、药物依从性和睡眠模式，并提供有关用户日常生活和整体健康的有价值见解。基于物联网（IoT）的健康系统在医疗辅助中发挥着重要作用，有助于提高数据处理及其预测能力。传输数据或报告不仅需要更多时间和精力，还会引发能源问题和更大的延迟。目前，许多研究聚焦于使用深度学习（DL）和机器学习（ML）方法进行人类活动识别（HAR），但仍需付出更多努力来改进针对残疾人的HAR模型。因此，本文提出了一种在物联网环境中使用深度学习模型集成的残疾人智能室内监测（SIMDP - EDLIoT）技术。SIMDP - EDLIoT模型旨在监测和检测室内空间中残疾人的各种状况和活动。首先,SIMDP - EDLIoT方法使用线性缩放归一化（LSN）来确保输入数据得到适当缩放。此外，采用基于改进鱼鹰优化算法（IOOA）的特征选择来对最相关的特征进行分类，通过降维提高系统效率。为了监测室内活动，采用了双向长短期记忆（BiLSTM）、门控循环单元（GRU）和条件变分自编码器（CVAE）这三种深度学习技术的集成。进行了实验研究以强调SIMDP - EDLIoT方法在HAR数据集下的重要性。SIMDP - EDLIoT方法的对比分析显示出卓越的性能，准确率为98.85%，精确率为97.71%，灵敏度为97.70%，特异性为99.24%，F值为97.70%，在所有指标上均优于现有方法。
