Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment

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.