Enhancing indoor monitoring of visually impaired people using temporal convolutional network with optimization model in IoT environment

The Internet of Things (IoT) has emerged as a powerful technology in various fields, including healthcare, assisting the elderly and disabled individuals. Solution-based IoT is widely utilized in healthcare support in diverse aspects of their daily lives and activities. These IoT-based assistive systems are crucial in uplifting the quality of life for disabled individuals and older adults. Furthermore, IoT-based interior navigation methods assist with easy accessibility and movement in areas, ensuring that disabled people can find objects with greater ease. Machine learning (ML) and deep learning (DL) approaches are efficiently beneficial for detecting indoor objects for visually challenged people. This paper proposes the Metaheuristic Optimization-Driven Attention Mechanism for Enhancing Indoor Monitoring of Visually Impaired People (MOAM-EIMVIP) technique. The main intention of the MOAM-EIMVIP technique is to develop an IoT-based DL model for indoor monitoring of disabled individuals. Initially, the data pre-processing stage applies min-max normalization to convert input data into a suitable format. The sine-cosine algorithm (SCA) is employed to select the feature process to detect and choose the most relevant features from input data. Moreover, the temporal convolutional network-attention mechanism (TCNA) model is implemented for classification. Finally, the Eurasian oystercatcher optimizer (EOO) method alters the hyperparameter tuning of the TCNA model optimally and results in higher classification execution. An extensive simulation is performed to highlight the performance of the MOAM-EIMVIP approach under the HAR dataset. The experimental analysis of the MOAM-EIMVIP approach portrayed a superior accuracy value of 99.30% over existing methods.