Advanced smart human activity recognition system for disabled people using artificial intelligence with snake optimizer techniques

Human Activity Recognition (HAR) has become an active research area in recent years due to its applicability in various domains and the growing need for convenient facilities and intelligent homes for the elderly. Physical activity tends to decrease as people age, along with their ability to perform day-to-day tasks, which affects both mental and physical health. Several investigators apply deep learning (DL) and machine learning (ML) approaches to recognize human activities, but minimal investigations are concentrated on human activity recognition of older adults. Recently, the DL method has demonstrated excellent performance in classifying human activities using HAR information. Therefore, this study introduces an Advanced Smart Human Activity Recognition for Disabled People Using Deep Learning with a Snake Optimiser (AHARDP-DLSO) approach. The purpose of the AHARDP-DLSO technique is to provide an efficient deep learning-based HAR model designed to detect and classify the daily activities of individuals with disabilities with high precision and adaptability. Primarily, the min-max normalization is utilized for data normalization to ensure consistent input data quality. Furthermore, the AHARDP-DLSO technique utilizes the deep belief network (DBN) model for the classification process. To further enhance performance, the hyperparameter tuning of the DBN method is performed by using the snake optimizer algorithm (SOA) model. The experimental validation of the AHARDP-DLSO method is performed under the WISDM dataset. The comparison study of the AHARDP-DLSO method revealed a superior accuracy value of 95.81% compared to existing models.