Dual Attention-Based recurrent neural network and Two-Tier optimization algorithm for human activity recognition in individuals with disabilities

Human activity recognition (HAR) has been one of the active research areas for the past two years for its vast applications in several fields like remote monitoring, gaming, health, security and surveillance, and human-computer interaction. Activity recognition can identify/detect current actions based on data from dissimilar sensors. Much work has been completed on HAR, and scholars have leveraged dissimilar methods, like wearable, object-tagged, and device-free, to detect human activities. The emergence of deep learning (DL) and machine learning (ML) methods has proven efficient for HAR. This research proposes a Dual Attention-Based Two-Tier Metaheuristic Optimization Algorithm for Human Activity Recognition with Disabilities (DATTMOA-HARD) model. The main intention of the DATTMOA-HARD model relies on improving HAR to assist disabled individuals. In the initial stage, the Z-score normalization converts input data into a beneficial format. Furthermore, the binary firefly algorithm (BFA) model is employed for feature selection. Moreover, the proposed DATTMOA-HARD model implements the dual attention bidirectional gated recurrent unit (DABiG) technique for the classification process. Finally, the Tasmanian devil optimizer (TDO)-based hyperparameter selection is accomplished to enhance the detection results of the DABiG model. The experimental evaluation of the DATTMOA-HARD approach is examined under the HAR dataset. The comparison analysis of the DATTMOA-HARD approach portrayed a superior accuracy value of 98.66% over existing methods.