Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization.

In recent years, wearable devices-based Human Activity Recognition (HAR) models have received significant attention. Previously developed HAR models use hand-crafted features to recognize human activities, leading to the extraction of basic features. The images captured by wearable sensors contain advanced features, allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions. Poor lighting and limited sensor capabilities can impact data quality, making the recognition of human actions a challenging task. The unimodal-based HAR approaches are not suitable in a real-time environment. Therefore, an updated HAR model is developed using multiple types of data and an advanced deep-learning approach. Firstly, the required signals and sensor data are accumulated from the standard databases. From these signals, the wave features are retrieved. Then the extracted wave features and sensor data are given as the input to recognize the human activity. An Adaptive Hybrid Deep Attentive Network (AHDAN) is developed by incorporating a "1D Convolutional Neural Network (1DCNN)" with a "Gated Recurrent Unit (GRU)" for the human activity recognition process. Additionally, the Enhanced Archerfish Hunting Optimizer (EAHO) is suggested to fine-tune the network parameters for enhancing the recognition process. An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model. The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36, 95.25 for recall, 95.48 for specificity, and 95.47 for precision, respectively. The result proved that the developed model is effective in recognizing human action by taking less time. Additionally, it reduces the computation complexity and overfitting issue through using an optimization approach. [ABSTRACT FROM AUTHOR]