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

作　　者：Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan 

机构地区：[1]Department of Accounting and Finance,Faculty of Business,Middle East University,Amman,11831,Jordan [2]Faculty of Engineering,Al-Balqa Applied University,Salt,19117,Jordan [3]Department of Mathematics,Kongunadu College of Engineering and Technology(Autonomous),Tholurpatti,Trichy,621215,India [4]Department of Computer Science and Software Engineering,Jaramogi Oginga Odinga University of Science and Technology,Bondo,210-40601,Kenya [5]Department of Electronics and Communication Engineering,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences,Chennai,602105,India [6]Department of Electronics and Communication Engineering,Saveetha Engineering College(Autonomous),Chennai,602105,India [7]Department of Computer Science and Engineering,Kongunadu College of Engineering and Technology(Autonomous),Tholurpatti,Trichy,621215,India

出　　处：《Computers, Materials & Continua》2024年第9期4791-4812,共22页计算机、材料和连续体（英文）

摘　　要：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.

关 键 词：Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]