Engagement Detection Based on Analyzing Micro Body Gestures Using 3D CNN  

作　　者：Shoroog Khenkar Salma Kammoun Jarraya 

机构地区：[1]Department of Computer Science,King Abdul-Aziz University,Jeddah,Saudi Arabia [2]MIRACL-Laboratory,Sfax,Tunisia

出　　处：《Computers, Materials & Continua》2022年第2期2655-2677,共23页计算机、材料和连续体（英文）

基　　金：Makkah Digital Gate Initiatives funded this research work under Grant Number(MDP-IRI-8-2020).Emirate of Makkah Province and King Abdulaziz University,Jeddah,Saudi Arabia.https://science.makkah.kau.edu.sa/Default-101888-AR.

摘　　要：This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro body gestures,which will be mapped to emotions and appropriate engagement states.The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames.We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset.The adopted C3D model was used based on two different approaches;as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pretrained model.Our model was tested and its performance was evaluated and compared to the existing models.It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%.The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’engagement levels.

关 键 词：Micro body gestures engagement detection 3D CNN transfer learning e-learning spatiotemporal features 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]