Automatic recognition of depression based on audio and video:A review
作者机构:Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information SecurityMinistry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied TechnologyFaculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer NetworksShandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward TwoShandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina
出 版 物:《World Journal of Psychiatry》 (世界精神病学杂志)
年 卷 期:2024年第14卷第2期
页 面:225-233页
核心收录:
学科分类:1002[医学-临床医学] 0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学]
基 金:Supported by Shandong Province Key R and D Program,No.2021SFGC0504 Shandong Provincial Natural Science Foundation,No.ZR2021MF079 Science and Technology Development Plan of Jinan(Clinical Medicine Science and Technology Innovation Plan),No.202225054
主 题:Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development
摘 要:Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.