{"title":"Automatic recognition of depression based on audio and video: A review.","authors":"Meng-Meng Han, Xing-Yun Li, Xin-Yu Yi, Yun-Shao Zheng, Wei-Li Xia, Ya-Fei Liu, Qing-Xiang Wang","doi":"10.5498/wjp.v14.i2.225","DOIUrl":null,"url":null,"abstract":"<p><p>Depression is a common mental health disorder. With current depression detection methods, specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment. Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized. Specialized physicians usually require extensive training and experience to capture changes in these features. Advancements in deep learning technology have provided technical support for capturing non-biological markers. Several researchers have proposed automatic depression estimation (ADE) systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening. This 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.</p>","PeriodicalId":23896,"journal":{"name":"World Journal of Psychiatry","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10921287/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5498/wjp.v14.i2.225","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
Abstract
Depression is a common mental health disorder. With current depression detection methods, specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment. Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized. Specialized physicians usually require extensive training and experience to capture changes in these features. Advancements in deep learning technology have provided technical support for capturing non-biological markers. Several researchers have proposed automatic depression estimation (ADE) systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening. This 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.
期刊介绍:
The World Journal of Psychiatry (WJP) is a high-quality, peer reviewed, open-access journal. The primary task of WJP is to rapidly publish high-quality original articles, reviews, editorials, and case reports in the field of psychiatry. In order to promote productive academic communication, the peer review process for the WJP is transparent; to this end, all published manuscripts are accompanied by the anonymized reviewers’ comments as well as the authors’ responses. The primary aims of the WJP are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in psychiatry.