{"title":"Poster Abstract: A Wearable Diagnostic Assessment System for Attention Deficit Hyperactivity Disorder","authors":"Xinlong Jiang, Yunbing Xing, Teng Zhang, Wuliang Huang, Chenlong Gao, Yiqiang Chen","doi":"10.1109/CHASE48038.2019.00012","DOIUrl":null,"url":null,"abstract":"Attention Deficit Hyperactivity Disorder (ADHD) is a mental disorder of the neurodevelopmental type. It is characterized by problems of paying attention, excessive activity, or difficulty controlling behavior which is not appropriate for a person’s age. Currently, as ADHD lacks of clear specificity, diagnosis of ADHD still mainly depends on doctors’ experiences and observation. Moreover, the monotonous environment of hospital may makes children feel nervous, which can lead to misdiagnosis. To cope with this problem, we design a contextualized and objective system to support auxiliary diagnosis of ADHD, which has eleven diagnostic scenarios designed according to the description of typical symptoms of ADHD in Diagnostic and Statistical Manual of Mental Disorders (DSM-V). During the testing, multi-source data are acquired, including physiological sensors’ data, motion sensors’ data and tasks related data. Now, Our system is still a primary prototype. In the next, we plan to deploy it in the hospital and collect more data, and research on the auxiliary diagnosis accuracy.","PeriodicalId":137790,"journal":{"name":"2019 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CHASE48038.2019.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a mental disorder of the neurodevelopmental type. It is characterized by problems of paying attention, excessive activity, or difficulty controlling behavior which is not appropriate for a person’s age. Currently, as ADHD lacks of clear specificity, diagnosis of ADHD still mainly depends on doctors’ experiences and observation. Moreover, the monotonous environment of hospital may makes children feel nervous, which can lead to misdiagnosis. To cope with this problem, we design a contextualized and objective system to support auxiliary diagnosis of ADHD, which has eleven diagnostic scenarios designed according to the description of typical symptoms of ADHD in Diagnostic and Statistical Manual of Mental Disorders (DSM-V). During the testing, multi-source data are acquired, including physiological sensors’ data, motion sensors’ data and tasks related data. Now, Our system is still a primary prototype. In the next, we plan to deploy it in the hospital and collect more data, and research on the auxiliary diagnosis accuracy.