UNWIND – A Mobile Application that Provides Emotional Support for Working Women

Priyanka Kugapriya, Mayuriya Manohara, Keerthiga Ranganathan, Dineshgaran Kanapathy, A. Gamage, Arshad Anzar
{"title":"UNWIND – A Mobile Application that Provides Emotional Support for Working Women","authors":"Priyanka Kugapriya, Mayuriya Manohara, Keerthiga Ranganathan, Dineshgaran Kanapathy, A. Gamage, Arshad Anzar","doi":"10.1109/ASIANCON55314.2022.9909084","DOIUrl":null,"url":null,"abstract":"Depression is a common phenomenon affecting more than 264 million people worldwide. It is one of the leading causes of disability and a major contributor to the overall global burden of disease. Around twice as many women are affected by mental illness compared to men. This situation has worsened during the pandemic. The need to balance both work life and personal life has put them under immense pressure. Even though the diagnosis of mental illness almost exclusively depends on doctor-patient communication, it has its own set of disadvantages such as patient denial, recall bias, subjective biases, time-consuming and inaccuracy and it is a long-term health problem that needs to be continuously monitored and managed. Considering this social problem, we have planned to develop an Emotional Support Mobile application UNWIND – using modern technological concepts of machine learning and artificial intelligence. Which focuses especially on working women and would include several functionalities: a Chabot to detect mental health status in real-time and to provide counseling, an internal activities tracker to find the correlation between changes in lifestyle and mental health, an improvement tracker of the user’s current mental state using facial recognition and also Recommendation system with the support group, which recommends the most suitable professional counselors to the user as per their preferences and enabling into the support group to provide with necessary treatments and consultation at greater accuracy.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"35 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASIANCON55314.2022.9909084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Depression is a common phenomenon affecting more than 264 million people worldwide. It is one of the leading causes of disability and a major contributor to the overall global burden of disease. Around twice as many women are affected by mental illness compared to men. This situation has worsened during the pandemic. The need to balance both work life and personal life has put them under immense pressure. Even though the diagnosis of mental illness almost exclusively depends on doctor-patient communication, it has its own set of disadvantages such as patient denial, recall bias, subjective biases, time-consuming and inaccuracy and it is a long-term health problem that needs to be continuously monitored and managed. Considering this social problem, we have planned to develop an Emotional Support Mobile application UNWIND – using modern technological concepts of machine learning and artificial intelligence. Which focuses especially on working women and would include several functionalities: a Chabot to detect mental health status in real-time and to provide counseling, an internal activities tracker to find the correlation between changes in lifestyle and mental health, an improvement tracker of the user’s current mental state using facial recognition and also Recommendation system with the support group, which recommends the most suitable professional counselors to the user as per their preferences and enabling into the support group to provide with necessary treatments and consultation at greater accuracy.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
UNWIND -一个为职业女性提供情感支持的移动应用程序
抑郁症是一种普遍现象,影响着全球超过2.64亿人。它是导致残疾的主要原因之一,也是造成全球总体疾病负担的主要因素。受精神疾病影响的女性大约是男性的两倍。这种情况在大流行期间更加恶化。平衡工作生活和个人生活的需要给他们带来了巨大的压力。尽管精神疾病的诊断几乎完全依赖于医患沟通,但它也有自己的一系列缺点,如患者否认、回忆偏差、主观偏见、耗时和不准确,这是一个需要持续监测和管理的长期健康问题。考虑到这个社会问题,我们计划开发一个情感支持移动应用程序UNWIND -使用机器学习和人工智能的现代技术概念。它特别侧重于职业妇女,并将包括以下几个功能:一个实时检测心理健康状况并提供咨询的Chabot,一个内部活动跟踪器,发现生活方式变化与心理健康之间的相关性,一个使用面部识别的用户当前心理状态改善跟踪器,以及与支持小组的推荐系统,它可以根据用户的喜好推荐最合适的专业咨询师,并使支持小组能够更准确地提供必要的治疗和咨询。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Distributed Multi-Sensor DCNN & Multivariate Time Series Classification Based technique for Earthquake early warning Cross Technology Communication between LTE-U and Wi-Fi to Improve Overall QoS of 5G System Prediction of Ayurvedic Herbs for Specific Diseases by Classification Techniques in Machine Learning Face Mask Detection Using Machine Learning Techniques Closed-form BER Expressions of QPSK Modulation over NOMA-PNC Parallel Relay Channels
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1