PFI:BIC: iSee - Intelligent Mobile Behavior Monitoring and Depression Analytics Service for College Counseling Decision Support

PFI：BIC：iSee - 用于大学咨询决策支持的智能移动行为监测和抑郁分析服务

• 批准号: 1632051
• 负责人: Mi Zhang
• 金额: $ 99.5万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632051&HistoricalAwards=false
• 关键词: PFI; BIC; iSee; Intelligent; Mobile

Depression is the leading health issue on college campuses in the U.S. Today, college students are dealing with depression at some of the highest rates in decades. Unfortunately, university counseling centers (UCCs), which are the primary access points for students to receive mental health services, are facing significant challenges in meeting the increasing demands. Specifically, clinicians at UCCs still rely on patients' inaccurate and biased self-reported symptoms for depression assessment. In addition, UCCs provide mental health services only during working hours in clinical settings. The lack of service access when needed could leave patients floundering helplessly and lead to lifelong consequences. Furthermore, with tight budgets, clinicians at UCCs have not grown and some UCCs even downsized. As a consequence, more students did not receive timely treatment. This project focuses on designing and developing iSee, a smart device based behavior monitoring and analytics platform. iSee harnesses smartphones/wristbands to extend the reach of mental health care far beyond clinical settings and to deliver timely therapies when needed. Furthermore, the continuously tracked depression symptoms allow UCCs to be more accurately informed with the severity of each patient and thus reduces unnecessary visits so that clinician time can be better utilized. If successful, iSee has the potential to enhance mental health services in thousands of colleges and universities, benefiting millions of college students. Although focusing on depression of college students, the technology can be extended to other mental health conditions such as anxiety, bipolar disorder, dementia, and schizophrenia; adapted to patients beyond college students; and deployed at other settings such as public hospitals and private clinics.iSee consists of a smartphone/wristband sensing system running on the patient side to continuously and passively track patient's daily behaviors using onboard sensors; a behavior analytics engine using machine learning and causality analysis algorithms running on the cloud side to translate behavior sensor data into meaningful analysis results for identifying the patient's depression severity and revealing behavioral causes that lead to the mitigation or the deterioration of the patient's status; and a dashboard running on the clinician side to visualize behavior information as well as analysis results to help clinicians make clinical decisions and conduct treatment. The system would allow clinicians to access an objective, quantitative, and longitudinal record of patients' daily behavior to support evidence-based clinical assessment. This project involves a multi-disciplinary and cross-organizational team of researchers from Michigan State University (lead institution) and Northwestern University (Chicago, IL). The primary industry partner is Microsoft Research (Redmond, WA), which is a large business company in U.S. Michigan State University Counseling Center (East Lansing, MI), which will be the test bed for the integration and evaluation of the iSee smart service system. Finally, the broader context partners include the MSU Office of the Vice President for Student Affairs and Services and MSU Technologies (East Lansing, MI).This award is partially supported by funds from the Directorate for Computer and Information Science and Engineering (CISE), Division of Information and Intelligent Systems (IIS).

抑郁症是美国大学校园里最主要的健康问题。如今，大学生患抑郁症的比例是几十年来最高的。不幸的是，大学咨询中心(UCC)作为学生接受心理健康服务的主要接入点，在满足日益增长的需求方面面临着巨大的挑战。具体地说，UCCS的临床医生仍然依赖患者不准确和有偏见的自我报告症状来评估抑郁。此外，UCC仅在临床环境下的工作时间提供精神健康服务。在需要时缺乏服务可能会让患者无助地挣扎，并导致终身后果。此外，在预算紧张的情况下，UCCS的临床医生没有增长，一些UCCS甚至缩小了规模。结果，更多的学生没有得到及时的治疗。本项目致力于设计和开发iSee，一个基于智能设备的行为监控和分析平台。ISEE利用智能手机/腕带将精神健康护理的覆盖范围远远扩展到临床环境之外，并在需要时提供及时的治疗。此外，持续跟踪的抑郁症状使UCC能够更准确地了解每个患者的严重程度，从而减少不必要的就诊，从而更好地利用临床医生的时间。如果成功，iSee有可能加强数千所高校的心理健康服务，使数百万大学生受益。虽然该技术关注的是大学生的抑郁症，但也可以扩展到其他心理健康状况，如焦虑、躁郁症、痴呆症和精神分裂症；适用于大学生以外的患者；并部署在其他环境中，如公立医院和私人诊所。iSee由运行在患者端的智能手机/腕带传感系统组成，使用机载传感器持续被动地跟踪患者的日常行为；行为分析引擎使用运行在云端的机器学习和因果分析算法，将行为传感器数据转换为有意义的分析结果，以识别患者的抑郁严重程度，并揭示导致患者状态缓解或恶化的行为原因；以及在临床医生端运行的仪表板，用于可视化行为信息以及分析结果，以帮助临床医生做出临床决策和进行治疗。该系统将允许临床医生访问患者日常行为的客观、定量和纵向记录，以支持基于证据的临床评估。这个项目涉及来自密歇根州立大学(牵头机构)和西北大学(伊利诺伊州芝加哥)的一个多学科和跨组织的研究团队。主要的行业合作伙伴是微软研究公司(华盛顿州雷蒙德)，这是美国密歇根州立大学咨询中心(密歇根州东兰辛)的一家大型商业公司，该中心将成为iSee智能服务系统集成和评估的试验台。最后，更广泛的背景合作伙伴包括密歇根州立大学负责学生事务和服务的副校长办公室和密歇根州立大学技术(密歇根州东兰辛)。该奖项由信息和智能系统司(IIS)计算机和信息科学与工程局(CEISE)提供部分资金支持。

项目成果

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study.

探索用户对抑郁管理的移动行为感应技术的需求：定性研究。

• DOI: 10.2196/10139
• 发表时间: 2018-07-17
• 期刊: Journal of medical Internet research
• 影响因子: 7.4
• 作者: Meng J;Hussain SA;Mohr DC;Czerwinski M;Zhang M
• 通讯作者: Zhang M

Deep AutoAugment

• DOI: 10.48550/arxiv.2203.06172
• 发表时间: 2022-03
• 期刊: ArXiv
• 作者: Yu Zheng;Z. Zhang;Shen Yan;Mi Zhang

SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration

• DOI: 10.1109/infocom41043.2020.9155435
• 发表时间: 2020-07
• 期刊: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
• 作者: Shuang Jiang;Zhiyao Ma;Xiao Zeng;Chenren Xu;Mi Zhang;Chen Zhang;Yunxin Liu

FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking

FedMask：通过异构掩码进行联合计算和高效通信的个性化联合学习

• DOI: 10.1145/3485730.3485929
• 发表时间: 2021
• 期刊: ACM Conference on Embedded Networked Sensor Systems (SenSys'21
• 作者: Li, Ang;Sun, Jingwei;Zeng, Xiao;Zhang, Mi;Li, Hai;Chen, Yiran
• 通讯作者: Chen, Yiran

PyramidFL: a fine-grained client selection framework for efficient federated learning

• DOI: 10.1145/3495243.3517017
• 发表时间: 2022-10
• 期刊: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
• 作者: Chenning Li;Xiao Zeng;Mi Zhang;Zhichao Cao