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Computational Support for Longitudinal Well-Being- [electronic resource]
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Computational Support for Longitudinal Well-Being- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100100
- ISBN
- 9798380326544
- DDC
- 020
- 저자명
- Xu, Xuhai Orson.
- 서명/저자
- Computational Support for Longitudinal Well-Being - [electronic resource]
- 발행사항
- [S.l.]: : University of Washington., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(133 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Dey, Anind K.;Mankoff, Jennifer.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약As artificial-intelligent-powered devices have become more embedded in our lives, they offer an unprecedented ability to passively sense our daily behavior at a high resolution. These everyday devices are already equipped with machine learning techniques to monitor our basic health behaviors, such as physical activity and heart rate, and provide suggestions accordingly. However, they are still far from understanding our high-level, longitudinal behaviors, such as mental well-being. Early research about longitudinal behavior modeling and intervention is still facing a set of deployability challenges before being ready for real-world deployment. For behavior modeling, these challenges include interpretability (revealing human-readable insights about behavior), personalization (adapting models to every individual), and generalizability (ensuring models work robustly on new users and contexts). Furthermore, the results and insights of behavior models need to be connected with intervention techniques to influence users' behavior and improve their well-being. With mental well-being as the main application, my research is targeted at these deployability challenges by (1) collecting and releasing the first multi-year passive sensing datasets, (2) developing new behavior modeling techniques that are interpretable, personalized, and generalizable, and (3) designing and deploying a novel intervention technique based on behavior models' insights to improve user well-being. Combining these efforts, I propose the vision of "computational longitudinal well-being", where interactive systems based on everyday devices can precisely and robustly understand, model, and influence long-term human behavior for better health and well-being.
- 일반주제명
- Information science.
- 일반주제명
- Computer science.
- 일반주제명
- Behavioral sciences.
- 키워드
- Human behavior
- 키워드
- Machine learning
- 기타저자
- University of Washington Information School
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016931666
■00520240214100100
■006m o d
■007cr#unu||||||||
■020 ▼a9798380326544
■035 ▼a(MiAaPQ)AAI30418190
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a020
■1001 ▼aXu, Xuhai Orson.
■24510▼aComputational Support for Longitudinal Well-Being▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Washington. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(133 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Dey, Anind K.;Mankoff, Jennifer.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aAs artificial-intelligent-powered devices have become more embedded in our lives, they offer an unprecedented ability to passively sense our daily behavior at a high resolution. These everyday devices are already equipped with machine learning techniques to monitor our basic health behaviors, such as physical activity and heart rate, and provide suggestions accordingly. However, they are still far from understanding our high-level, longitudinal behaviors, such as mental well-being. Early research about longitudinal behavior modeling and intervention is still facing a set of deployability challenges before being ready for real-world deployment. For behavior modeling, these challenges include interpretability (revealing human-readable insights about behavior), personalization (adapting models to every individual), and generalizability (ensuring models work robustly on new users and contexts). Furthermore, the results and insights of behavior models need to be connected with intervention techniques to influence users' behavior and improve their well-being. With mental well-being as the main application, my research is targeted at these deployability challenges by (1) collecting and releasing the first multi-year passive sensing datasets, (2) developing new behavior modeling techniques that are interpretable, personalized, and generalizable, and (3) designing and deploying a novel intervention technique based on behavior models' insights to improve user well-being. Combining these efforts, I propose the vision of "computational longitudinal well-being", where interactive systems based on everyday devices can precisely and robustly understand, model, and influence long-term human behavior for better health and well-being.
■590 ▼aSchool code: 0250.
■650 4▼aInformation science.
■650 4▼aComputer science.
■650 4▼aBehavioral sciences.
■653 ▼aHuman behavior
■653 ▼aBehavior modeling
■653 ▼aMachine learning
■653 ▼aUbiquitous computing
■653 ▼aLongitudinal well-being
■690 ▼a0723
■690 ▼a0984
■690 ▼a0602
■71020▼aUniversity of Washington▼bInformation School.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931666▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
