CHS: Small: Computational Modeling of Human Rhythms to Improve Health and Quality of Life

CHS：小：人类节律的计算模型，以改善健康和生活质量

• 批准号: 2023762
• 负责人: Afsaneh Doryab
• 金额: $ 44.82万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-17 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023762&HistoricalAwards=false
• 关键词: CHS; Small; Computational; Modeling; Human

According to recent research, balance is a significant contributor to health and well-being. Health challenges are associated with work and personal imbalances, in the form of overload, that exceed human capability to manage their own natural pace. Part of these overloads are due to the imposition of activities and mis-timed or incompatible demands on biological and psychological needs, or lack of attention to synchronization or receptivity of mental and physical states when meeting work and life demands. Advice about life management and health is often generic (for example, have 8 hours sleep a day) rather than tailored to individual lifestyles. This research proposes to improve health and wellbeing in individuals through awareness of their personal rhythms, which are repeated cycles of internal and external events including biological, mental, social, and environmental. The investigators seek to design and evaluate a data analytic and modeling method to make people aware of what they could be doing at any given time to align with their biological clock and to achieve high performance in daily life and work without burning out or developing stress-related disorders. The research provides an intelligent platform for collecting longitudinal data and modeling human rhythms to enable detection of current, and prediction of future states in each rhythm to provide optimal action recommendations and interventions. The work leverages advances in wearable devices, sensing technology, and online sources to proactively collect and analyze physiological, psychological, behavioral, social, and environmental data to identify personal rhythms, to explore their relationship to positive physical, mental, and behavioral outcomes, and to provide people with tools to reason about these relationships and to improve outcomes. In addition to advancing the state of the art in modeling rhythmic patterns, the research will demonstrate the impact of rhythm-aware technology in changing people?s perceptions and behavior and directing them towards a more balanced and successful life. Integration of personal rhythms in daily lives of individuals will transform societal activities and the overall performance of people and contributes to creating a healthier society.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

根据最近的研究，平衡是健康和幸福的重要贡献者。健康挑战与工作和个人不平衡有关，以超负荷的形式，超出了人类管理自己自然节奏的能力。这些超负荷的部分原因是强加的活动和错误的时间或不相容的生物和心理需求的要求，或缺乏注意的同步或接受心理和身体状态时，满足工作和生活的要求。关于生活管理和健康的建议通常是通用的（例如，每天有8小时的睡眠），而不是针对个人的生活方式。这项研究建议通过了解个人的节奏来改善个人的健康和福祉，这些节奏是内部和外部事件的重复周期，包括生物，心理，社会和环境。研究人员试图设计和评估一种数据分析和建模方法，使人们意识到他们可以在任何给定的时间做什么，以与他们的生物钟保持一致，并在日常生活和工作中实现高性能，而不会耗尽或发展与压力相关的疾病。该研究提供了一个智能平台，用于收集纵向数据并对人体节律进行建模，以检测每个节律的当前状态并预测未来状态，从而提供最佳的行动建议和干预措施。这项工作利用可穿戴设备、传感技术和在线资源的进步，主动收集和分析生理、心理、行为、社会和环境数据，以识别个人节奏，探索它们与积极的身体、心理和行为结果的关系，并为人们提供工具来推理这些关系并改善结果。除了推进节奏模式建模的最新技术，这项研究还将展示节奏感知技术在改变人们方面的影响。的看法和行为，并引导他们走向一个更平衡和成功的生活。将个人节奏融入个人的日常生活将改变社会活动和人们的整体表现，并有助于创造一个更健康的社会。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Deep Reinforcement Learning Verification: A Survey

• DOI: 10.1145/3596444
• 发表时间: 2023-05
• 期刊: ACM Computing Surveys
• 影响因子: 16.6
• 作者: Matthew Landers;Afsaneh Doryab

Sounds of Health: Using Personalized Sonification Models to Communicate Health Information

健康之声：使用个性化的发声模型来传达健康信息

• DOI: 10.1145/3570346
• 发表时间: 2022
• 期刊: Wearable and Ubiquitous Technologies
• 作者: Clark, Matthew;Doryab, Afsaneh
• 通讯作者: Doryab, Afsaneh

A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams

用于根据移动和可穿戴数据流对生物行为节律进行建模的计算框架

• DOI: 10.1101/2020.08.10.244020
• 发表时间: 2021
• 期刊: ACM transactions on intelligent systems and technology
• 影响因子: 5
• 作者: Yan, Runze

A Computational Framework for Modeling Cyclic Human Behaviors from Multi-Modal Sensor Data

根据多模态传感器数据对人类循环行为进行建模的计算框架

• 发表时间: 2023
• 期刊: Libra ETD Repository
• 作者: Yan, Runze;Doryab, Afsaneh
• 通讯作者: Doryab, Afsaneh

Machine Learning Modeling of Adaptive and Maladaptive Personality Traits from Passively Sensed Behavior

被动感知行为的适应性和适应不良人格特征的机器学习建模

• DOI: 10.31234/osf.io/s3wj8
• 发表时间: 2021
• 期刊: Future generation computer systems
• 作者: Yan, Runze Ringwald