SCH: Detecting and mapping stress patterns across space and time: Multimodal modeling of individuals in real-world physical and social work environments

SCH：检测和映射跨空间和时间的压力模式：现实世界物理和社会工作环境中个体的多模态建模

• 批准号: 2204942
• 负责人: Shawn Roll
• 金额: $ 110万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204942&HistoricalAwards=false
• 关键词: SCH; Detecting; mapping; stress; patterns

Stress has been identified as the health epidemic of the 21st century, and office-related work is a significant driver of stress among Americans due to long hours, rapid deadlines, heavy workload, and job insecurity. Yet, office workers are often entirely unaware of the impact of stress until they notice symptoms of declining physical or mental health or well-being, such as musculoskeletal discomfort, headaches, poor sleep, or lack of motivation. Even more problematic, most individuals do not know how their work activities and the physical and social work environments are related to stress and other health outcomes. While stress is almost always treated as unfavorable, stress can be positive. Opportunities exist to better understand how to promote eustress that is energizing and essential for productivity, and minimize distress that leads to negative emotions, disturbed bodily states, strain, and burnout. Thus, this project aims to generate new analytic models to uncover and map the patterns and pathways that influence work-related stress to understand the primary contributing factors to stress across space and time. The project will develop methods for integrating different types of data from the environment, the person, and other existing technologies to identify patterns that inform personalized solutions for improving self-awareness and management of work-related health and well-being. By developing a deeper individualized understanding and detection of eustress and distress, this project will impact and advance workplace health and wellness. The project will serve as a foundation for the development of sensing systems embedded within smart workplaces to automate environmental supports or provide behavioral feedback. These impacts will not only lead to improved worker health and well-being but can support decreased worker absenteeism and improved productivity. Thus, the project has the potential to change the way health and well-being are promoted and achieved in the office by engaging the worker in their health and wellness and ultimately reducing social and financial losses due to stress. The work will also have broader impacts regarding several criteria of NSF interest. It will promote awareness of the effects of the built, social, and work environments on health and well-being to encourage K-12 students to pursue careers in science and engineering. It will enhance the infrastructure for research and education by incorporating findings into the curriculum across multiple disciplines and disseminating findings via publications, presentations, and other media. The project will use a stakeholder-engaged, transactional approach to describe individualized experiences of stress and develop multimodal models using a wide range of bio-behavioral, environmental, and activity engagement sensing technologies to identify the most valuable combinations of data that inform personalized, automated, or technology-supported intervention approaches to stress management as workers engage in their daily work. To build an individually contextualized understanding of stress among office workers, machine learning methods that can operate with heterogeneous and noisy multimodal data streams at multiple temporal resolutions, including enabling unsupervised discovery of behavioral routines will be developed. Individual interviews and ecological momentary assessment (EMA) surveys will be used to characterize each participant, their work, and how they understand the concepts of stress (i.e., distress and eustress), particularly related to their work. Mobile and wearable technologies will be evaluated to understand stress experiences as workers engage in different workspaces (e.g., home, formal, public) across time. Sensing methods that could be embedded within the formal workspace to obtain alternative, complementary, or additional data useful in determining experiences of worker stress will be evaluated for differentiating worker distress from eustress. Specifically, the contribution of the physical environment, task engagement, posture, and worker emotive states to the understanding of stress will be examined. Additionally, through focus groups that will elicit user insights, feedback, and preferences, the work will advance our knowledge about acceptance of technology for health in work settings, and how that interacts with stress/health self-management including privacy, trustworthiness, acceptance, preferred/appropriate methods for feedback or automation. Novel machine learning methods will be developed and employed to predict positive and negative stress from multimodal data that include reference assessments of behavioral traits and baseline states–including those related to stress, affect, and the job–that serve as constructs for modeling.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.

压力已被认为是 21 世纪的健康流行病，由于工作时间长、截止日期快、工作量大和工作不稳定，与办公室相关的工作是造成美国人压力的一个重要因素。然而，办公室工作人员往往完全没有意识到这一点。直到他们注意到身体或心理健康或幸福感下降的症状，例如肌肉骨骼不适、头痛、睡眠不佳或缺乏动力，更严重的是，大多数人不知道他们的工作活动和身体状况如何。和社会工作环境虽然压力几乎总是被视为不利的，但压力也可以是积极的，可以更好地理解如何促进对生产力至关重要的积极压力，并最大限度地减少导致负面情绪和不安的痛苦。因此，该项目旨在生成新的分析模型，以揭示和绘制影响工作相关压力的模式和路径，以了解跨空间和时间的压力的主要影响因素。对于不同的集成类型来自环境、人员和其他现有技术的数据，以确定为个性化解决方案提供信息的模式，从而提高自我意识和工作相关健康和福祉的管理。该项目将影响和促进工作场所的健康和福祉。该项目将作为开发嵌入智能工作场所的传感系统的基础，以实现环境支持自动化或提供行为反馈。这些影响不仅会改善工人的健康和福祉。但可以支持工人缺勤并改善因此，该项目有可能通过让员工参与健康和保健来改变办公室促进和实现健康和福祉的方式，并最终减少因工作压力而造成的社会和经济损失。它将提高人们对建筑、社会和工作环境对健康和福祉的影响的认识，鼓励 K-12 学生从事科学和工程职业。它将加强基础设施。通过将研究结果纳入跨多个学科的课程来进行研究和教育该项目将通过出版物、演示文稿和其他媒体传播研究结果，使用利益相关者参与的交易方法来描述个性化的压力体验，并使用广泛的生物行为、环境和活动参与传感技术来开发多模式模型。最有价值的数据组合，为员工日常工作中的压力管理提供个性化、自动化或技术支持的干预方法。将开发多个时间分辨率的嘈杂多模式数据流，包括实现无监督的行为常规发现，将用于描述每个参与者、他们的工作以及他们如何理解压力的概念。将评估与工作相关的移动和可穿戴技术，以了解员工在不同时间从事不同工作场所（例如家庭、正式、公共场所）时的压力体验。嵌入正式工作空间中以获得可用于确定工人压力体验的替代、补充或附加数据的数据将被评估，以区分工人的痛苦与良性压力，具体来说，是物理环境、任务投入、姿势和工人情绪状态对工人压力的影响。此外，通过焦点小组收集用户的见解、反馈和偏好，这项工作将提高我们对工作环境中健康技术的接受度，以及它如何与压力/健康自我相互作用的知识。管理包括隐私、可信度、接受度、将开发和采用新的机器学习方法来从多模式数据中预测积极和消极的压力，其中包括行为特征和基线状态的参考评估——包括与压力、情感和工作相关的评估——该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Interaction effects of indoor environmental quality factors on cognitive performance and perceived comfort of young adults in open plan offices in North American Mediterranean climate

北美地中海气候开放式办公室室内环境质量因素对年轻人认知表现和感知舒适度的交互影响

• DOI: 10.1016/j.buildenv.2023.110743
• 发表时间: 2023-08
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: Seyedrezaei, Mirmahdi;Awada, Mohamad;Becerik;Lucas, Gale;Roll, Shawn
• 通讯作者: Roll, Shawn

An Engineering View on Emotions and Speech: From Analysis and Predictive Models to Responsible Human-Centered Applications

情绪和言语的工程视角：从分析和预测模型到负责任的以人为本的应用

• DOI: 10.1109/jproc.2023.3276209
• 发表时间: 2023-06
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: Lee, Chi;Chaspari, Theodora;Provost, Emily Mower;Narayanan, Shrikanth S.
• 通讯作者: Narayanan, Shrikanth S.

Ten questions concerning the impact of environmental stress on office workers

环境压力对上班族影响的十个问题

• DOI: 10.1016/j.buildenv.2022.109964
• 发表时间: 2023-02
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: Awada, Mohamad;Becerik;Liu, Ruying;Seyedrezaei, Mirmahdi;Lu, Zheng;Xenakis, Matheos;Lucas, Gale;Roll, Shawn C.;Narayanan, Shrikanth
• 通讯作者: Narayanan, Shrikanth