CHS: Medium: Collaborative Research: Managing Stress in the Workplace: Unobtrusive Monitoring and Adaptive Interventions

工作压力是一个严重的问题，对健康，幸福和生产力有直接和负面的影响。 目前测量压力和减轻压力的方法都很有限;测量通常依赖于自我报告或强迫性传感器，而人们通常不会寻求治疗，直到压力达到危险水平（或者根本不会，如果他们害怕别人的判断）。工作场所的常见压力来源是噪音、分心和时间压力。该项目的目标是开发方法来检测压力和提供个性化的放松练习，在真实的时间和工作环境。 为了检测压力，研究团队将应用机器学习来研究工作中常用设备（如网络摄像头、健身追踪器和键盘）的数据如何预测个人的压力水平。 为了减轻压力，该团队将开发一套简短的放松练习和一个系统，该系统使用预测的压力水平来推荐不同的练习，随着时间的推移，学习哪些练习最适合特定的人。 这些预测模型和干预措施将在真实的办公室环境中进行长期研究，验证工作并对实验参与者的健康产生直接影响。 该项目还将对STEM领域代表性不足的群体产生直接的教育影响，并生成其他研究人员可以使用的匿名数据集。该团队将开发实验方法，使用一套代表知识工作和典型工作场所压力源的认知任务（例如，时间压力、噪音、干扰）。 参与者将执行任务并体验压力源，而团队将使用来自热成像的生理信号从商品设备和地面真实压力测量中收集行为数据。 该团队将评估使用不同设备集从感知行为数据中获得的特征如何预测地面真实压力数据以及它如何根据特定压力源而变化。 该团队还将开发一个框架，提供简短的减压练习，促进深呼吸，这是一种有效且可学习的减压技术。 该团队将使用迭代原型来开发新颖的、吸引人的移动的应用程序，这些应用程序使用生物反馈、游戏和音乐来支持呼吸练习;这些应用程序将由基于多臂Bandit的推荐系统提供，该推荐系统考虑当前环境（预测的压力和压力源、一天中的时间、特定的计算机活动）沿着历史锻炼坚持性和结果，以建议有效的锻炼。 压力感知模型和干预框架将通过一系列的实验室和现场研究与信息工作者在一家软件公司，收集压力数据与生态瞬时评估技术，验证调查工具的压力和影响，和采访。

Workplace stress is a serious problem that has a direct and negative impact on health, happiness, and productivity. Current approaches for both measuring stress and reducing it are limited; measurements typically rely on self-report or obtrusive sensors, while people often don't seek treatment until the stress has built to dangerous levels (or at all, if they are afraid of other people's judgments). Common workplace sources of stress are noise, distractions and time pressure. This project's goal is to develop methods both to detect stress and provide personalized relaxation exercises, in real time and in the work context. To detect stress, the research team will apply machine learning to study how well data from commonly available devices at work such as webcams, fitness trackers, and keyboards can predict individuals' stress levels. To reduce stress, the team will develop a suite of brief relaxation exercises and a system that uses predicted stress levels to recommend different exercises, learning over time which ones work best for a particular person. These predictive models and interventions will be tested in a long-term study in a real office environment, both validating the work and providing direct effects on experimental participants' well-being. The project will also have direct educational impacts for groups underrepresented in STEM fields and generate anonymized datasets that other researchers can use. The team will develop experimental methods to reliably extract stress cues from commodity devices, using a suite of cognitive tasks that represent knowledge work and typical workplace stressors (e.g., time pressure, noise, distractions). Participants will perform the tasks and experience stressors while the team collects behavioral data from the commodity devices and ground truth stress measurements using physiological signals derived from thermal imaging. The team will evaluate how well features derived from the sensed behavioral data, using different sets of devices, can predict the ground truth stress data and how it varies based on specific stressors. The team will also develop a framework to deliver brief stress-reduction exercises that promote deep breathing, a proven effective and learnable stress reduction technique. The team will use iterative prototyping to develop novel, engaging mobile apps that use biofeedback, games, and music to support breathing exercises; these will be delivered by a multi-arm bandit-based recommendation system that considers the current context (predicted stress and stressors, time of day, particular computer activities) along with historical exercise adherence and results to suggest effective exercises. The stress sensing models and intervention framework will be validated through a series of lab and field studies with information workers at a software company, collecting stress data in situ with ecological momentary assessment techniques, validated survey instruments for stress and affect, and interviews.