GOALI: Coadaptation of Intelligent Office Desks and Human Users to Promote Worker Productivity, Health and Wellness

该项目的主要目标是进行基础研究，最终使GOALI团队能够开发和测试智能办公工作站（智能办公桌），通过调整姿势，热量和照明设置来优化用户的健康和生产力。 智能办公桌使用可穿戴和工作站安装的传感器来推断人的意图，生理状况和当前任务。该项目推进基础研究，解决如何最好地结合联合收割机传感器数据，机器学习方法和用户与工作站之间的结构化通信，使热，视觉和姿势条件更接近经过验证的最佳实践，同时提高用户满意度和使用系统的意愿。该项目将生成实验数据，人类意图和偏好的特定任务模型，以及开发机器人设备所需的自适应控制算法，该设备将与工人进行物理和直观的交互，以提高他们的身体舒适度和工作效率。该项目意义重大，因为在办公室增加智能工作站有可能改变在工作场所促进和实现健康和福祉的方式。 该项目直接服务于NSF的使命，促进科学，探索人类工人和智能机器人系统之间的互动模式，促进个人工人，他们的雇主和国家的健康，繁荣和福利。该项目通过旨在招募和留住研究中代表性不足的学生的外联活动以及促进创业和创新来支持教育和促进多样性。这个智能工作站将通过持续的、双向的、自适应的感知、反馈和操纵环境参数的过程来学习工人的偏好并塑造工人的行为，这些环境参数有可能直接影响姿势、姿势和姿势。热舒适性和视觉舒适性，并提高工人的生产力。 研究了四项任务。任务1评估用于推断工人的热、视觉和姿势舒适的现有状态的感测和学习方法。感知模式将包括：用于皮肤温度、皮肤电反应和心率的可穿戴传感器;用于温度、湿度、气压、光强度和色温的环境传感器;结构光深度传感器（用于姿势评估）;以及用于记录用户对桌子/椅子高度、风扇/加热器速度/设定点以及光强度和颜色的改变的“被动”传感器。学习方法包括监督学习（由用户对工作站的调整以及使用Likert类量表对用户舒适度的地面真实评估驱动），无监督学习和半监督学习（使用有限的用户反馈来标记数据集群）。系统将在推断用户偏好时考虑任务上下文。任务2检查用户和自主工作站如何最好地协商对本地工作站环境的控制，以优化工人的生产力、健康和福祉。 研究了两个子任务。 第一个探讨了在极端条件下的共享控制（完全用户控制与机器提示或完全工作站控制与手动覆盖）。第二子任务探讨了当人类和工作站完全访问传感器数据时，工人和工作站之间的环境状态的自适应、协商控制方法。任务3将使用焦点小组和用户体验研究来确定最适合促进共享控制和用户/工作站交互的沟通和反馈提示的类型，这些交互将环境条件推向理想状态。任务4整合了前三个任务的结果，以合成一个工作站控制器，这将有助于用户/工作站的协同适应，促进生产力，健康和健康。 然后，该团队将在为期6个月的功效研究中测试共享自主工作站，研究协同适应的人机系统随着时间的推移可以在多大程度上使热，视觉和姿势条件更接近经过验证的最佳实践，同时还可以提高用户满意度和使用该系统的意愿。该项目的成果可能会通过提高个人和社会的工作场所的生产力，健康和福祉产生长期影响。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

The main objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) project is to perform fundamental research that will ultimately allow the GOALI team to develop and test an intelligent office workstation (a smart desk) that will optimize the user's wellbeing and productivity through adjustment of postural, thermal, and lighting settings. The smart desk uses wearable and workstation-mounted sensors to infer human intent, physiological condition and current task. The project advances fundamental research addressing how best to combine sensor data, machine learning approaches and structured communication between the user and the workstation to bring thermal, visual and postural conditions closer to proven best practices over time, while simultaneously improving user satisfaction and willingness to use the system. The project will generate the experimental data, task-specific models of human intent and preferences, and adaptive control algorithms needed to develop a robotic device that will interact physically and intuitively with workers to enhance their physical comfort and workplace productivity. The project is significant because the addition of intelligent workstations in offices has potential to change the way health and wellbeing are promoted and achieved in the workplace. This project directly serves the NSF mission by promoting science that explores modes of interaction between human workers and intelligent robotic systems that advance the health, prosperity and welfare of individual workers, their employers, and the nation. The project supports education and promotes diversity through outreach activities aimed at recruiting and retaining under-represented students in research, as well as by promoting entrepreneurship and innovation.This intelligent workstation will learn worker preferences and shape worker behavior through an ongoing, bi-directional, adaptive process of sensing, feedback and manipulation of environmental parameters that have the potential to directly impact postural, thermal and visual comfort and to increase worker productivity. Four tasks are researched. Task 1 evaluates sensing and learning methods for inferring the worker's existing state of thermal, visual and postural comfort. Sensing modalities will include: wearable sensors for skin temperature, galvanic skin response, and heartrate; environmental sensors for temperature, humidity, air pressure, light intensity and color temperature; a structured light depth sensor (for postural assessment); and "passive" sensors to record user changes to desk/chair height, fan/heater speed/set-points, and light intensity and color. Learning approaches include supervised learning (driven by user adjustments to the workstation as well as ground truth assessments of user comfort using Likert-like scales), unsupervised learning, and semi-supervised learning (using limited user feedback to label data clusters). The system will consider task context when inferring user preferences. Task 2 examines how the user and the autonomous workstation might best negotiate control of the local workstation environment to optimize worker productivity, health and well-being. Two sub-tasks are researched. The first explores shared control under the extreme conditions (full user control with machine cueing or full workstation control with manual overrides). The second sub-task explores approaches for adaptive, negotiated control of environmental state between the worker and the workstation when the human and workstation have full access to the sensor data. Task 3 will use focus group and user experience studies to identify the kinds of communication and feedback prompts best suited to promote shared-control and user/workstation interactions that drive environmental conditions toward the ideal. Task 4 integrates the results from the first three tasks to synthesize a workstation controller that will facilitate user/workstation co-adaptations promoting productivity, health and wellness. The team will then test the shared-autonomy workstation in a 6-month efficacy study examining the extent to which the co-adaptive human/machine system can bring thermal, visual and postural conditions closer to proven best practices over time, while also improving user satisfaction and willingness to use the system. The project outcomes may have long-term impact by improving individual and societal workplace productivity, health and well-being.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.