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

University Of Southern California, Los Angeles CA

Investigators

Abstract

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.