Smart & Autonomous Built Environments
Smart IoT Desk
Office workers spend most of their working time at their desks, where they are engaged in sedentary behavior, and usually subjected to poor indoor thermal and lighting conditions. Prolonged sitting, and long term exposure to homogeneous temperatures have been linked with reduced metabolism, increased obesity, and increased risk of cardiovascular disease and type two diabetes.
Furthermore, uncomfortable thermal environment and improper lighting conditions have a negative influence on worker productivity and well being. We envision smart desks in the near future to provide a solution to adverse health and productivity impacts that current office work entails. The smart desk uses a wide range of sensors to monitor the environment around the user, as well as the user behavior. The desk uses reinforcement learning to learn and adapt to their thermal, lighting and posture preferences. The desk will control the local thermal and lighting environment around the user based on user preferences while trying to promote health and productivity based on the best practices identified in literature. The goal is to engage users in a bi-directional interaction where the desk promotes exposure to wider range of thermal conditions, and healthier use of sit-stand regimen to improve their productivity and reduce adverse health impacts from the indoor environment.
Worker-Robot Collaboration on Construction Sites
The construction industry is increasingly adapting robots for automating various construction tasks. Unlike other industries, where multiple robots complete various tasks independently, worker–robot collaboration is necessary in construction activities due to specific project and site requirements, such as the need for multiple parallel or sequential activities, exposure to outdoor conditions and dangerous working conditions .
In this project, we aim to contribute to the fundamental understanding of trust-in-automation, specifically understanding how construction workers develop trust-in-automation. We focus on understanding the role of individual, as well as task differences, and how they moderate the development of trust-in-automation. Through this effort, compared with real-life situations, we will systematically collect data on the affordances and hindrances of existing automation and common problems that arise at the individual- and construction site-level. In addition, the environment, developed as part of this project, will serve as a platform for the research community to explore important research questions as they relate to human-machine collaboration, as well as human-machine collaboration’s impact on construction work processes.