Advancing Management and Control of Enclosed Spaces through Human-Centered Artificial Intelligence
Creators: name: Deng, Min
Publisher: name: My University
Year: 2023
Subjects: subject: Smart Building
subject: Digital ID
subject: Human Well-being
subject: Flexible Workplace
subject: Physiological Sensing
subject: Optimization
subject: Civil and Environmental Engineering
subject: Engineering

description: People are expected to spend the majority of their time in enclosed spaces in modern life. The maintenance of safe and healthy environments in these spaces is of paramount importance to human comfort, productivity, and health. Poor management of such spaces might not only adversely affect human well-being but also lead to extra energy consumption and thereby deteriorate the effectiveness of sustainability initiatives in urban areas. This dissertation proposes a human-centered artificial intelligence (AI) framework for the efficient operation and maintenance of sustainable enclosed spaces that can positively impact the well-being and productivity of the occupants. Specifically, the research focuses on two main enclosed spaces where people in developed countries are expected to spend most of their time doing various tasks, buildings, and Autonomous Vehicles (AVs). As a departure point, human-centric data is incorporated with environmental conditions in a holistic way to enhance human-building interaction and improve the efficiency of building management. A generic framework for human-centric real-time monitoring and management of the indoor environment is developed. Since different individuals might have distinct preferences for indoor environments, the concept of activity-based workplaces (ABW) is also incorporated. At first, a joint optimization approach for thermal comfort and energy consumption is used to demonstrate the feasibility of the developed framework. The results indicate that the appropriate adoption of personal models can significantly improve the occupants’ thermal comfort in the building while a large amount of energy can be saved. Second, the scope of indoor well-being is not only limited to human comfort but also extended to mental health and productivity. To obtain the fundamental knowledge, experiments regarding the effects of wearing masks and lighting conditions on mental states such as work engagement and mental health of the occupants based on physiological sensing are conducted. The results reveal that wearing masks might reduce the mental workload (MW) and task performance of the occupants while the effects of lighting levels vary across different subjects. To better incorporate the mental states into the developed framework, personal prediction models for the work engagement are established based on the experimental results using machine learning techniques. A case study concerning both thermal comfort and work engagement is given to demonstrate the scalability of the framework. Based on similar human sensing technologies, the scope of the research has been extended from buildings to AVs aiming at safer driving environments. In L3 AVs, the drivers still need to take over vehicles in some situations due to the limitations of automation technologies. Therefore, the states of the drivers such as how they are engaged in these tasks are essential as they will determine the success of any takeover event. A systematic analysis regarding the effects of takeover behaviors on human physiological responses is conducted. The correlation between different physiological features from humans, takeover scenarios, and vehicles is investigated. Prediction models for takeover readiness are then tested. The results reveal that takeover readiness can be reflected in the physiological data and takeover scenarios. In general, this research provides a new paradigm regarding how humans can be involved in the loop of control of enclosed spaces for smart decision-making. The proposed framework, algorithms, and discoveries can serve as fundamental theories to help with future research to investigate how this knowledge can be extended in the context of smart and connected communities and cities.