Research Projects Description and Funding Source

Funding Source: National Science Foundation - NSF Award Number 2124857

Project Title: SCC-IRG Track 1: Advancing Human-Centered Sociotechnical Research for Enabling Independent Mobility in People with Physical Disabilities 

Description:  This Smart and Connected Communities (S&CC) project will advance methods to improve end-to-end mobility for people with physical disabilities who rely on wheelchairs in their daily activities and encounter several barriers to their movement in the built environment. A typical mobility scenario involves navigation (i.e., finding accessible routes) and maneuvering tasks (i.e., parking wheelchair in confined spaces). These scenarios demand substantial effort and pose safety and anxiety risks for people with physical disabilities adversely affecting their quality of life. This project engages a broad group of stakeholders with converging disability perspectives (e.g., veterans with disabilities), patient care expertise, and experience in public service to create a user-centered autonomy that will enable people with physical disabilities to independently control their travel needs. The project scope will focus on individuals without any significant impairment in upper extremity function and/or sensory and cognitive domains, opening the door for future translational research that will extend research outcomes to other groups with diverse abilities. This integrative research project addresses critical knowledge gaps and leverages a participatory design process to: 1) Discover determinants for successful end-to-end mobility system performance from the perspective of people with physical disabilities; 2) Integrate new navigation and maneuvering algorithms to support end-to-end personal mobility of people with physical disabilities; 3) Investigate mechanisms to enhance a symbiotic relationship between users and the end-to-end mobility system; and 4) Explore psychological, social, and economic factors conductive to promoting widespread adoption in communities. 

Funding Source: National Science Foundation - NSF Award Number 2128623 

Project Title: FW-HTF-R: Collaborative Research: Partnering Workers with Interactive Robot Assistants to Usher Transformation in Future Construction Work 

Description:  Construction is a $10 trillion industry that employs about 180 million workers worldwide. However, the future of construction work is at crossroads. First, productivity in construction work has been stagnant relative to other industries (e.g., manufacturing), and the industry has historically been slow to adopt innovations that affect efficiency. Second, it has been difficult to offset the aging and retiring workforce with younger and more diverse workers, causing the workforce supply to fall short of rising demand. This is mainly because construction work tends to be physically strenuous leading to occupational hazards that often force workers to retire early. Robotization has been suggested as a potential solution to these problems. However, the unstructured nature of construction work presents several technical, social and economic impediments that hinder the direct adoption and integration of such innovations by the construction industry. For construction workers, robotic technology can only be transformative if it allows them to channel their passion for the work while avoiding the chronic pain and health outcomes associated with its physical demands. This project investigates if construction work can be conceived as a human-robot partnership, where human workers play the critical role of planning the work, and training and supervising robotic assistants to adapt to presented workspace conditions and perform useful work. The project team is integrating advances in interactive task learning, mixed reality, and reinforcement learning to enable construction workers to naturally collaborate with robot assistants through direct physical interaction and virtual supervision and training. For such a symbiotic human-robot partnership to benefit construction workers and result in widespread deployment, workers need to be equipped with new skills. The project team is exploring new educational and professional development programs to support worker aspirations for upskilling and lifelong learning, and to open avenues for people of diverse abilities to be productive members of the construction workforce. Tight-knit partnerships with industry collaborators will inform the project activities and provide access to construction work sites and training facilities for testing and evaluation.

Funding Source: Center for Connected and Automated Transportation

Project Title: Predicting Driver Takeover Performance in Conditional Automation (Level 3) through Physiological Sensing 

Description:  The National Highway Traffic Safety Administration (NHTSA) calls for fundamental research on “the driver performance profile over time in sustained and short-cycle automation and driver-vehicle interface to allow safe operation and transition between automated and non-automated vehicle operation.” The emerging level 3 autonomous vehicle (AV) has the potential to transform driving because it can perform all aspects of the driving task and allow for complete disengagement of drivers (e.g., sit back and relax) under certain driving scenarios. The vehicle can handle situations that require an immediate response, such as emergency braking. However, this is not fully autonomous, and still requires the driver to be prepared for takeover at all times with a few seconds of warning. Being able to measure and predict the takeover performance (TOP) ahead of time and issue adequate warnings is thus critical to ensure driver comfort, trust, and safety in the system and ultimately acceptance of the technology by different stakeholders. This has not been explored to the extent of establishing complete and irrefutable trust in the autonomous vehicle system and its ability to engage the driver in safe and effective takeover under certain driving scenarios. Therefore, the objective of this project is to perform fundamental research to understand drivers’ capabilities of taking over the vehicle safely and promptly at any time in level 3 automation. This project advances fundamental research in human factors in level 3 AVs. This is achieved through an integrated treatment of the drivers’ TOP measured and predicted through physiological features and driving environment data in level 3 AVs. Thus, the main objective of this research will be to investigate the feasibility of using multimodal physiological features collected from drivers in level 3 AVs under different driving and disengagement scenarios (secondary tasks) to develop a personalized and real-time prediction of TOP. The project will engage a diverse group of students and faculty and develop a research program in an unexplored area of level 3 AVs, leading to substantial advances in how human physiological sensing can be used to understand the driver’s TOP, especially in a personalized manner. Such an understanding can eventually lead to the design of adaptive and personalized alerts that can be integrated in level 3 AVs.

Funding Source: National Science Foundation - NSF Award Number 2025805 

Project Title: FW-HTF-P: Redesigning the Future of Construction Work by Replicating the Master-Apprentice Learning Model in Human-Robot Worker Teams

Description:  The construction industry is ill-famed for its stagnant productivity, use of antiquated work processes, adversarial relationships among stakeholders, and safety and health issues among construction workers. Over time, this has led to a chronic shortage of skilled workers, largely due to an aging and retiring workforce and the reluctance of younger generations or people of different abilities to pursue such careers. More recently, the outbreak of the Covid-19 pandemic has caused serious economic impact and schedule delays on construction projects since it is hard to maintain social-distancing between workers while working in close proximity on construction sites. This further emphasizes the need for construction techniques that can allow workers to perform tasks remotely, allowing for reduction in the number of on-site workers in close proximity to ensure worker health and safety. This FW-HTF planning grant will investigate whether intelligent human-robot teams have the potential to transform future construction work and the profile of future construction workers resulting in new career opportunities and significant benefits to the industry. In these teams, we envision that human workers will use technology to teach co-robots to perform construction work tasks remotely resulting in symbiotic human-robot teams that can be widely deployed in the construction industry. This envisioned approach parallels the classical Master-Apprentice vocational model prevalent in today’s construction industry. The overarching goal of this research planning grant is to explore the feasibility and potential of the outlined vision through engagement with a wide range of stakeholders. The proposed activities for this planning project include: 1) Fact-finding surveys distributed to representatives of construction firms, current construction workers and potential future construction workers (high school students); 2) Technology pilot presentation and feedback from representatives of construction firms and workers; and 3) A comprehensive research program development workshop with expert stakeholder participants from academia and industry. 

Funding Source: National Science Foundation - NSF Award Number 1804321

Project Title: Non-Intrusive Interpretation and Improvement of Multi-Occupancy Human Thermal Comfort through Analysis of Facial Infrared Thermography

Description:  In the U.S. and worldwide, HVAC systems represent the biggest energy end use, accounting for approximately 50 percent of the total energy required to operate residential and commercial buildings. Despite the significant energy footprint of HVAC systems, occupants in the built environment are usually dissatisfied with their thermal comfort. Current human-in-the-loop approaches provide opportunities for occupants to vote on thermal comfort and preferences, thus allowing for HVAC system adjustment based on human feedback. However, these methods have two key limitations: 1) lack of actionable human bio-signal data for robust thermal comfort evaluation; and 2) inability to determine a comfortable setpoint using only intermittent, non-continuous human feedback. This research has two primary remedial objectives. First, we will explore the feasibility of using infrared thermography as a truly non-intrusive method of predicting human thermal comfort preferences in single and multi-occupancy spaces. Second, we will design and validate a robust HVAC control framework for buildings that will synchronously use the analyzed thermography data to adjust its setpoint to improve thermal comfort in indoor spaces and reduce overall dissatisfaction among occupants in multi-occupancy spaces.