Students will participate in one of three projects during Summer 2025.
Enhancing physical performance through personalized training and assistive devices
H-CORE areas: humans and analytics
Faculty Mentor: Dr. Jessica Avilés
The goal of this project is to examine performance-based metrics that can guide training progression for assistive technology use (e.g., exoskeleton devices) and balance training to individualize training. This data-driven approach will leverage wearable technology to detect small but meaningful changes in an individual’s physical performance and increase training complexity to promote motor learning among individuals learning to use a new wearable technology device or when improving their balance to recover from a trip. Students will have hands-on experiencing conducting human subjects research to investigate how someone’s individual characteristics such as physical fitness, age, gender, and cognitive fitness should be considered in training to reduce injury of a worker and improve performance. Students will also assist with data analysis of common biomechanical outcome measures such as body position, muscle activation, and strength to determine the success of individualized training.
Optimizing worker allocation policies in high-cognitive workload environments
H-CORE areas: humans, systems, and analytics
Faculty Mentor: Dr. Tuğçe Işık
In collaboration with Clemson Vehicle Assembly Center (CVAC) and an industry partner, BMW Manufacturing Co., LLC, this project will implement two phases of research to investigate optimal worker allocation policies in environments with high cognitive workloads: (i) hypotheses development, experimentation, and data analysis, (ii) model development and analysis. In the first phase, the students will conduct controlled experiments at CVAC (located near Greenville, SC) and collect data on the impact of high cognitive workloads on worker performance. CVAC features a fully reconfigurable mock assembly line which will be reconfigured to match the real assembly line at the BMW Manufacturing plant. Manufacturing workers with automotive assembly experience will be recruited as study participants. In the second phase, the students will analyze the data collected through the experiments to develop and parametrize optimization models. The models will be analyzed to develop ideas to improve workflow, reduce worker errors, and increase job satisfaction among manufacturing workers.
Optimizing community access to local parks
H-CORE areas: humans and systems
Faculty Mentor: Dr. Emily Tucker
Equitable access is focused on ensuring that different groups are provided access to resources (e.g,. parks) in a way that the outcomes are similar for all groups. Students will learn about person-centered concepts of equity and the challenges of expressing them mathematically. The first goal of the project will be to study how to appropriately incorporate community-focused objectives in optimization models for park investments. The second goal will be for students to study how stakeholders may interact with optimization models and visualizations and how to revise the models accordingly. Students will get to meet with park experts and consider the real-world impacts of strategic optimization models.