This project involves the analysis of motion capture data and the development of human motor learning metrics to evaluate a wearable system developed by a company sponsor. Specifically, the undergraduate role is to assist in data collection and analysis. The student must take ethics courses and training to be approved for limited participation in human research and be comfortable interacting with healthy human subjects.

Project is co-advised by Dr Laura Blumenschein and Deva Chan

Biological Characterization and Imaging, Human Factors, Learning and Evaluation, Medical Science and Technology, Other

• Biomedical Engineering
• Mechanical Engineering
• Kinesiology
• Health Science PreProfessional
• Health and Disease
• Occupational Health Science
• Rehabilitation Engineering
• Pre-physical Therapy
• Applied Exercise and Health (Pre)

Human subjects, physiology, data analysis, statistics, motor learning

Mechanical Engineering

Laura Blumenschein

The project will involve reviewing qualitative data to identify how individuals view or experience ethics in engineering. The SURF student will thus learn to conduct educational research with a concerted focus on applying qualitative research methods. Subjects of this research study include faculty members in biomedical, electrical, or computer engineering. The outcome of this research will include better understandings of views and perceptions on ethics in engineering. The ultimate objectives of this work will be to translate these findings to improve engineering ethics instruction.

Learning and Evaluation, Other

• No Major Restriction

Prior course work in engineering ethics; Interest in learning more about education research methods; Good writing and communication skills

Engineering Education

Justin Hess

Automation is being applied to increasingly complex tasks in manufacturing, medical, military applications and more. There is a need for better human automation interaction to prevent the misuse, disuse and abuse of automation. Our main objective is to develop algorithms for cognitive control so that automated and autonomous systems can respond better to, and guide, human behavior such that task performance is maximized. During this SURF 2023 project, the researcher will help with developing experimental platforms involving human-automation interaction utilizing an online quadrotor simulator module. This may include (but is not limited to) control algorithm and heuristic design of automation assistance, development of human automation interaction contexts and tasks, and incorporating psychophysiological sensors for data collection. Previous and current experiments utilizing this and similar platforms have involved modeling trust, modeling self-confidence, modeling risk perception, and improving learning rates.

Big Data/Machine Learning, Human Factors, Learning and Evaluation

• No Major Restriction

Strong coding skills, including experience in HTML, Javascript, MATLAB, and/or Python.

School of Mechanical Engineering

Neera Jain