BioE 3700: Bioinstrumentation and Imaging Introduction of fundamental topics in bioinstrumentation and bioimaging focused on the acquisition and monitoring of vital signals. Basic principles for the selection and appropriate use of instruments for solving bioengineering and medical problems such as microscopy, magnetic resonance imaging, and ultrasounds, among others, are addressed. Preq: ECE 202 or 307; and MTHSC 208; or consent of instructor.     BioE 4510: Creative Inquiry Please contact Dr. Gilmore to learn more about Creative Inquiry and current projects. Designing with Docs - In bioengineering, the opportunity to collaborate with clinicians in the design of biomedical devices is considered the highlight of any design experience, but usually these design experiences are limited to senior year, if at all. Clinicians are an essential contributor to the design process, in that they are both the users of biomedical devices, and often the first point of contact for problems that occur in their use. Typically, students explore design related issues, and recruit clinicians to support their work. In this new CI, clinical collaborators that have the support of their clinical innovation departments will work with students to create the next generation of biomedical devices. This CI will be open to all undergraduates, and projects will be multi-semester, to support the development of long-term innovations in healthcare. Signals, Sensors, and Machine Learning to Improve Psychotherapy Outcomes - This Creative Inquiry project is an interdisciplinary collaboration led by engineers, a computer scientist, and a social scientist. The team will work together to create training tools for therapists to improve session outcomes, especially using the Motivational Interviewing framework. The team will be assisted by clinical psychologists from the Medical University of South Carolina (MUSC) and Florida State University, who will provide content knowledge of the therapy setting. Engineering and computing students will develop instrumentation and data processing techniques that will allow the therapists to be physiologically monitored, adding important information to the session records, which can be improved for better patient outcomes. Digital Wellness Nurse - Applied Biomedical Sensing and Machine Learning in Nursing - The Digital Wellness Nurse (DWN) is an ongoing collaborative project aimed at developing an interactive, digital assistant for healthcare professionals engaging a diverse patient population. The project will feature the development of an artificially intelligent chat interface that collects patient data from multiple sources, including previous health data, wearable sensor data, and patient interviews. The combination of these data can be used to develop a patient wellness profile, which can be monitored overtime to assess progress toward a healthy lifestyle over time. The DWN will be designed to interact with electronic health record systems to access patient health and medication history. Students participating in this project will engage in human subjects research during the design and implementation of a DWN prototype. Research will include usability studies for an interactive patient software application, machine learning applications to develop autonomous patient interviewing and data management, and wearable sensor integration to enable continuous patient monitoring.