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Course Development For Applications of Artificial Intelligence and Wearable Devices In Healthcare

This effort describes development of a prospective course which combines artificial intelligence (AI) and wearable devices with a focus on applications such as telehealth, healthcare accessibility, health-focused serious gaming, remote patient monitoring, and point-of-care diagnostics. The course is designed to operate jointly with a partner course at Clemson University and The Citadel. Faculty from The Citadel’s Electrical and Computer Engineering (ECE) and Computer Science Departments, in collaboration with faculty from the Clemson University Bioengineering Department and School of Computing, are working closely to develop and teach this new course. The course operations will occur in a hybrid format in order to allow for remote participation and collaborative teaching between institutions. Technological resources involve canvas (LMS), zoom, cameras, microphones, computing resources, GPUs, wearable sensors, cross reality systems, monitors/projectors for live interaction, along with live interaction for student teams and working groups through Discord. Open-source hardware and software is also being explored for computing, operating systems, wearable devices, machine learning applications, and AI implementation. Additionally, a hybrid cloud and edge AI deployment for operation and student activities is being explored to enhance industry and research applicability. Mentor engagement from industry partners will also integrated for student project teams to provide practitioner experiences from professionals for more applied insight and alterative perspectives. Collaboration with industry will be further enhanced with guest lecturers from industry practitioners and support from professional organizations such as IEEE, ACM and BMES. The interdisciplinary set of topics will touch on biomedical engineering, electrical engineering, computer engineering and computer science. Some potential course topics include wearable sensors, brain computer interface, wearable actuators, electrochemistry, electrophysiology, instrumentation, data cleaning, applied ML and AI concepts, along with human factors, ethical considerations, societal impact and regulatory needs in the healthcare industry. Some learning outcomes considered include define, differentiate, and analyze applications related to the course topics. Additionally, surveys of students for feedback are being integrated at multiple stages of the course development and implementation to allow for continuous improvement and opportunities to better serve learning outcomes. Future work may allow for introduction of topics such as digital twin concepts in healthcare, along with extension of these topics into additional courses in track of interdisciplinary AI in healthcare courses.