BMEG-E 512 Digital Health Technologies
3 credits
- Prerequisite(s): None
- Delivery: On-Campus
Description
This course covers principles of human physiology and digital health innovations integrating AI, wearable devices, big data, and mobile platforms. Students capture, analyze, and use physiological data for personalized medicine, remote monitoring, and diagnostics. They engage in projects applying IoT and data-driven approaches to improve healthcare delivery and outcomes.
Topics
Introduction to human physiology
- Principles of human physiology
- Physiological systems relevant to healthcare
Fundamentals of digital health
- Digital health technologies
- AI, IoT, and wearable devices in healthcare
Physiological signals and feature extraction
- Types of physiological signals (e.g., ECG, EEG)
- Feature engineering and extraction
Supervised and unsupervised learning
- Machine learning fundamentals
- Algorithms for healthcare data
Wearable technologies overview
- Wearable sensors
- Applications in healthcare
IoT and big data in healthcare
- IoT infrastructure for healthcare
- Big data pipelines and analytics
Machine learning with ECG applications
- Machine learning techniques for ECG data
- Detecting cardiac abnormalities
Deep learning for EEG applications
- Deep learning for EEG analysis
- Applications in mental health
Wearable sensor technologies and applications
- Advances in wearable sensors
- Biochemical and mechanical sensors
IoT applications: telemedicine and monitoring
- Remote monitoring systems
- Telemedicine for elderly care
Data analytics for wearable health solutions
- Analyzing wearable sensor data
- Data-driven healthcare insights
Security and ethical considerations in digital health
- Cybersecurity in healthcare
- Ethical considerations in AI
Emerging topics: generative AI and healthcare
- Generative AI in diagnostics and treatment
- Explainable AI in healthcare
Final integration of digital health concepts
- System design for healthcare solutions
- Cross-disciplinary collaboration strategies
Learning Outcomes
- Analyze physiological signals to extract features and assess their relevance to healthcare.
- Design systems integrating wearable sensors, machine learning, and IoT for data-driven healthcare applications.
- Evaluate supervised and unsupervised learning techniques for processing physiological data.
- Develop workflows for analyzing wearable sensor data to improve healthcare outcomes.
- Design IoT and telemedicine solutions to solve healthcare monitoring challenges.
- Synthesize knowledge of AI, IoT, and wearable technologies to create innovative healthcare solutions.
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.