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Duration 21 hours
Course Outline
Foundations of TinyML in Healthcare
- Key attributes of TinyML systems
- Specific constraints and demands in healthcare
- Overview of AI architectures for wearables
Biosignal Acquisition and Preprocessing
- Interfacing with physiological sensors
- Methods for noise reduction and filtering
- Extracting features from medical time-series data
Developing TinyML Models for Wearables
- Choosing algorithms suitable for physiological data
- Training models within constrained environments
- Assessing performance against health datasets
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference
- Embedding AI models into medical wearables
- Conducting tests and validation on embedded hardware
Power and Memory Optimization
- Strategies for minimizing computational load
- Improving data flow and memory efficiency
- Achieving a balance between accuracy and efficiency
Safety, Reliability, and Compliance
- Regulatory aspects of AI-enabled wearables
- Guaranteeing robustness and clinical applicability
- Implementing fail-safe mechanisms and error handling
Case Studies and Healthcare Applications
- Wearable systems for cardiac monitoring
- Activity recognition in rehabilitation settings
- Continuous tracking of glucose levels and biometrics
Future Directions in Medical TinyML
- Approaches to multi-sensor fusion
- Personalized health analytics
- Emerging low-power AI chips
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning principles
- Hands-on experience with embedded or biomedical systems
- Proficiency in development using Python or C
Target Audience
- Medical professionals
- Biomedical engineers
- AI developers
Testimonials (2)
skills and projects in claude, variety of tools for different purpose
Agnieszka Czypionka
Course - Generative AI in Healthcare: Transforming Medicine and Patient Care
Comprehensiveness about the backround; how to do a prompt.
