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Duration 21 hours
Course Outline
Core Principles of TinyML in Healthcare
- Key features of TinyML systems
- Specific constraints and needs in the healthcare sector
- Introduction to AI architectures for wearables
Acquisition and Preprocessing of Biosignals
- Utilizing physiological sensors
- Methods for noise reduction and signal filtering
- Extracting features from medical time-series data
Building TinyML Models for Wearables
- Choosing suitable algorithms for physiological data
- Training models within constrained environments
- Assessing model performance on health-related datasets
Model Deployment on Wearable Hardware
- Implementing on-device inference with TensorFlow Lite Micro
- Integrating AI models into medical wearables
- Conducting testing and validation on embedded hardware
Optimizing Power and Memory Usage
- Strategies to minimize computational load
- Enhancing data flow and memory management
- Achieving a balance between accuracy and efficiency
Safety, Reliability, and Regulatory Compliance
- Regulatory aspects of AI-enabled wearables
- Guaranteeing robustness and clinical practicality
- Implementing fail-safe mechanisms and error management
Case Studies and Medical Applications
- Systems for wearable cardiac monitoring
- Activity recognition in rehabilitation contexts
- Continuous tracking of glucose levels and biometrics
Future Trends in Medical TinyML
- Approaches to multi-sensor fusion
- Tailored health analytics
- Next-generation low-power AI processors
Conclusion and Recommended Next Steps
Requirements
- Foundational knowledge of machine learning principles
- Practical experience with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Clinicians and healthcare specialists
- 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.
