Course Outline
Foundations of TinyML in Healthcare
- Core characteristics of TinyML systems
- Healthcare-specific constraints and operational requirements
- Overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Interaction with physiological sensors
- Techniques for noise reduction and signal filtering
- Feature extraction for medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data
- Training models under constrained computational environments
- Evaluating model performance on health-specific datasets
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models within medical wearables
- Testing and validating on embedded hardware platforms
Power and Memory Optimisation
- Strategies for minimising computational load
- Optimising data flow and memory utilisation
- Balancing model accuracy against energy efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearables
- Ensuring system robustness and clinical usability
- Implementing fail-safe mechanisms and error handling protocols
Case Studies and Healthcare Applications
- Wearable cardiac monitoring systems
- Activity recognition in rehabilitation settings
- Continuous glucose and biometric tracking
Future Directions in Medical TinyML
- Multi-sensor fusion approaches
- Personalised health analytics
- Next-generation low-power AI chipsets
Summary and Next Steps
Requirements
- Foundational understanding of machine learning principles
- Hands-on experience with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Clinical and healthcare professionals
- Biomedical engineers
- AI and software developers
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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.
