Course Outline
Foundations of TinyML in Healthcare
- Core characteristics of TinyML systems
- Constraints and requirements specific to the healthcare sector
- An overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Interfacing with physiological sensors
- Techniques for noise reduction and signal filtering
- Feature extraction methods for medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data
- Training models within constrained environments
- Performance evaluation on health-related datasets
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearables
- Testing and validation on embedded hardware
Power and Memory Optimization
- Strategies for reducing computational load
- Optimizing data flow and memory utilization
- Balancing model accuracy with operational efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearables
- Ensuring system robustness and clinical usability
- Implementing fail-safe mechanisms and error handling
Case Studies and Healthcare Applications
- Wearable cardiac monitoring systems
- Activity recognition for rehabilitation purposes
- Continuous tracking of glucose levels and biometrics
Future Directions in Medical TinyML
- Approaches to multi-sensor data fusion
- Personalized health analytics
- Next-generation low-power AI processors
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning concepts
- Practical experience with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Healthcare professionals
- Biomedical engineers
- AI 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.
