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Duration 21 hours
Course Outline
Core Principles of TinyML in Healthcare
- Key attributes of TinyML systems
- Specific limitations and needs within the healthcare sector
- An introduction to wearable AI architectures
Acquiring and Preparing Biosignals
- Interaction with physiological sensors
- Methods for noise reduction and signal filtering
- Extracting meaningful features from medical time-series data
Building TinyML Models for Wearables
- Choosing appropriate algorithms for physiological data
- Training models within constrained environments
- Measuring performance using health-specific datasets
Deploying Models onto Wearable Hardware
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearable devices
- Conducting testing and validation on embedded hardware
Optimizing Power and Memory Usage
- Strategies to lower computational demands
- Enhancing data flow and memory utilization
- Achieving a balance between model accuracy and efficiency
Safety, Reliability, and Compliance
- Regulatory frameworks for AI-powered wearables
- Safeguarding system robustness and clinical usability
- Implementing fail-safe mechanisms and error management
Case Studies and Medical Use Cases
- Systems for continuous cardiac monitoring
- Activity detection in rehabilitation settings
- Ongoing tracking of glucose levels and biometrics
Future Trajectories in Medical TinyML
- Approaches involving multi-sensor fusion
- Tailored health analytics
- Next-generation low-power AI processors
Conclusion and Path Forward
Requirements
- A solid grasp of fundamental machine learning principles
- Hands-on experience with embedded or biomedical hardware
- Proficiency in development using Python or C
Target Audience
- Medical professionals
- Biomedical engineers
- AI software 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.
