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Duration 21 hours
Course Outline
Foundations of TinyML in Healthcare
- Defining characteristics of TinyML systems
- Specific constraints and needs in healthcare settings
- Introduction to wearable AI structures
Biosignal Collection and Processing
- Utilizing physiological sensors
- Methods for noise reduction and signal filtering
- Extracting features from medical time-series data
Building TinyML Models for Wearables
- Choosing algorithms suitable for physiological data
- Training models under constrained conditions
- Assessing model performance using health datasets
Implementing Models on Wearable Hardware
- Leveraging TensorFlow Lite Micro for on-device inference
- Incorporating AI models into medical wearables
- Conducting tests and validation on embedded hardware
Optimizing Power and Memory
- Strategies to minimize computational demands
- Enhancing data flow and memory efficiency
- Achieving a balance between accuracy and efficiency
Safety, Reliability, and Compliance
- Regulatory aspects for AI-enabled wearables
- Guaranteeing robustness and clinical applicability
- Implementing fail-safe protocols and error management
Case Studies and Medical Applications
- Wearable systems for cardiac monitoring
- Activity tracking for rehabilitation purposes
- Ongoing monitoring of glucose levels and biometrics
Future Trends in Medical TinyML
- Approaches involving multi-sensor integration
- Personalized health data analysis
- Emerging low-power AI processors
Conclusion and Path Forward
Requirements
- A grasp of fundamental machine learning principles
- Experience working with embedded or biomedical systems
- Knowledge of development using Python or C
Target Audience
- Healthcare practitioners
- 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.
