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Duration 21 hours
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics of TinyML systems
- Constraints and requirements specific to healthcare
- Overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Interfacing with physiological sensors
- Techniques for noise reduction and signal filtering
- Extracting features from medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data
- Training models within resource-constrained environments
- Evaluating model performance on health-related datasets
Deploying Models on Wearable Devices
- Utilizing TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearables
- Conducting testing and validation on embedded hardware
Power and Memory Optimization
- Methods for minimizing computational load
- Optimizing data flow and memory utilization
- Achieving a balance between accuracy and 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 systems for cardiac monitoring
- Activity recognition for rehabilitation purposes
- Continuous tracking of glucose levels and biometrics
Future Directions in Medical TinyML
- Approaches to multi-sensor fusion
- Personalized health analytics
- Next-generation low-power AI chips
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning concepts
- Experience working with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Healthcare professionals
- 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.