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Duration 21 hours
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics of TinyML systems
- Specific constraints and requirements unique to the healthcare sector
- Introduction to wearable AI architectures
Biosignal Acquisition and Preprocessing
- Utilizing physiological sensors for data collection
- Methods for noise reduction and signal filtering
- Extracting meaningful features from medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data streams
- Training models within constrained computational environments
- Performance evaluation using diverse health datasets
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearable hardware
- Conducting testing and validation on embedded platforms
Power and Memory Optimization
- Strategies to minimize computational load
- Optimizing data flow and memory consumption
- Achieving a balance between model accuracy and operational efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearable devices
- 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 applications in rehabilitation
- Continuous glucose and biometric tracking systems
Future Directions in Medical TinyML
- Approaches to multi-sensor fusion
- Development of personalized health analytics
- Emerging low-power AI chip technologies
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning principles
- Practical experience with embedded systems or biomedical equipment
- Proficiency in Python or C-based development environments
Target Audience
- Medical and healthcare professionals
- Biomedical engineers
- AI and machine learning developers
