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Duration 21 hours
Course Outline
Core Principles of TinyML in Healthcare
- Defining characteristics of TinyML architectures
- Specific constraints and requirements within the healthcare sector
- Introduction to wearable AI system designs
Acquiring and Preprocessing Biosignals
- Interaction with physiological sensing technologies
- Advanced noise reduction and filtering methodologies
- Extracting meaningful features from medical time-series data
Building TinyML Models for Wearable Integration
- Choosing appropriate algorithms for physiological data analysis
- Training models suitable for resource-constrained environments
- Benchmarking performance against health-specific datasets
Model Deployment on Wearable Hardware
- Leveraging TensorFlow Lite Micro for on-device inference
- Embedding AI capabilities into medical wearable devices
- Conducting rigorous testing and validation on embedded systems
Optimizing Power Consumption and Memory Usage
- Strategies for minimizing computational overhead
- Refining data pipelines and memory management
- Achieving an optimal balance between accuracy and efficiency
Ensuring Safety, Reliability, and Regulatory Compliance
- Navigating regulatory landscapes for AI-powered wearables
- Guaranteeing system robustness and clinical applicability
- Implementing fail-safe protocols and error handling mechanisms
Real-World Case Studies and Medical Applications
- Systems for continuous cardiac monitoring via wearables
- Applications of activity recognition in patient rehabilitation
- Tracking glucose levels and other biometric markers continuously
Emerging Trends in Medical TinyML
- Techniques for multi-sensor data fusion
- Advancements in personalized health analytics
- Next-generation low-power AI chipsets
Conclusion and Recommended Next Steps
Requirements
- Fundamental understanding of core machine learning principles
- Practical experience with embedded systems or biomedical device interfaces
- Proficiency in Python or C-based software development
Target Audience
- Clinical and healthcare professionals
- Biomedical engineers
- Artificial Intelligence developers
