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Duration 21 hours
Course Outline
Core Principles of TinyML in Healthcare
- Defining characteristics of TinyML systems
- Specific constraints and requirements in healthcare settings
- Architectural overview of wearable AI systems
Biosignal Acquisition and Data Preprocessing
- Interfacing with physiological sensors
- Strategies for noise reduction and signal filtering
- Extracting relevant features from medical time-series data
Building TinyML Models for Wearable Devices
- Selecting appropriate algorithms for physiological data
- Training models within resource-constrained environments
- Benchmarking performance against health datasets
Model Deployment on Wearable Hardware
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearables
- Conducting testing and validation on embedded hardware
Optimizing Power Consumption and Memory Usage
- Methods for minimizing computational overhead
- Refining data flow and memory management
- Achieving a balance between accuracy and efficiency
Safety, Reliability, and Regulatory Compliance
- Navigating regulatory standards for AI-enabled wearables
- Safeguarding robustness and clinical usability
- Implementing fail-safe mechanisms and error handling protocols
Case Studies and Healthcare Implementations
- Wearable cardiac monitoring systems
- Activity recognition for rehabilitation purposes
- Continuous glucose and biometric tracking solutions
Emerging Trends in Medical TinyML
- Approaches to multi-sensor fusion
- Personalized health analytics capabilities
- Next-generation low-power AI processors
Conclusions and Recommendations for Future Development
Requirements
- Foundational knowledge of machine learning concepts
- Practical experience with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Healthcare professionals
- Biomedical engineers
- AI developers
