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Duration 21 hours
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics of TinyML systems.
- Specific constraints and requirements in the healthcare sector.
- An overview of wearable AI architectures.
Biosignal Acquisition and Preprocessing
- Interfacing with physiological sensors.
- Techniques for noise reduction and signal filtering.
- Extracting relevant features from medical time-series data.
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data analysis.
- Training models within computationally constrained environments.
- Evaluating model performance on specialized health datasets.
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference.
- Integrating AI models into medical wearable ecosystems.
- Conducting testing and validation on embedded hardware platforms.
Power and Memory Optimization
- Strategies for minimizing computational load.
- Optimizing data pipelines and memory utilization.
- Achieving the optimal balance between model accuracy and efficiency.
Safety, Reliability, and Compliance
- Navigating regulatory considerations for AI-enabled wearables.
- Ensuring system robustness and clinical usability.
- Implementing fail-safe mechanisms and comprehensive error handling.
Case Studies and Healthcare Applications
- Development of wearable cardiac monitoring systems.
- Application of activity recognition in patient rehabilitation.
- Continuous tracking of glucose levels and other biometric indicators.
Future Directions in Medical TinyML
- Approaches to multi-sensor data fusion.
- Advances in personalized health analytics.
- Emerging trends in next-generation low-power AI chips.
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
- Healthcare industry professionals.
- Biomedical engineers.
- AI and software developers.
