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Duration 21 hours
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics and capabilities of TinyML systems.
- Specific constraints and requirements unique to healthcare applications.
- An overview of wearable AI architectures and their components.
Biosignal Acquisition and Preprocessing
- Interfacing with and utilizing physiological sensors.
- Advanced noise reduction and signal filtering techniques.
- Extracting meaningful features from medical time-series data.
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for processing physiological data.
- Training models within the constraints of limited resources.
- Evaluating model performance using diverse health datasets.
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for efficient on-device inference.
- Seamlessly integrating AI models into medical wearable ecosystems.
- Conducting rigorous testing and validation on embedded hardware.
Power and Memory Optimization
- Strategies for minimizing computational load and energy consumption.
- Optimizing data flow and memory management for efficiency.
- Achieving the optimal balance between model accuracy and performance.
Safety, Reliability, and Compliance
- Navigating regulatory considerations for AI-enabled wearables.
- Ensuring system robustness and clinical usability in real-world scenarios.
- Implementing fail-safe mechanisms and robust error handling protocols.
Case Studies and Healthcare Applications
- Implementing wearable cardiac monitoring systems.
- Utilizing activity recognition for rehabilitation support.
- Enabling continuous glucose and biometric tracking solutions.
Future Directions in Medical TinyML
- Exploring multi-sensor fusion approaches for enhanced accuracy.
- Advancing personalized health analytics and predictive models.
- Integrating next-generation low-power AI chips.
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning concepts.
- Practical experience with embedded or biomedical devices.
- Familiarity with Python or C-based development environments.
Target Audience
- Healthcare professionals seeking to integrate AI into clinical workflows.
- Biomedical engineers focused on device innovation.
- AI developers specializing in edge computing and healthcare applications.
