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Course Outline
Foundational Concepts of TinyML in Healthcare
- Defining characteristics of TinyML ecosystems
- Specific constraints and requirements in healthcare settings
- Insights into wearable AI architectural patterns
Acquisition and Processing of Biosignals
- Interfacing with physiological sensors
- Strategies for noise mitigation and signal filtering
- Extracting meaningful features from medical time-series data
Building TinyML Models for Wearables
- Choosing appropriate algorithms for physiological data analysis
- Training models tailored for resource-constrained environments
- Assessing model performance using health-related datasets
Implementing Models on Wearable Hardware
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearable devices
- Conducting testing and validation on embedded platforms
Optimizing for Power Efficiency and Memory Usage
- Methods to minimize computational overhead
- Streamlining data flow and memory management
- Achieving a balance between model accuracy and efficiency
Ensuring Safety, Reliability, and Compliance
- Addressing regulatory requirements for AI-enabled wearables
- Safeguarding robustness and clinical utility
- Designing fail-safe mechanisms and error handling protocols
Case Studies and Practical Healthcare Applications
- Wearable systems for continuous cardiac monitoring
- Activity recognition technologies in rehabilitation contexts
- Continuous tracking of glucose levels and biometric metrics
Emerging Trends in Medical TinyML
- Approaches to multi-sensor data fusion
- Personalized health analytics frameworks
- Advances in next-generation low-power AI chipsets
Recap and Pathways for Further Learning
Requirements
- A solid grasp of fundamental machine learning principles
- Practical experience with embedded systems or biomedical equipment
- Proficiency in development using Python or C-based languages
Target Audience
- Medical professionals
- Biomedical engineers
- Artificial Intelligence developers
21 Hours