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Course Outline
TinyML Fundamentals in the Medical Sector
- Defining the key attributes of TinyML ecosystems
- Addressing unique constraints and standards in healthcare
- Examining architectures for wearable AI
Capturing and Refining Biosignals
- Interacting with physiological sensor arrays
- Applying noise suppression and signal filtering methods
- Extracting relevant features from medical time-series data
Building TinyML Models for Wearable Technology
- Choosing appropriate algorithms for biological data
- Training models within constrained computational environments
- Benchmarking model performance on health-related datasets
Implementing Models on Wearable Hardware
- Leveraging TensorFlow Lite Micro for on-device inference
- Embedding AI models into medical wearable devices
- Conducting testing and validation on embedded hardware
Enhancing Power and Memory Efficiency
- Strategies to minimize computational overhead
- Optimizing data pipelines and memory allocation
- Achieving a balance between model accuracy and efficiency
Ensuring Safety, Reliability, and Regulatory Compliance
- Navigating regulations for AI-integrated wearables
- Guaranteeing system robustness and clinical usability
- Implementing fail-safe protocols and error management
Real-World Case Studies and Medical Applications
- Designing wearable systems for cardiac monitoring
- Utilizing activity recognition in patient rehabilitation
- Implementing continuous glucose and biometric surveillance
Emerging Trends in Medical TinyML
- Exploring multi-sensor fusion techniques
- Developing personalized health analytics
- Integrating next-generation low-power AI processors
Wrap-Up and Forward Planning
Requirements
- Foundational knowledge of core machine learning principles
- Practical experience with embedded systems or biomedical equipment
- Proficiency in Python or C-based programming
Target Audience
- Healthcare practitioners
- Biomedical engineers
- Artificial intelligence developers
21 Hours