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Course Outline

Foundations of TinyML in Healthcare

  • Defining the characteristics of TinyML systems
  • Addressing healthcare-specific constraints and requirements
  • Surveying wearable AI architectures

Biosignal Acquisition and Preprocessing

  • Utilizing physiological sensors effectively
  • Applying noise reduction and filtering techniques
  • Extracting features from medical time-series data

Developing TinyML Models for Wearables

  • Choosing appropriate algorithms for physiological data
  • Training models within constrained environments
  • Performance evaluation using health datasets

Deploying Models on Wearable Devices

  • Leveraging TensorFlow Lite Micro for on-device inference
  • Integrating AI models into medical wearables
  • Conducting testing and validation on embedded hardware

Power and Memory Optimization

  • Strategies for minimizing computational load
  • Optimizing data flow and memory consumption
  • Achieving a balance between accuracy and efficiency

Safety, Reliability, and Compliance

  • Navigating regulatory considerations for AI-enabled wearables
  • Guaranteeing robustness and clinical usability
  • Implementing fail-safe mechanisms and error handling

Case Studies and Healthcare Applications

  • Wearable cardiac monitoring systems
  • Activity recognition for rehabilitation purposes
  • Continuous glucose and biometric tracking

Future Directions in Medical TinyML

  • Exploring multi-sensor fusion approaches
  • Personalized health analytics
  • Next-generation low-power AI chips

Summary and Next Steps

Requirements

  • A solid grasp of fundamental machine learning concepts
  • Practical experience with embedded systems or biomedical devices
  • Proficiency in Python or C-based development

Target Audience

  • Clinical and healthcare professionals
  • Biomedical engineers
  • AI developers and engineers
 21 Hours

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