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Course Outline

Foundations of TinyML in Healthcare

  • Core characteristics of TinyML systems
  • Specific constraints and requirements in healthcare
  • Overview of wearable AI architectures

Biosignal Acquisition and Preprocessing

  • Interfacing with physiological sensors
  • Techniques for noise reduction and signal filtering
  • Extracting features from medical time-series data

Developing TinyML Models for Wearables

  • Choosing algorithms suited for physiological data
  • Training models within constrained environments
  • Evaluating model performance on health-specific datasets

Deploying Models on Wearable Devices

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

Power and Memory Optimization

  • Methods to minimize computational load
  • Optimizing data flow and memory consumption
  • Achieving a balance between accuracy and efficiency

Safety, Reliability, and Compliance

  • Regulatory aspects of AI-enabled wearables
  • Ensuring system robustness and clinical usability
  • Implementing fail-safe mechanisms and error handling

Case Studies and Healthcare Applications

  • Wearable systems for cardiac monitoring
  • Activity recognition in rehabilitation settings
  • Continuous tracking of glucose levels and biometrics

Future Directions in Medical TinyML

  • Approaches to multi-sensor fusion
  • Personalized health analytics
  • Next-generation low-power AI processors

Summary and Next Steps

Requirements

  • A foundational understanding of machine learning principles
  • Practical experience with embedded systems or biomedical devices
  • Proficiency in development using Python or C

Target Audience

  • Healthcare professionals
  • Biomedical engineers
  • AI developers
 21 Hours

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