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 Duration 21 hours

Course Outline

Foundations of TinyML in Healthcare

  • Key characteristics of TinyML systems
  • Specific constraints and requirements in the healthcare sector
  • Overview of AI architectures for wearable devices

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

  • Selecting appropriate algorithms for physiological data
  • Training models within constrained resource environments
  • Assessing performance using health-specific datasets

Deploying Models on Wearable Devices

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

Power and Memory Optimization

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

Safety, Reliability, and Compliance

  • Regulatory considerations for AI-enabled wearables
  • Ensuring system robustness and clinical usability
  • Implementing fail-safe mechanisms and error handling protocols

Case Studies and Healthcare Applications

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

Future Directions in Medical TinyML

  • Approaches to multi-sensor data fusion
  • Personalized health analytics and insights
  • 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 hardware
  • Proficiency in Python or C-based programming

Target Audience

  • Healthcare practitioners
  • Biomedical engineers
  • AI and machine learning developers

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