TinyML in Healthcare: AI on Wearable Devices Training Course
TinyML is the integration of machine learning into low-power, resource-limited wearable and medical devices.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to implement TinyML solutions for healthcare monitoring and diagnostic applications.
After completing this training, participants will be able to:
- Design and deploy TinyML models for real-time health data processing.
- Collect, preprocess, and interpret biosensor data for AI-driven insights.
- Optimize models for low-power and memory-constrained wearable devices.
- Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs.
Format of the Course
- Lectures supported by live demonstrations and interactive discussion.
- Hands-on practice with wearable device data and TinyML frameworks.
- Implementation exercises in a guided lab environment.
Course Customization Options
- For tailored training that aligns with specific healthcare devices or regulatory workflows, please contact us to customize the program.
Course Outline
Foundations of TinyML in Healthcare
- Characteristics of TinyML systems
- Healthcare-specific constraints and requirements
- Overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Working with physiological sensors
- Noise reduction and filtering techniques
- Feature extraction for medical time-series
Developing TinyML Models for Wearables
- Selecting algorithms for physiological data
- Training models for constrained environments
- Evaluating performance on health datasets
Deploying Models on Wearable Devices
- Using TensorFlow Lite Micro for on-device inference
- Integrating AI models in medical wearables
- Testing and validation on embedded hardware
Power and Memory Optimization
- Techniques for reducing computational load
- Optimizing data flow and memory usage
- Balancing accuracy and efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearables
- Ensuring robustness and clinical usability
- Fail-safe mechanisms and error handling
Case Studies and Healthcare Applications
- Wearable cardiac monitoring systems
- Activity recognition in rehabilitation
- Continuous glucose and biometric tracking
Future Directions in Medical TinyML
- Multi-sensor fusion approaches
- Personalized health analytics
- Next-generation low-power AI chips
Summary and Next Steps
Requirements
- An understanding of basic machine learning concepts
- Experience with embedded or biomedical devices
- Familiarity with Python or C-based development
Audience
- Healthcare professionals
- Biomedical engineers
- AI developers
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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