TinyML in Healthcare: AI on Wearable Devices Training Course
TinyML involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.
This live, instructor-led training (available online or onsite) is designed for intermediate-level practitioners aiming to implement TinyML solutions for healthcare monitoring and diagnostic use cases.
Upon completion, participants will be equipped to:
- Design and deploy TinyML models for the real-time processing of health data.
- Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
- Optimize models for the low-power and memory-constrained nature of wearable devices.
- Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML.
Course Format
- Lectures complemented by live demonstrations and interactive discussions.
- Hands-on practice utilizing wearable device data and TinyML frameworks.
- Implementation exercises conducted within a guided lab environment.
Customization Options
- For training tailored to specific healthcare devices or regulatory workflows, please contact us to customize the program.
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics of TinyML systems
- Specific constraints and requirements in healthcare settings
- Overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Working with physiological sensors
- Techniques for noise reduction and filtering
- Feature extraction for medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data
- Training models for constrained environments
- Evaluating performance on health-related datasets
Deploying Models on Wearable Devices
- Utilizing TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearables
- Testing and validating on embedded hardware
Power and Memory Optimization
- Strategies to reduce computational load
- Optimizing data flow and memory utilization
- Balancing accuracy with efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearables
- Ensuring system robustness and clinical usability
- Implementing fail-safe mechanisms and error handling
Case Studies and Healthcare Applications
- Wearable cardiac monitoring systems
- Activity recognition in rehabilitation settings
- 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
- Fundamental understanding of machine learning concepts
- Practical experience with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Healthcare professionals
- Biomedical engineers
- AI developers
Open Training Courses require 5+ participants.
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