TinyML in Healthcare: AI on Wearable Devices Training Course
TinyML involves embedding machine learning capabilities into wearable and medical devices that operate on limited power and resources.
This live, instructor-led training, available online or onsite, is designed for intermediate-level practitioners looking to implement TinyML solutions in healthcare monitoring and diagnostic settings.
Upon completion, participants will have the skills to:
- Develop and deploy TinyML models capable of processing health data in real time.
- Gather, clean, and analyze biosensor data to derive AI-powered insights.
- Tune models to perform efficiently within the low-power and limited memory constraints of wearable devices.
- Assess the clinical value, consistency, and safety of outputs generated by TinyML systems.
Course Delivery Format
- Presentations accompanied by live demonstrations and interactive discussions.
- Practical sessions focusing on wearable device data and TinyML frameworks.
- Guided lab exercises for implementation practice.
Customization Possibilities
- Reach out to us to tailor the program to specific healthcare devices or regulatory workflows.
Course Outline
Core Principles of TinyML in Healthcare
- Key features of TinyML systems
- Specific constraints and needs in the healthcare sector
- Introduction to AI architectures for wearables
Acquisition and Preprocessing of Biosignals
- Utilizing physiological sensors
- Methods for noise reduction and signal filtering
- Extracting features from medical time-series data
Building TinyML Models for Wearables
- Choosing suitable algorithms for physiological data
- Training models within constrained environments
- Assessing model performance on health-related datasets
Model Deployment on Wearable Hardware
- Implementing on-device inference with TensorFlow Lite Micro
- Integrating AI models into medical wearables
- Conducting testing and validation on embedded hardware
Optimizing Power and Memory Usage
- Strategies to minimize computational load
- Enhancing data flow and memory management
- Achieving a balance between accuracy and efficiency
Safety, Reliability, and Regulatory Compliance
- Regulatory aspects of AI-enabled wearables
- Guaranteeing robustness and clinical practicality
- Implementing fail-safe mechanisms and error management
Case Studies and Medical Applications
- Systems for wearable cardiac monitoring
- Activity recognition in rehabilitation contexts
- Continuous tracking of glucose levels and biometrics
Future Trends in Medical TinyML
- Approaches to multi-sensor fusion
- Tailored health analytics
- Next-generation low-power AI processors
Conclusion and Recommended Next Steps
Requirements
- Foundational knowledge of machine learning principles
- Practical experience with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Clinicians and healthcare specialists
- Biomedical engineers
- AI developers
Open Training Courses require 5+ participants.
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