TinyML in Healthcare Training in Bangkok, Thailand

The TinyML in Healthcare Training is a practical, industry-focused program designed to teach how TinyML (machine learning on ultra-low-power devices) can be applied to healthcare and medical applications.
The course covers how to design, deploy, and manage on-device AI for wearables, medical sensors, remote patient monitoring, and point-of-care devices, enabling real-time intelligence, privacy-first processing, and low-latency healthcare solutions.

This training is ideal for healthcare technologists, biomedical engineers, IoT teams, medical device developers, researchers, and healthcare innovation leaders.

Introduction to TinyML in Healthcare

• What is TinyML and why it matters in healthcare

• Benefits of on-device AI for medical use cases

• TinyML vs cloud-based healthcare AI

• Key healthcare challenges TinyML can address

Healthcare Sensors & Data Sources

• Physiological sensors (ECG, PPG, SpO2, temperature, motion)

• Wearables and body-area networks

• Medical-grade vs consumer-grade sensors

• Data quality, noise, and reliability considerations

TinyML Pipeline for Healthcare

• End-to-end TinyML workflow in healthcare

• Data collection → preprocessing → model → deployment

• Edge vs cloud roles in healthcare AI

• Designing for safety and reliability

Signal Processing & Feature Extraction

• Time-series data for healthcare signals

• Windowing and segmentation

• Feature extraction for physiological data

• Reducing noise and artifacts

Model Selection for Medical TinyML

• Choosing lightweight models for healthcare

• Accuracy vs power and memory trade-offs

• Classical ML vs small neural networks

• Interpretable models for clinical relevance

Training & Validation for Healthcare Use

• Training with limited and imbalanced datasets

• Cross-validation and testing strategies

• Avoiding overfitting in medical datasets

• Performance metrics for healthcare (sensitivity, specificity)

Model Optimization for Wearables & Devices

• Quantization and compression

• Reducing inference latency

• Power and battery-life optimization

• Memory footprint management

Deployment to Medical & Wearable Devices

• Integrating TinyML into device firmware

• Resource allocation and memory mapping

• On-device inference workflows

• Device testing and validation

Privacy & On-Device Health Data Processing

• Processing sensitive data on-device

• Minimizing cloud transmission

• Privacy-by-design for healthcare AI

• Data anonymization and filtering

Safety, Reliability & Clinical Considerations

• False positives and false negatives in health AI

• Fail-safe system design

• Human-in-the-loop for medical decisions

• Validation and verification concepts

Regulatory & Compliance Overview

• Medical device regulations (high-level overview)

• Data protection and patient privacy principles

• Documentation and audit requirements

• Responsible AI in healthcare

Edge-to-Cloud Integration in Healthcare

• Secure data synchronization

• Remote monitoring dashboards

• Alerting and escalation workflows

• Continuous model improvement loops

Hands-on Exercises

Summary and Conclusion