Edge AI for Healthcare: TinyML for Medical Wearables
Biosignal processing for devices like ECG patches, glucose monitors, fitness bands
Skills you will gain:
About Program:
Edge AI for Healthcare: TinyML for Medical Wearables is a technical, hands-on workshop focused on the development of lightweight, on-device machine learning models that run on resource-constrained wearable health devices.
Participants will learn to build applications such as heart rate anomaly detection, fall prediction, sleep stage classification, and respiratory monitoring, using platforms like TensorFlow Lite for Microcontrollers, Arduino Nano BLE Sense, Edge Impulse, and Raspberry Pi Pico.
Aim: To equip healthcare innovators and engineers with the tools, technologies, and design frameworks to develop AI-powered medical wearables using TinyML and Edge AI, enabling real-time, low-latency, and power-efficient healthcare monitoring.
Program Objectives:
- Introduce TinyML’s potential for next-gen medical devices
- Enable participants to prototype real-time, privacy-preserving AI solutions
- Foster the development of affordable, accessible, and intelligent health wearables
- Promote cross-functional skills across AI, embedded systems, and healthcare
- Empower early adoption of decentralized health monitoring technologies
What you will learn?
Day 1: Model Compression Basics
- Why Model Compression Matters
- Challenges of deploying deep learning models on edge devices
- Trade-offs: Accuracy vs. efficiency
- Compression Techniques Overview
- Pruning (structured/unstructured)
- Quantization (8-bit, binary networks)
- Knowledge distillation basics
- Hands-on Compression Lab
- Applying pruning and quantization using TensorFlow Lite or PyTorch Mobile
- Benchmarking latency and model size
Day 2: On-Device Arrhythmia Classification – Case Study
- Introduction to ECG & Arrhythmia Detection
- ECG signal properties and common arrhythmia types
- Importance of real-time edge inference in cardiology
- End-to-End Model Workflow
- Data preprocessing and labeling (MIT-BIH dataset)
- Model selection (CNN, LSTM for time-series classification)
- Deployment & Evaluation
- Model deployment on microcontrollers (Arduino, EdgeTPU)
- Performance metrics: accuracy, latency, power use
Day 3: Privacy-Preserving Federated Updates
- Fundamentals of Federated Learning
- Decentralized model training across edge devices
- Comparison with traditional cloud-based learning
- Ensuring Data Privacy
- Differential privacy in federated updates
- Homomorphic encryption & secure aggregation techniques
- Use Case Simulation
- Implementing a federated training round using PySyft or Flower
- Simulating data from multiple hospitals for arrhythmia classification
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Students & Researchers in AI, biomedical engineering, or healthcare technologies
- Ph.D. Scholars working on TinyML, federated learning, or wearable health monitoring
- Faculty & Academicians teaching AI, embedded systems, or digital health
- MedTech Developers & Engineers building real-time, low-power medical AI devices
- AI/ML Professionals interested in model compression and on-device inference
- Healthcare Innovators focused on privacy-preserving, intelligent wearable solutions
Career Supporting Skills
Program Outcomes
- Gain hands-on experience in developing AI models for edge healthcare devices
- Understand real-time biosignal processing for wearable sensors
- Build and deploy ML pipelines for constrained devices
- Learn to balance performance, accuracy, and power efficiency
- Receive a professional certificate in TinyML for Medical Wearables