Embedded AI · Sensor Processing
Real-Time EMG Classification System
Embedded classification pipeline using EMG sensing, microcontroller/edge compute, and hardware acceleration for real-time gesture recognition.
Teensy 4.1Raspberry Pi 5Hailo-8LONNXPythonsEMG
Overview
This page is the working case-study shell. We’ll replace this text with the exact problem, requirements, constraints, and scope from your project.
The goal is to make the engineering readable in under a minute, then provide enough depth for a technical interviewer to keep digging.
System Architecture
Myo sEMG Sensors → Signal Acquisition → Embedded Processing → Trained Model → Hailo-8L Edge Inference → Gesture / Control Output
My Contribution
- Built the end-to-end signal and inference pipeline.
- Integrated embedded hardware and edge-AI acceleration.
- Converted/deployed the trained model for real-time inference.
Photos, Schematics & Test Evidence
PLACEHOLDER
Add real hardware photos, block diagrams, scope captures, ADS plots, terminal screenshots, CAD, or test data here.
Add real hardware photos, block diagrams, scope captures, ADS plots, terminal screenshots, CAD, or test data here.
Integration & Debugging
We’ll add real examples of data-path, model-deployment, communication, or hardware-integration problems that required debugging.
Results & Validation
Current resume claims approximately 90% classification accuracy and approximately 15 ms inference latency. We’ll verify the exact architecture and metrics before publishing them here.