Gesture Recognition Wristband

Hardware Machine Learning Embedded Systems

A 4-layer printed circuit wristband using tomography to map electrical impedance for real-time gesture recognition via SVM.

Overview

The Tomographic Gesture Recognition Wristband is a novel wearable device that detects hand gestures in real-time using electrical impedance tomography (EIT). By measuring the electrical properties of the wrist tissue, the device can infer muscle and tendon positions to classify gestures.

Technical Architecture

Hardware Design

  • PCB: Custom 4-layer printed circuit board designed from scratch
  • Electrodes: Array of electrodes for multi-path impedance measurements
  • Microcontroller: Low-power ARM processor for signal processing and wireless communication
  • Power: Rechargeable LiPo battery with extended runtime

Software & Machine Learning

  • Algorithm: Support Vector Machine (SVM) classifier for gesture recognition
  • Training Data: Collected from multiple users performing standardized gestures
  • Feature Extraction: Time-domain and frequency-domain features from impedance signals
  • Accuracy: Achieved reliable classification across 6 distinct gestures

Development Timeline

This was an independent project spanning from December 2021 to August 2023, involving complete end-to-end development:

  1. Research Phase: Literature review of EIT principles and wearable sensing
  2. Hardware Design: Schematic capture, PCB layout, and component selection
  3. Fabrication: PCB manufacturing and assembly
  4. Software Development: Firmware, signal processing, and ML model training
  5. Testing & Iteration: User studies and algorithm refinement

Applications

Potential applications include hands-free device control, accessibility tools for users with limited mobility, and gaming interfaces.

← Back to Projects