Danniel | Xingyu Hu

Handheld sEMG Device

Role: Biomedical Engineering Co-op | Embedded Systems & Signal Processing Lead

Timeline: May–August 2024
Organization: The KITE Research Institute (Toronto Rehab)

Overview

This project focused on redesigning and optimizing a handheld surface electromyography (sEMG) device intended for physiotherapists and occupational therapists. The goal was to transform an early-stage prototype into a clinically viable tool for real-time muscle activity monitoring with improved signal quality, usability, and system performance.

Design Objectives

  • Enable accurate, real-time visualization of sEMG signals in a compact handheld form
  • Improve signal quality through digital filtering and optimized data handling
  • Redesign hardware and firmware for efficient processing and clinician-friendly operation

Key Features

🔹 Hardware Integration

  • Integrated a Nextion 3.5” touchscreen to offload graphical processing and improve frame rate
  • Achieved stable 1kHz sampling rate for reliable EMG acquisition
  • Redesigned the handheld enclosure to support physical controls, electrode ports, and future expansions

🔹 Signal Processing & Firmware

  • Implemented digital filters (bandpass and notch) to reduce interference and boost clarity
  • Structured firmware to maintain real-time acquisition + touch-based UI control
  • Included simple gesture logic (e.g., left-tap to pause, right-tap to return) for in-clinic usability

🔹 Prototyping & Validation

  • Built and validated a working prototype tested with both wet and dry electrodes
  • Captured high-fidelity EMG data from multiple muscles (e.g., biceps, forearm flexors) under controlled conditions

Skills Applied

  • Embedded programming with Arduino IDE + Nextion Editor
  • Digital signal processing (real-time EMG filtering, FFT concepts)
  • 3D modeling and fabrication for handheld electronics
  • UI logic design for touchscreen interactions
  • Team collaboration with clinicians and biomedical engineers

Deliverables

  • Functional sEMG device prototype
  • CAD files, firmware, and internal documentation package
  • Performance benchmarks, including sampling validation and filter comparisons

Future Directions

  • Integrate a higher-performance MCU (e.g., Raspberry Pi with DMA support)
  • Add SD card logging for offline EMG analysis
  • Upgrade to a high-refresh-rate LCD for smoother signal rendering

IP & Confidentiality Note

Detailed firmware structure, analog front-end specs, and internal calibration logic are withheld to protect intellectual property and future research development.