Status: Archived (Legacy Hardware)
License: CERN-OHL-S v2.0

The OpenMuscle-Band is the original prototype of the OpenMuscle project—a wearable forearm band equipped with 12 pressure sensors designed to detect muscle contractions and predict finger movements. This open-source hardware was developed to provide biometric data for machine learning applications in prosthetics and human-computer interaction.

• Sensors: 12 Hall Effect Sensors (49E) arranged in 6 dual-input sensor cells
• Microcontrollers: 3 × ESP32-C3 modules
• Connectivity: Wi-Fi (UDP transmission)
• Sampling Rate: Up to 1200 samples per second
• Construction: 3D-printed enclosures with integrated springs and magnets
• Firmware: MicroPython

• BOM/: Bill of Materials detailing all components used
• KiCad/: PCB design files for the sensor array
• STLs/: 3D-printable files for enclosures and mounts
• Schematics/: Electrical schematics of the system

This repository serves as an archive for the original OpenMuscle-Band design. Active development has moved to the OpenMuscle GitHub Organization, where you'll find:

• OpenMuscle-FlexGrid: A modular 60-sensor wearable, successor to the OM12
• OpenMuscle-LASK5: Labeling device with joystick and buttons
• OpenMuscle-Software: MicroPython firmware, communication protocols, and ML hooks

For historical data and machine learning scripts, refer to the legacy repository.

Electromaker:
🏆 2nd Place — Electromaker of the Month, April 2024
Electromaker Winners – March 2024

Hackaday Articles Featuring OpenMuscle:

• Forearm Muscle Contraction Sensor Is Useful Component For Open Source Prosthetics
• Hackaday Prize 2023: Finger Tracking Via Muscle Sensors
• Hackaday Prize 2023: LASK4 Watches Those Finger Wiggles

To replicate or study the OpenMuscle-Band:

1. Hardware Assembly:

   • Print the enclosures from the STLs/ directory.
   • Assemble the sensor cells with Hall Effect Sensors, magnets, and springs.
   • Connect the sensors to the ESP32-C3 modules as per the schematics.

2. Firmware Installation:

   • Flash MicroPython onto each ESP32-C3 module.
   • Upload the appropriate boot.py and main.py scripts to handle data acquisition and transmission.

3. Data Collection:

   • Use the band to collect muscle contraction data.
   • Transmit data via UDP to a host machine for processing.

4. Machine Learning (Optional):

   • Utilize the scripts from the legacy repository to train models that predict finger movements based on sensor data.

As this is an archived repository, we are not accepting contributions. For ongoing development and contributions, please visit the OpenMuscle GitHub Organization.

This project is licensed under the CERN Open Hardware License v2.0. See the LICENSE file for details.