Repositories

All of our work is open source. Explore the hardware designs, firmware, software, and documentation that make up the OpenMuscle ecosystem.

Have a FlexGrid V4 band? Start here

Everything a new band needs lives in the repositories: the app installers, a step-by-step first day, and a reference for the band's buttons and lights. Questions go to the GitHub issues of the repository the question is about.

Getting started

Install the app, give the band your Wi-Fi, record, train and predict. Step by step, about an hour the first time.

Kit cheat sheet (PDF)

Four pages: what is on the board, what the light means, the recording routine and the training words.

Band reference

Every button, light pattern and screen of the FlexGrid V4 band, with a one-page quick card to print.

Troubleshooting

The band is not found, the heatmap is flat, Record will not start, Train is grayed out: what to check.

OpenMuscle-Hub

Central documentation and project hub for the OpenMuscle ecosystem.

DocsHub MIT

OpenMuscle-FlexGrid

60-sensor (15x4) forearm band: KiCad designs for the flexible sensor sheet and the rigid ESP32-S3 board. V4 is the current board.

HardwareESP32-S3 CERN-OHL-S

OpenMuscle-LASK5

Label Acquiring System K5 — handheld device for capturing ground-truth finger positions for ML training.

HardwareML Training CERN-OHL-S

OpenMuscle-Software

The OpenMuscle Studio PC app: finds bands on your Wi-Fi, records with webcam or LASK5 labels, trains finger-prediction models and runs them live. Installers for Windows, macOS and Linux, guides and the kit cheat sheet.

PythonStudio app MIT

FlexGridV4-Firmware

MicroPython firmware for the FlexGrid V4 band: phone-based Wi-Fi setup, discovery, streaming, the status light and the pre-ship kit check. Release v4.0.0 is what the first kit bands run.

MicroPythonFlexGrid V4 MIT