sEMGxRoboticHand

Recent advances in surface electromyography (sEMG) decoding, such as Meta’s EMG2Pose, EMG2QWERTY datasets, and their associated pretrained models, have demonstrated high-accuracy hand-pose and typing reconstruction. However, these breakthroughs rely on Meta’s proprietary acquisition hardware (sEMG-RD), limiting reproducibility and broader utility for independent research and open development.

Hand Pose & Reconstruction Examples

EMG-RD Wristband & Interaction Setup

Project Description

To address this gap, we present open-sEMG-16, a fully open-source, 16-channel, wrist-wearable sEMG acquisition system designed with specifications roughly matched to Meta’s proprietary platform, including a 4 kHz sampling rate and a high-fidelity 24-bit analog front-end. The goal of this project is to replicate Meta’s sEMG-RD architecture using low-cost, commercially available components, and to evaluate whether comparable or superior performance can be achieved through optimized analog design and modular firmware.

System Architecture

The system integrates dual ADS1298 24-bit ADCs for synchronized multi-channel acquisition, an ESP32-S3 microcontroller for real-time Wi-Fi/BLE streaming, and dry gold-plated pogo-pin electrodes arranged circumferentially around the wrist.

By maintaining compatibility with vEMG2Pose and similar models, open-sEMG-16 enables direct benchmarking and reproducible validation against Meta’s datasets, serving as a practical platform for open, repeatable sEMG research.

Contributors

Emir Sahin
Lia Brahami
Katherine Lambert
Karen Chen Lai

References

[1] EMG2Pose — Meta AI Research (2024) https://arxiv.org/abs/2410.20081

[2] EMG2QWERTY — Meta AI Research (2024) https://arxiv.org/abs/2410.20081

[3] A generic non-invasive neuromotor interface for human-computer interaction (2025) https://www.nature.com/articles/s41586-025-09255-w

[4] Advancing Neuromotor Interfaces by Open Sourcing Surface Electromyography (sEMG) Datasets for Pose Estimation and Surface Typing (2024) https://ai.meta.com/blog/open-sourcing-surface-electromyography-datasets-neurips-2024/