Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs

TL;DR

This paper introduces a new end-to-end framework using Riemannian features and RNNs for continuous high-dimensional finger motion decoding from EMG, enabling real-time control of prostheses and robotic hands.

Contribution

It presents a novel lightweight RNN model with Riemannian features, a new dataset, and demonstrates real-time deployment on consumer hardware, outperforming existing methods.

Findings

TRR achieves lower error rates than state-of-the-art methods.

The framework enables real-time finger motion decoding on a Raspberry Pi.

The EMG-FK dataset provides rich, synchronized EMG and finger kinematics data.

Abstract

Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses, AR/XR interfaces, and teleoperation. However, the complexity of human hand gestures and the entanglement of forearm muscles make accurate recognition intrinsically challenging. Existing approaches typically reduce task complexity by relying on classification-based machine learning, limiting the controllable degrees of freedom and compromising on natural interaction. We present an end-to-end framework for continuous EMG-to-kinematics regression using only consumer-grade hardware. The framework combines an 8-channel EMG armband, a single webcam, and an automatic synchronization procedure, enabling the collection of the EMG Finger-Kinematics dataset (EMG-FK), a 10-h dataset of synchronized EMG and 15 finger joint angles from 20 participants performing rich, unconstrained right-hand motions. We also introduce the Temporal Riemannian Regressor (TRR), a lightweight GRU-based model that uses sequences of multi-band Riemannian covariance features to decode finger motion. Across EMG-FK and the public emg2pose benchmark, TRR outperforms state-of-the-art methods in both intra- and cross-subject evaluation. On EMG-FK, it reaches an average absolute error of $9.79 \deg \pm 1.48$ in intra-subject and $16.71 \deg \pm 3.97$ in cross-subject. Finally, we demonstrate real-time deployment on a Raspberry Pi 5 and intuitive control of a robotic hand; TRR runs at nearly 10 predictions/s and is roughly an order of magnitude faster than state-of-the-art approaches. Together, these contributions lower the barrier to reproducible, real-time EMG-based decoding of high-dimensional finger motion, and pave the way toward more natural and intuitive control of embedded EMG-based systems.