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

2026-04-24

AI Summary

This work addresses the challenge of decoding continuous, high-dimensional finger movements from electromyography (EMG) signals, which is hindered by hand complexity and muscular coupling. Existing approaches predominantly rely on classification models, limiting natural human–machine interaction. To overcome this, the authors propose an end-to-end framework for regressing EMG signals directly to finger joint angles, leveraging an 8-channel consumer-grade EMG armband synchronized with a monocular camera to construct a large-scale EMG–finger kinematics (EMG-FK) dataset. They introduce a lightweight Temporal Riemannian Regressor (TRR) that integrates multi-band Riemannian covariance features with a GRU to model temporal dynamics. The method achieves real-time, high-dimensional finger motion decoding using only consumer hardware, attaining mean absolute errors of 9.79° ± 1.48° (within-subject) and 16.71° ± 3.97° (cross-subject), and runs at nearly 10 Hz on a Raspberry Pi 5—nearly an order of magnitude faster than prior approaches.

Technology Category

• Machine Learning: Learning with Manifolds
• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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 $9.79 °\pm 1.48$ in intra-subject and $16.71 °\pm 3.97$ 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.

Problem

• EMG
• finger kinematics
• high-dimensional motion decoding
• continuous estimation
• hand gesture recognition

Innovation

• Riemannian features
• EMG-to-kinematics regression
• Temporal Riemannian Regressor
• high-dimensional finger motion
• real-time EMG decoding