emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation
Sasha Salter, Richard Warren, Collin Schlager, Adrian Spurr, Shangchen, Han, Rohin Bhasin, Yujun Cai, Peter Walkington, Anuoluwapo Bolarinwa, Robert, Wang, Nathan Danielson, Josh Merel, Eftychios Pnevmatikakis, Jesse Marshall

TL;DR
This paper introduces emg2pose, a comprehensive benchmark dataset for surface electromyographic hand pose estimation, enabling improved generalization and new control schemes for human-computer interaction in virtual and augmented reality.
Contribution
The paper presents the largest publicly available sEMG hand pose dataset, emg2pose, with diverse users, gestures, and sensor placements, facilitating research on robust sEMG-based hand pose inference.
Findings
Provides high-quality sEMG and pose data from 193 users
Establishes baseline models and evaluation tasks for generalization
Enables research on sensor placement and user variability
Abstract
Hands are the primary means through which humans interact with the world. Reliable and always-available hand pose inference could yield new and intuitive control schemes for human-computer interactions, particularly in virtual and augmented reality. Computer vision is effective but requires one or multiple cameras and can struggle with occlusions, limited field of view, and poor lighting. Wearable wrist-based surface electromyography (sEMG) presents a promising alternative as an always-available modality sensing muscle activities that drive hand motion. However, sEMG signals are strongly dependent on user anatomy and sensor placement, and existing sEMG models have required hundreds of users and device placements to effectively generalize. To facilitate progress on sEMG pose inference, we introduce the emg2pose benchmark, the largest publicly available dataset of high-quality hand pose…
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Taxonomy
TopicsHand Gesture Recognition Systems · Muscle activation and electromyography studies · Stroke Rehabilitation and Recovery
