Summary of Emg2pose: a Large and Diverse Benchmark For Surface Electromyographic Hand Pose Estimation, by Sasha Salter et al.
emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation
by 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
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the emg2pose benchmark, a large-scale dataset for surface electromyography (sEMG) based hand pose inference. The goal is to enable reliable and always-available control schemes for human-computer interactions in virtual and augmented reality. Currently, computer vision approaches require cameras and struggle with occlusions, lighting, and limited field of view. Wearable sEMG presents a promising alternative, but existing models require hundreds of users and device placements to generalize effectively. emg2pose addresses this challenge by providing a dataset of high-quality hand pose labels and wrist sEMG recordings from 193 users, 370 hours, and 29 stages with diverse gestures. The paper also provides competitive baselines and challenging tasks evaluating real-world generalization scenarios: held-out users, sensor placements, and stages. This work has the potential to significantly enhance the development of sEMG-based human-computer interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making computers understand what our hands are doing without needing cameras or special lighting. Right now, computer vision approaches have limitations like not working well with occlusions or bad lighting conditions. Wearable sensors that measure muscle activity (sEMG) could be a better solution, but we need to overcome the challenge of generalizing these models across different people and settings. To help with this, the researchers created a large dataset called emg2pose that includes hand pose labels and sEMG recordings from many users doing various gestures. This dataset can help machine learning experts develop more accurate and reliable algorithms for understanding human-computer interactions. |
Keywords
» Artificial intelligence » Generalization » Inference » Machine learning
