# EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography

arXiv: https://arxiv.org/abs/2410.23625 · HTML: https://mlsanity.com/paper/2410.23625/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 98 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [TinyViT: Fast Pretraining Distillation for Small Vision Transformers](https://mlsanity.com/paper/2207.10666/) (2022)
- [Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning](https://mlsanity.com/paper/1801.07756/) (2018)
- [Domain-invariant Feature Exploration for Domain Generalization](https://mlsanity.com/paper/2207.12020/) (2022)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)
- [Independence in the Home: A Wearable Interface for a Person with Quadriplegia to Teleoperate a Mobile Manipulator](https://mlsanity.com/paper/2312.15071/) (2023)
- [High-density Electromyography for Effective Gesture-based Control of Physically Assistive Mobile Manipulators](https://mlsanity.com/paper/2312.07745/) (2023)
- [Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond](https://mlsanity.com/paper/2210.06295/) (2022)
- [Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning](https://mlsanity.com/paper/2305.08014/) (2023)
- [EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine](https://mlsanity.com/paper/2106.10561/) (2021)
- [HAT: Head-Worn Assistive Teleoperation of Mobile Manipulators](https://mlsanity.com/paper/2209.13097/) (2022)
- [Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification](https://mlsanity.com/paper/2209.05804/) (2022)
- [Analysis of Fatigue-Induced Compensatory Movements in Bicep Curls: Gaining Insights for the Deployment of Wearable Sensors](https://mlsanity.com/paper/2402.11421/) (2024)

## Built on by (most cited first, 7 of 7)

- [Scaling and Distilling Transformer Models for sEMG](https://mlsanity.com/paper/2507.22094/) (2025)
- [EgoEMG: A Multimodal Egocentric Dataset with Bilateral EMG and Vision for Hand Pose Estimation](https://mlsanity.com/paper/2605.05712/) (2026)
- [From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG](https://mlsanity.com/paper/2605.03462/) (2026)
- [MyoSem: Aligning Electromyography with Natural-Language Action Semantics](https://mlsanity.com/paper/2606.00174/) (2026)
- [Conservative Subject Invariant EMG-based Gesture Recognition](https://mlsanity.com/paper/2607.03783/) (2026)
- [EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding](https://mlsanity.com/paper/2609.25582/) (2026)
- [EVFormer: An Egocentric Vision-EMG Bidirectional Attention Model for Bimanual Hand Pose Estimation](https://mlsanity.com/paper/2610.06970/) (2026)

## Abstract (arXiv)

This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn from a different distribution than the training distribution is critical for real-world deployment as a control interface. By predicting the user's intended gesture using EMG signals, we can create a wearable solution to control assistive technologies, such as computers, prosthetics, and mobile manipulator robots. This new out-of-distribution benchmark consists of two major tasks that have utility for building robust and adaptable control interfaces: 1) intersubject classification and 2) adaptation using train-test splits for time-series. This benchmark spans nine datasets--the largest collection of EMG datasets in a benchmark. Among these, a new dataset is introduced, featuring a novel, easy-to-wear high-density EMG wearable for data collection. The lack of open-source benchmarks has made comparing accuracy results between papers challenging for the EMG research community. This new benchmark provides researchers with a valuable resource for analyzing practical measures of out-of-distribution performance for EMG datasets. Our code and data from our new dataset can be found at emgbench.github.io.