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 six 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.
@misc{yang2024benchmarking,
title={Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography},
author={Jehan Yang and Maxwell Soh and Vivianna Lieu and Douglas J Weber and Zackory Erickson},
year={2024}
}