EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography
This provides a valuable resource for the EMG research community to analyze practical performance measures for robust and adaptable control interfaces, though it is incremental as it builds on existing datasets and methods.
The paper introduces EMGBench, the first benchmark for evaluating out-of-distribution generalization and adaptation in electromyography (EMG) classification algorithms, spanning nine datasets including a new one with a novel wearable device, to address challenges in comparing accuracy results for real-world control interfaces.
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.