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|October 6, 2020

Summary

This study investigated the signal quality of the Non-Invasive Adaptive Prosthetics (NinaPro) dataset, finding significant noise contamination that impacts electromyography (EMG) based gesture recognition performance.

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Area of Science:

Biomedical Engineering
Rehabilitation Engineering
Signal Processing

Background:

Publicly available electromyography (EMG) benchmark datasets are crucial for advancing myoelectric control research.
Many research groups rely on these datasets due to limited resources for data collection.
Signal quality is paramount for reliable EMG-based gesture recognition, yet has not been systematically evaluated in popular benchmarks.

Purpose of the Study:

To comprehensively assess the signal quality of the widely used NinaPro EMG dataset.
To quantify noise contamination across NinaPro sub-datasets.
To establish criteria for evaluating EMG dataset quality and analyze its impact on classification performance.

Main Methods:

Quantitative analysis of noise levels within each NinaPro sub-dataset.
Development and application of novel signal quality assessment criteria for EMG data.
Correlation analysis between signal quality metrics and EMG-based gesture recognition accuracy.
Evaluation of the integrity and accuracy of data labels within the NinaPro dataset.

Main Results:

Significant variations in noise contamination were identified across different NinaPro sub-datasets.
Proposed signal quality criteria demonstrated effectiveness in assessing EMG data integrity.
Lower signal quality was found to negatively correlate with classification performance in EMG-based gesture recognition tasks.
Inconsistencies and potential inaccuracies were observed in the dataset labels.

Conclusions:

The NinaPro dataset, despite its widespread use, exhibits considerable signal quality issues that can compromise research findings.
The proposed signal quality criteria offer a valuable tool for researchers selecting and utilizing EMG benchmark datasets.
Improving EMG signal quality and label accuracy in benchmark datasets is essential for the reliable development of myoelectric control systems.