ArticleIEEE ... International Conference on Rehabilitation Robotics : [proceedings]2023
Correcting Temporal Inaccuracies in Labeled Training Data for Electromyographic Control Algorithms.
Article in IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Preliminary Investigation of Myoelectric Control of an Assistive Neck Exoskeleton by Individuals With Amyotrophic Lateral Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Quantifying the Cognitive Demand of Controlling a Prosthesis with a Secondary Detection Response Task.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- A Low-Profile High-Density Electromyography Neckband for Recording Neck Muscle Activity.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Training Movement Velocity Significantly Affects the Performance of Myoelectric Control.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data.Journal of neural engineering · 2024Article
- Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Electromyographic (EMG) control relies on supervised-learning algorithms that correlate EMG to motor intent. The quality of the training dataset is critical to the runtime performance of the algorithm, but labeling motor intent is imprecise and imperfect. Traditional EMG training data is collected while participants mimic predetermined movements of a virtual hand with their own hand. This assumes participants are perfectly synchronized with the predetermined movements, which is unlikely due to reaction time and signal-processing delays. Prior work has used cross-correlation to globally shift and re-align kinematic data and EMG. Here, we quantify the impact of this global re-alignment on both classification algorithms and regression algorithms with and without a human in the loop. We also introduce a novel trial-by-trial re-alignment method to re-align EMG with kinematics on a per-movement basis. We show that EMG and kinematic data are inherently misaligned, and that reaction time is inconsistent throughout data collection. Both global and trial-by-trial re-alignment significantly improved offline performance for classification and regression. Our trial-by-trial re-alignment further improved offline classification performance relative to global realignment. However, online performance, with a human actively in the loop, was no different with or without re-alignment. This work highlights inaccuracies in labeled EMG data and has broad implications for EMG-control applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.