ArticleScientific data2023
High-density Surface and Intramuscular EMG Data from the Tibialis Anterior During Dynamic Contractions.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A Flexible Ionically Conductive Biopolymer Hydrogel Interface for Physiological Signal Acquisition: A Chitosan-Glycerol-PVA Composite.Materials (Basel, Switzerland) · 2026Article
- A novel SE-ResNet architecture for continuous estimation of wrist and hand movements from HD-sEMG.Medical & biological engineering & computing · 2026Article
- Enhanced rehabilitation after total joint replacement using a wearable high-density surface electromyography system.Frontiers in rehabilitation sciences · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established approach for mapping neural input to mechanical output, there is a lack of adaptability to dynamic environments due to a lack of data from dynamic movements. This report presents data consisting of simultaneously recorded high density surface EMG, intramuscular EMG, and joint dynamics from the tibialis anterior during static and dynamic muscle contractions. The dataset comes from seven subjects performing three to five trials each of different types of muscle contractions, both static (isometric) and dynamic (isotonic and isokinetic). Each subject was seated in an isokinetic dynamometer such that ankle movement was isolated and instrumented with four fine wire electrodes and a 126-electrode surface EMG grid. This data set can be used to (i) validate methods for extracting neural signals from surface EMG, (ii) develop models for predicting torque output, or (iii) develop classifiers for movement intent.
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Registered trials
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