EMG dataset for predicting forearm muscle force exerted during dynamometer squeezing (different signal capture factors) (EMG-FMFP)

Published: 10 August 2026| Version 1 | DOI: 10.17632/2prvk292rp.1
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Description

This dataset is intended for training models to predict the force generated during hand dynamometer squeezing by analyzing surface electromyography (EMG) signals captured from two forearm muscles: the finger flexor and extensor. The primary focus of this dataset is on investigating the influence of EMG capture factors on model prediction accuracy (due to variability in the signal's amplitude-frequency characteristics): - Inter-subject differences; - Different recording sessions within the same day; - Electrode repositioning within the same day; - Different recording days; - Caffeine intake; - Presence or absence of support for the hand and dynamometer; - Presence of muscle fatigue; - Different hands: dominant and non-dominant hands; - Different hands: left and right; - Different exertion intensities.

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Steps to reproduce

For each of the six participants, experiments were conducted over two days, consisting of four recording sessions per hand: 1. Experiment performed on a rested hand with initial electrode placement. Electrodes were not removed upon completion of the experiment. 2. Electrodes remained in the previous position. Experiment conducted after a break of at least 40 minutes without electrode repositioning. Electrodes were removed at the end of the experiment, with their positions marked using a gel pen. 3. Experiment conducted after a break of at least 40 minutes with electrode repositioning according to the markings. Electrodes were not removed at the end of the experiment, and a cup of coffee was consumed. 4. Experiment conducted after consuming a cup of coffee and a break of at least 40 minutes without electrode repositioning. Electrodes were removed at the end of the experiment, and markings were erased. The more detailed dataset description is provided in the ReadMe files.

Institutions

Categories

Machine Learning, Electromyography, Dynamometry

Funders

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