READ ME File For 'Dataset to support the article: Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals From Two sEMG Electrodes'

Dataset DOI: https://doi.org/10.5258/SOTON/D2941

Date that the file was created: April, 2024

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GENERAL INFORMATION
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ReadMe Author: Hope O. Shaw, University of Southampton (Orcid: https://orcid.org/0000-0002-5211-2501)

Date of data collection: 2023

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SHARING/ACCESS INFORMATION
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The data is available on demand to bone fide researchers only. Please fill in the attached access request form and send it to researchdata@soton.ac.uk

This dataset supports the publication:
AUTHORS: Hope O. Shaw, Kirstie M. Devin, Jinghua Tang, and Liudi Jiang
TITLE: Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals From Two sEMG Electrodes
JOURNAL: MDPI Sensors, Special Issue: EMG Sensors and Signal Processing Technologies

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DATA & FILE OVERVIEW
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This dataset contains: 	data_Shaw_Devin_Tang_Jiang_2024.xls
			Sheets are named according to the figure it relates to.

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METHODOLOGICAL INFORMATION
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Software: MATLab R2023a, Excel, Origin 2023b

People involved with sample collection, processing, analysis and/or submission: Hope O. Shaw, Kirstie M. Devin, Jinghua Tang, and Liudi Jiang


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DATA-SPECIFIC INFORMATION 
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Abbreviations:

	sEMG		Surface Electromyography
	+/-SD		Standard deviation
	HO		Hand Open
	HC		Hand Closed
	WE		Wrist Extension
	WF		Wrist Flexion
	WS		Wrist Supernation
	WP		Wrist Pronation
	Min		Minimum Value
	Max		Maximum Value
	IQR (25%)	Interquartile Range, First Percentile
	IQR (75%)	Interquartile Range, Third Percentile
	KNN		k-Nearest Neighbours
	LDA		Linear Discriminant Analysis
	SVM		Support Vector Machine

Units of measure:

	µV	Micro Volt
	S	Seconds
	%	Percentage
	

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ACKNOWLEDGEMENTS 
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Hope O. Shaw acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/S02249X/1 for the Centre for Doctoral Training in Prosthetics and Orthotics to provide PhD studentship. 
The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.