Data-Raspberry data for 'Wearable multimodal skin sensing for the diabetic foot'
Data-Raspberry data for 'Wearable multimodal skin sensing for the diabetic foot'
This data is supplied in support of the the article "Wearable multimodal skin sensing for the diabetic foot" by Coates, Chipperfield and Clough in the open access journal 'electronics - Raspberry Pi Special edition'.
A data set is provided for each volunteer referenced as 001, 009 and 1001 are enclosed. Biomentric data is supplied for each volunteer along with the data set used in the article. The title of each graph presented in the article identifies the data set used together with the chosen data filter type (low pass) and filter cut off frequency. TA 6 pole Butterwoth filter was used. Data was analysed in Python Spyder 2.3.5.2.
Data is taken in time series with a descriptor of the data stream collected in row 5 of the respective column. The unis for each data stream is in row 6. All data ws taken at 20Hz.
diabetes, skin, monitoring, multi-sensor, remote sensing, evaluation, test, Raspberry Pi
University of Southampton
Coates, James, Martin
064f3710-e235-4a8a-b4bc-e4effd2b36f0
Chipperfield, Andrew
524269cd-5f30-4356-92d4-891c14c09340
Clough, Geraldine
9f19639e-a929-4976-ac35-259f9011c494
Coates, James, Martin
064f3710-e235-4a8a-b4bc-e4effd2b36f0
Chipperfield, Andrew
524269cd-5f30-4356-92d4-891c14c09340
Clough, Geraldine
9f19639e-a929-4976-ac35-259f9011c494
Coates, James, Martin, Chipperfield, Andrew and Clough, Geraldine
(2016)
Data-Raspberry data for 'Wearable multimodal skin sensing for the diabetic foot'.
University of Southampton
doi:10.5258/SOTON/386374
[Dataset]
Abstract
This data is supplied in support of the the article "Wearable multimodal skin sensing for the diabetic foot" by Coates, Chipperfield and Clough in the open access journal 'electronics - Raspberry Pi Special edition'.
A data set is provided for each volunteer referenced as 001, 009 and 1001 are enclosed. Biomentric data is supplied for each volunteer along with the data set used in the article. The title of each graph presented in the article identifies the data set used together with the chosen data filter type (low pass) and filter cut off frequency. TA 6 pole Butterwoth filter was used. Data was analysed in Python Spyder 2.3.5.2.
Data is taken in time series with a descriptor of the data stream collected in row 5 of the respective column. The unis for each data stream is in row 6. All data ws taken at 20Hz.
Spreadsheet
Data_Raspberry_Pi_Coates_Chipperfield_Clough.xlsx
- Dataset
More information
Published date: 2016
Keywords:
diabetes, skin, monitoring, multi-sensor, remote sensing, evaluation, test, Raspberry Pi
Organisations:
Education Hub, Bioengineering Group, Human Development & Health
Projects:
DTA - University of Southampton
Funded by: UNSPECIFIED (EP/K503150/1)
October 2012 to September 2016
Identifiers
Local EPrints ID: 386374
URI: http://eprints.soton.ac.uk/id/eprint/386374
PURE UUID: a09669ec-0072-4fba-a182-a48e8ba4c6b5
Catalogue record
Date deposited: 23 May 2016 15:51
Last modified: 05 Nov 2023 02:39
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Contributors
Creator:
James, Martin Coates
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