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Detection of posture and mobility in individuals at risk of developing pressure ulcers

Detection of posture and mobility in individuals at risk of developing pressure ulcers
Detection of posture and mobility in individuals at risk of developing pressure ulcers
Pressure mapping technologies provide the opportunity to estimate trends in posture and mobility over extended periods in individuals at risk of developing pressure ulcers. The aim of the study was to combine pressure monitoring with an automated algorithm to detect posture and mobility in a vulnerable population of Spinal Cord Injured (SCI) patients.

Pressure data from able-bodied cohort studies involving prescribed lying and sitting postures were used to train the algorithm. This was tested with data from two SCI patients. Variations in the trends of the centre of pressure (COP) and contact area were assessed for detection of small- and large-scale postural movements. Intelligent data processing involving a deep learning algorithm, namely a convolutional neural network (CNN), was utilised for posture classification.

COP signals revealed perturbations indicative of postural movements, which were automatically detected using individual- and movement-specific thresholds. CNN provided classification of static postures, with an accuracy ranging between 70-84% in the training cohort of able-bodied subjects. A clinical evaluation highlighted the potential of the novel algorithm to detect postural movements and classify postures in SCI patients.

Combination of continuous pressure monitoring and intelligent algorithms offers the potential to objectively detect posture and mobility in vulnerable patients and inform clinical-decision making to provide personalized care.
Continuous pressure monitoring, Convolutional neural network, Deep learning, Postural movements, Pressure ulcer prevention, Spinal cord injury
1350-4533
39-47
Caggiari, Silvia
5ec3fb71-9706-4394-8ea3-495a5f01e3ee
Worsley, Peter R.
6d33aee3-ef43-468d-aef6-86d190de6756
Fryer, Sarah L.
b3bb565e-d38b-401b-baf4-18fac8ee765b
Mace, Joseph
98392991-e093-4de6-909f-74299b44393f
Bader, Dan L.
9884d4f6-2607-4d48-bf0c-62bdcc0d1dbf
Caggiari, Silvia
5ec3fb71-9706-4394-8ea3-495a5f01e3ee
Worsley, Peter R.
6d33aee3-ef43-468d-aef6-86d190de6756
Fryer, Sarah L.
b3bb565e-d38b-401b-baf4-18fac8ee765b
Mace, Joseph
98392991-e093-4de6-909f-74299b44393f
Bader, Dan L.
9884d4f6-2607-4d48-bf0c-62bdcc0d1dbf

Caggiari, Silvia, Worsley, Peter R., Fryer, Sarah L., Mace, Joseph and Bader, Dan L. (2021) Detection of posture and mobility in individuals at risk of developing pressure ulcers. Medical Engineering & Physics, 91, 39-47. (doi:10.1016/j.medengphy.2021.03.006).

Record type: Article

Abstract

Pressure mapping technologies provide the opportunity to estimate trends in posture and mobility over extended periods in individuals at risk of developing pressure ulcers. The aim of the study was to combine pressure monitoring with an automated algorithm to detect posture and mobility in a vulnerable population of Spinal Cord Injured (SCI) patients.

Pressure data from able-bodied cohort studies involving prescribed lying and sitting postures were used to train the algorithm. This was tested with data from two SCI patients. Variations in the trends of the centre of pressure (COP) and contact area were assessed for detection of small- and large-scale postural movements. Intelligent data processing involving a deep learning algorithm, namely a convolutional neural network (CNN), was utilised for posture classification.

COP signals revealed perturbations indicative of postural movements, which were automatically detected using individual- and movement-specific thresholds. CNN provided classification of static postures, with an accuracy ranging between 70-84% in the training cohort of able-bodied subjects. A clinical evaluation highlighted the potential of the novel algorithm to detect postural movements and classify postures in SCI patients.

Combination of continuous pressure monitoring and intelligent algorithms offers the potential to objectively detect posture and mobility in vulnerable patients and inform clinical-decision making to provide personalized care.

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Detection of posture
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More information

Accepted/In Press date: 25 March 2021
e-pub ahead of print date: 28 March 2021
Published date: 1 May 2021
Keywords: Continuous pressure monitoring, Convolutional neural network, Deep learning, Postural movements, Pressure ulcer prevention, Spinal cord injury

Identifiers

Local EPrints ID: 448987
URI: http://eprints.soton.ac.uk/id/eprint/448987
ISSN: 1350-4533
PURE UUID: f4c5f6f8-ff64-4585-9614-ac1d20882a96
ORCID for Peter R. Worsley: ORCID iD orcid.org/0000-0003-0145-5042
ORCID for Dan L. Bader: ORCID iD orcid.org/0000-0002-1208-3507

Catalogue record

Date deposited: 12 May 2021 16:48
Last modified: 26 Nov 2021 02:57

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Contributors

Author: Silvia Caggiari
Author: Sarah L. Fryer
Author: Joseph Mace
Author: Dan L. Bader ORCID iD

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