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

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.

Keywords: Continuous pressure monitoring; Convolutional neural network; Deep learning; Postural movements; Pressure ulcer prevention; Spinal cord injury.