Skin Health Research Group, Faculty of Environmental and Life Sciences, School of Heath Sciences, University of Southampton, SO17 1BJ, United Kingdom.
Skin Health Research Group, Faculty of Environmental and Life Sciences, School of Heath Sciences, University of Southampton, SO17 1BJ, United Kingdom.
Med Eng Phys. 2021 May;91:39-47. doi: 10.1016/j.medengphy.2021.03.006. Epub 2021 Mar 28.
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.
压力测绘技术提供了机会，可以在有发生压疮风险的个体中，长时间估计姿势和活动度的趋势。本研究的目的是将压力监测与自动算法相结合，以检测脊髓损伤（SCI）患者这一脆弱人群的姿势和活动度。使用涉及规定的卧床和坐姿的健康人队列研究中的压力数据来训练算法。然后使用两名 SCI 患者的数据对其进行测试。评估了压力中心（COP）和接触面积的变化趋势，以检测小范围和大范围的姿势运动。使用智能数据处理，即深度学习算法，即卷积神经网络（CNN），进行姿势分类。COP 信号显示出姿势运动的干扰，这些运动可以使用个体和运动特定的阈值自动检测。CNN 对静态姿势进行分类，在健康人训练队列中，准确率在 70-84%之间。临床评估突出了该新型算法在检测 SCI 患者的姿势运动和分类姿势方面的潜力。连续压力监测与智能算法的结合有可能客观地检测脆弱患者的姿势和活动度，并为提供个性化护理提供临床决策信息。
