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
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DOI：
10.1016/j.medengphy.2021.03.006
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发表时间：
2021-04-05
影响因子：
2.2
通讯作者：
Bader, Dan L.
中科院分区：
文献类型：
--
作者：
Caggiari, Silvia;Worsley, Peter R.;Bader, Dan L.
摘要 压力测绘技术提供了估计有压疮风险的个体在较长时间内的姿势和活动度趋势的机会。该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) 患者弱势群体的姿势和活动能力。来自健全队列研究（涉及规定的躺姿和坐姿）的压力数据被用来训练算法。这是用两名 SCI 患者的数据进行测试的。评估压力中心 (COP) 和接触面积趋势的变化，以检测小范围和大规模的姿势运动。涉及深度学习算法（即卷积神经网络 (CNN)）的智能数据处理用于姿势分类。COP 信号揭示了指示姿势运动的扰动，这些扰动是使用个体和运动特定阈值自动检测到的。 CNN 提供静态姿势分类，在健全受试者的训练队列中，准确率在 70-84% 之间。临床评估强调了新算法在检测 SCI 患者的姿势运动和分类姿势方面的潜力。连续压力监测和智能算法的结合提供了客观检测弱势患者的姿势和活动性并为临床决策提供信息以提供个性化护理的潜力。(c) 2021 IPEM。由爱思唯尔有限公司出版。保留所有权利。
A B S T R A C T 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.(c) 2021 IPEM. Published by Elsevier Ltd. All rights reserved.