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实时界面压力分析新技术:从大型图像数据集中获取更多信息。

New technique for real-time interface pressure analysis: getting more out of large image data sets.

作者信息

Bogie Kath, Wang Xiaofeng, Fei Baowei, Sun Jiayang

机构信息

Cleveland Advanced Platform Technology Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA.

出版信息

J Rehabil Res Dev. 2008;45(4):523-35, 10 p following 535. doi: 10.1682/jrrd.2007.03.0046.

Abstract

Recent technological improvements have led to increasing clinical use of interface pressure mapping for seating pressure evaluation, which often requires repeated assessments. However, clinical conditions cannot be controlled as closely as research settings, thereby creating challenges to statistical analysis of data. A multistage longitudinal analysis and self-registration (LASR) technique is introduced that emphasizes real-time interface pressure image analysis in three dimensions. Suitable for use in clinical settings, LASR is composed of several modern statistical components, including a segmentation method. The robustness of our segmentation method is also shown. Application of LASR to analysis of data from neuromuscular electrical stimulation (NMES) experiments confirms that NMES improves static seating pressure distributions in the sacral-ischial region over time. Dynamic NMES also improves weight-shifting over time. These changes may reduce the risk of pressure ulcer development.

摘要

最近的技术改进使得界面压力映射在座位压力评估中的临床应用日益增加,而座位压力评估通常需要反复进行。然而,临床条件无法像研究环境那样得到严格控制,从而给数据的统计分析带来了挑战。本文介绍了一种多阶段纵向分析和自注册(LASR)技术,该技术强调对三维界面压力图像进行实时分析。LASR适用于临床环境,由几个现代统计组件组成,包括一种分割方法。我们还展示了分割方法的稳健性。将LASR应用于神经肌肉电刺激(NMES)实验数据的分析证实,随着时间的推移,NMES可改善骶骨 - 坐骨区域的静态座位压力分布。动态NMES也会随着时间的推移改善体重转移。这些变化可能会降低压疮形成的风险。

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