Background

Pressure mapping technology has been adapted to monitor over prolonged periods to evaluate pressure ulcer risk in individuals during extended lying postures. However, temporal pressure distribution signals are not currently used to identify posture or mobility. The present study was designed to examine the potential of an automated approach for the detection of a range of static lying postures and corresponding transitions between postures.

Methods

Healthy subjects (n = 19) adopted a range of sagittal and lateral lying postures. Parameters reflecting both the interactions at the support surface and body movements were continuously monitored. Subsequently, the derivative of each signal was examined to identify transitions between postures. Three machine learning algorithms, namely Naïve-Bayes, k-Nearest Neighbors and Support Vector Machine classifiers, were assessed to predict a range of static postures, established with a training model (n = 9) and validated with new input from test data (n = 10).

Findings

Results showed that the derivative signals provided a means to detect transitions between postures, with actimetry providing the most distinct signal perturbations. The accuracy in predicting the range of postures from new test data ranged between 82%–100%, 70%–98% and 69%–100% for Naïve-Bayes, k-Nearest Neighbors and Support Vector Machine classifiers, respectively.

Interpretation

The present study demonstrated that detection of both static postures and their corresponding transitions was achieved by combining machine learning algorithms with robust parameters from two monitoring systems. This approach has the potential to provide reliable indicators of posture and mobility, to support personalised pressure ulcer prevention strategies.

中文翻译：

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生物力学监测和机器学习，用于检测躺卧姿势

背景

压力测绘技术已经过改进，可以长时间监控，以评估长时间卧姿时个人的压疮风险。然而，暂时的压力分布信号当前未被用于识别姿势或活动性。本研究旨在检查一种自动方法的潜力，该方法可用于检测一系列静态躺卧姿势和姿势之间的相应过渡。

方法

健康受试者（n = 19）采取了一系列矢状和侧卧姿势。连续监测反映支撑表面相互作用和身体运动的参数。随后，检查每个信号的导数以识别姿势之间的转换。对三种机器学习算法（即朴素贝叶斯，k最近邻和支持向量机分类器）进行了评估，以预测一系列静态姿势，并使用训练模型（n = 9）建立了模型，并使用了来自测试数据的新输入进行了验证（n = 10）。

发现

结果表明，导数信号提供了一种检测姿势之间转换的方法，而静电法则提供了最明显的信号扰动。对于朴素贝叶斯，k最近邻和支持向量机分类器，根据新测试数据预测姿势范围的准确度分别在82％–100％，70％–98％和69％–100％之间。

解释

本研究表明，通过将机器学习算法与来自两个监视系统的强大参数相结合，可以实现对静态姿势及其相应过渡的检测。这种方法有可能提供可靠的姿势和活动性指标，以支持个性化的压疮预防策略。