论文标题
生物力学监测和机器学习以检测说谎的姿势
Biomechanical monitoring and machine learning for the detection of lying postures
论文作者
论文摘要
背景：已对压力映射技术进行了调整，以在长时间内监测，以评估延长姿势期间个体的压力溃疡风险。但是，时间压分布信号目前尚未用于识别姿势或移动性。本研究旨在检查自动化方法检测一系列静态姿势的潜力，并在姿势之间进行相应的过渡。反映在支撑表面和身体运动处的相互作用的参数都经常监测。随后，检查了每个信号的衍生物，以识别姿势之间的过渡。评估了三种机器学习算法，即na { ^ ve-bayes，k-neart的邻居和支持矢量机分类器，评估以预测一系列静态姿势，并通过培训模型（n = 9）确定（n = 9）（n = 9）（n = 9），并通过测试数据提供了一种不同的信号（n = 10）。结果：结果表明，派生的范围是派生的范围。扰动。预测新测试数据的姿势范围的准确性在82％ - 100％，70％-70％-98％和69％ - 100％ - Na { ^} ve-bayes，k-nearealt邻居和支持矢量机构分类器的100％范围内。目前的研究表明，通过相应的过渡性来检测静态的固定术语，这表明了静态式均匀的调查。两个监视系统。这种方法有可能提供可靠的姿势和流动性指标，以支持个性化的压溃疡预防策略。
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 Na{ï}ve-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 Na{ï}ve-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.
