Pervasive Lying Posture Tracking.
Pervasive Lying Posture Tracking.
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普遍说谎的姿势跟踪。
DOI：
10.3390/s20205953
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发表时间：
2020-10-21
期刊：
影响因子：
--
通讯作者：
Parvaneh S
中科院分区：
文献类型：
--
作者：
Alinia P;Samadani A;Milosevic M;Ghasemzadeh H;Parvaneh S
自动化的躺姿跟踪对于预防与床相关的疾病非常重要，例如压力损伤、睡眠呼吸暂停和下背痛。先前的研究研究了使用不同模态的传感器（例如，加速度计和压力传感器）。然而，关于如何设计有效的床上躺姿跟踪系统的研究仍然存在重大差距。这些差距可以通过以下几个研究问题加以阐述。首先，我们能不能设计一个单一传感器，普及，廉价的系统，可以准确地检测说谎的姿势？第二，什么样的计算模型在准确检测说谎姿势方面最有效？最后，传感器系统的什么物理配置对于躺卧姿势跟踪最有效？为了回答这些重要的研究问题，在这篇文章中，我们提出了一个全面的方法来设计一个传感器系统，使用一个单一的加速度计沿着与机器学习算法在床上躺姿分类。我们设计了两类基于深度学习和传统分类的机器学习算法，并使用手工特征来检测说谎姿势。我们还调查了什么样的佩戴部位在准确检测说谎姿势方面是最有效的。我们广泛地评估所提出的算法的性能上九个不同的身体位置和四个人类躺姿使用两个数据集。我们的研究结果表明，具有单个加速度计的系统可以与深度学习或传统分类器一起使用，以准确检测说谎姿势。在我们的方法中，最好的模型的得分范围从%到%，变异系数从到。研究结果还确定大腿和胸部是最显着的身体部位，为躺姿跟踪。我们在这篇文章中的研究结果表明，由于加速度计是无处不在的廉价传感器，它们可以成为一个可行的信息来源，用于对床上姿势的普遍监测。
Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an score that ranges from % to % with a coefficient of variation from to . The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.
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影响因子：
5.8
作者：
Lee, M.;Choh, A. C.;Demerath, E. W.;Knutson, K. L.;Duren, D. L.;Sherwood, R. J.;Sun, S. S.;Chumlea, W. M. C.;Towne, B.;Siervogel, R. M.;Czerwinski, S. A.
通讯作者：
Czerwinski, S. A.
影响因子：
5.7
作者：
Berridge, Clara;Wetle, Terrie Fox
通讯作者：
Wetle, Terrie Fox
影响因子：
8
作者：
Altun, Kerem;Barshan, Billur;Tuncel, Orkun
通讯作者：
Tuncel, Orkun
影响因子：
1.9
作者：
Lindgren, M;Unosson, M;Ek, AC
通讯作者：
Ek, AC
影响因子：
15.2
作者：
Coravos, Andrea;Doerr, Megan;Wood, William A.
通讯作者：
Wood, William A.