Unobtrusive Sleep Position Classification Using a Novel Optical Tactile Sensor.

Alexander Breuss, Carmelo Sferrazza, Jonas Pleisch, Raffaello D'Andrea, Robert Riener
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Abstract

Unobtrusive sleep position classification is essential for sleep monitoring and closed-loop intervention systems that initiate position changes. In this paper, we present a novel unobtrusive under-mattress optical tactile sensor for sleep position classification. The sensor uses a camera to track particles embedded in a soft silicone layer, inferring the deformation of the silicone and therefore providing information about the pressure and shear distributions applied to its surface.We characterized the sensitivity of the sensor after placing it under a conventional mattress and applying different weights (258 g, 500 g, 5000 g) on top of the mattress in various predefined locations. Moreover, we collected multiple recordings from a person lying in supine, lateral left, lateral right, and prone positions. As a proof-of-concept, we trained a neural network based on convolutional layers and residual blocks that classified the lying positions based on the images from the tactile sensor.We observed a high sensitivity of the optical tactile sensor: Even after placing the sensor below a conventional mattress, we were able to detect our lowest test weight of 258 g. Using the neural network, we were able to classify the four sleep positions, lateral left, lateral right, prone, and supine with a classification accuracy of 91.2 %.The high sensitivity of the sensor, as well as the good performance in the classification task, demonstrate the feasibility of using such a sensor in a robotic bed setup.Clinical Relevance- Positional Obstructive Sleep Apnea is highly prevalent across the general population. Today's gold standard treatment of using CPAP ventilation is often not accepted, leading to unwanted treatment cessations. Alternative treatments, such as positional interventions through robotic beds are highly promising. However, these beds require reliable detection of the lying position. In this paper, we present a novel, scalable and completely unobtrusive sensor that is concealed under the mattress while classifying sleeping positions with high accuracy.

利用新型光学触觉传感器进行无干扰睡眠姿势分类
对于睡眠监测和启动体位变化的闭环干预系统来说,不引人注意的睡眠体位分类至关重要。在本文中,我们介绍了一种用于睡眠位置分类的新型非侵入式床垫下光学触觉传感器。我们将传感器置于传统床垫下,并在床垫顶部不同预定位置施加不同重量(258 克、500 克和 5000 克),从而确定了传感器的灵敏度。此外,我们还收集了人在仰卧、左侧卧、右侧卧和俯卧姿势下的多次记录。作为概念验证,我们训练了一个基于卷积层和残差块的神经网络,该网络可根据触觉传感器的图像对躺卧姿势进行分类:传感器的高灵敏度以及在分类任务中的良好表现,证明了在机器人床上使用这种传感器的可行性。临床意义--体位性阻塞性睡眠呼吸暂停症在普通人群中非常普遍。如今,使用 CPAP 通气的金标准治疗方法往往不被接受,导致不必要的治疗中断。通过机器人床进行体位干预等替代治疗方法前景广阔。然而,这些床需要可靠的卧位检测。在本文中,我们介绍了一种新型、可扩展且完全不显眼的传感器,它可以隐藏在床垫下,同时对睡眠姿势进行高精度分类。
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