Learning Predictive Movement Models From Fabric-Mounted Wearable Sensors
Learning Predictive Movement Models From Fabric-Mounted Wearable Sensors
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DOI：
10.1109/tnsre.2015.2507941
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发表时间：
2016-12-01
影响因子：
4.9
通讯作者：
Howard, Matthew
中科院分区：
文献类型：
--
作者：
Michael, Brendan;Howard, Matthew
用于临床诊断或康复的人体运动测量和分析通常是在实验室环境中使用静态运动捕捉设备进行的。人们对分析日常环境（如家庭）中的运动越来越感兴趣，这促使了“可穿戴传感器”的发展，目前最流行的可穿戴传感器是嵌入到衣服中的传感器。然而，使用这些织物嵌入式传感器的一个主要问题是织物运动伪影破坏运动信号的不良影响。在本文中，提出了一种非参数方法来学习身体运动，将不希望的运动视为感知运动的随机扰动，并使用正交回归技术形成穿戴者运动的预测模型，以消除学习过程中的这些误差。本文的实验表明，标准的非参数学习技术在织物运动背景下表现不佳，使用正交回归技术可以提高预测精度。将该运动伪像问题建模为随机学习问题表明，与运动学模型相比，使用织物嵌入式传感器的身体姿势任务的预测误差平均降低了77%。
The measurement and analysis of human movement for applications in clinical diagnostics or rehabilitation is often performed in a laboratory setting using static motion capture devices. A growing interest in analyzing movement in everyday environments (such as the home) has prompted the development of "wearable sensors", with the most current wearable sensors being those embedded into clothing. A major issue however with the use of these fabric-embedded sensors is the undesired effect of fabric motion artefacts corrupting movement signals. In this paper, a nonparametric method is presented for learning body movements, viewing the undesired motion as stochastic perturbations to the sensed motion, and using orthogonal regression techniques to form predictive models of the wearer's motion that eliminate these errors in the learning process. Experiments in this paper show that standard nonparametric learning techniques underperform in this fabric motion context and that improved prediction accuracy can be made by using orthogonal regression techniques. Modelling this motion artefact problem as a stochastic learning problem shows an average 77% decrease in prediction error in a body pose task using fabric-embedded sensors, compared to a kinematic model.