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Validity of Using Tri-Axial Accelerometers to Measure Human Movement - Part I: Posture and Movement Detection

机译:使用三轴加速度计测量人体运动的有效性-第一部分:姿势和运动检测

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摘要

A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.
机译:需要一种可靠的方法来识别自由生活环境中的运动,以客观地测量身体活动。这项研究的目的是验证从加速度数据对姿势定向和运动的识别,而不是视频记录的目视检查。使用放置在腰部和大腿上的三轴加速度计,可以识别站立,坐着和躺下的静态方向,以及步行,慢跑和姿势转换之间的动态运动。此外,受试者以自己选择的慢,舒适和快的速度行走和慢跑。使用信号幅度区域,连续小波变换和加速度计方向的组合来执行任务识别。在实验室研究了十二名健康的成年人,两名研究人员在视频观察的每一秒中确定了任务。除过渡以外,所有活动的评分者间可靠性的类内相关系数均大于0.95。结果表明,该方法具有很高的有效性,对于坐着和躺着的敏感性和阳性预测值均大于85%,步行和慢跑的识别率大于90%。当受试者被要求站立或坐立时,他们会在算法和视频的识别精度上出现最大的分歧。在变速任务中,步态已正确识别,速度介于0.1m / s和4.8m / s之间。这项研究包括一系列步行速度和自然运动,例如静态姿势中的坐立不安,这表明加速度计数据可用于识别一般人群的方向和运动。

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