Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA.
Med Eng Phys. 2014 Feb;36(2):169-76. doi: 10.1016/j.medengphy.2013.06.005. Epub 2013 Jul 27.
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.
需要一种稳健的方法来识别自由活动环境中的运动,以便客观地测量身体活动。本研究的目的是验证通过加速度数据对视频记录进行视觉检查来识别姿势定向和运动的方法。使用放置在腰部和大腿上的三轴加速度计,识别了站立、坐、躺的静态姿势以及行走、慢跑和姿势转换的动态运动。此外,受试者以自己选择的慢、舒适和快速度行走和慢跑。任务的识别是通过信号幅度面积、连续小波变换和加速度计方向的组合来完成的。12 名健康成年人在实验室中进行了研究,两名研究人员在视频观察的每一秒中识别任务。除了转换外,所有活动的内部一致性系数(ICC)均大于 0.95。结果表明,该算法具有很高的有效性,坐姿和卧位的敏感性和阳性预测值均大于 85%,行走和慢跑的识别率大于 90%。当要求受试者在站立或坐立时烦躁不安时,算法和视频之间的识别准确性存在最大差异。在变速任务中,0.1m/s 至 4.8m/s 之间的步态被正确识别。本研究包括了一系列行走速度和自然运动,如在静态姿势时烦躁不安,证明加速度计数据可用于识别普通人群中的姿势和运动。