Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON, Canada.
Gait Posture. 2011 Oct;34(4):462-6. doi: 10.1016/j.gaitpost.2011.06.019. Epub 2011 Jul 31.
Techniques have been developed to analyze walking gait using accelerometer and gyroscope data from miniature inertial measurement units (IMU), but few attempts have been made to use similar approaches for running gait. The purpose of this study was to develop an algorithm capable of estimating running speed using a single shank-mounted IMU. Raw acceleration and angular velocity were recorded from an IMU sensor attached on the lateral side of the shank in the sagittal plane and a method of reliably detecting the shank vertical and the minimal shank velocity gait event was used to segment a running sequence into individual strides. Through integration, the orientation of the shank segment was determined and an estimate of stride-by-stride running speed was calculated by integrating the acceleration data. The algorithm was verified using data collected from a group of seven volunteers running on a treadmill at speeds between 2.50 m/s and 3.50 m/s. Over the entire speed range, the estimation results gave a percentage root mean square error (%RMSE) of approximately 4.10%. With the accurate estimation capability and portability, the use of the proposed system in outdoor running gait analysis is promising.
已经开发出了使用微型惯性测量单元(IMU)的加速度计和陀螺仪数据来分析步行步态的技术,但很少有人尝试使用类似的方法来分析跑步步态。本研究的目的是开发一种能够使用单个安装在小腿上的 IMU 估算跑步速度的算法。在矢状面小腿的外侧附着 IMU 传感器,记录原始加速度和角速度,并使用可靠地检测小腿垂直和最小小腿速度步态事件的方法将跑步序列分割成单个步幅。通过积分,确定小腿段的方向,并通过积分加速度数据计算逐步的跑步速度估计值。该算法通过在跑步机上以 2.50 m/s 至 3.50 m/s 之间的速度对七名志愿者进行的一组数据进行了验证。在整个速度范围内,估计结果的均方根百分比误差(%RMSE)约为 4.10%。由于具有精确的估计能力和便携性,因此有望将所提出的系统用于户外跑步步态分析。