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A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices

机译:使用两个腿部安装的陀螺仪估算膝盖角度的新方法用于连续监测移动医疗设备

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摘要

Tele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms. Therefore, this paper proposes a novel algorithm for estimating knee joint angle using lower limb angular velocity, obtained with only two leg-mounted gyroscopes. This gyroscope only (GO) algorithm calculates knee angle by integrating gyroscope-derived knee angular velocity signal, and thus avoids reliance on noisy accelerometer data. To eliminate drift in gyroscope data, a zero-angle update derived from a characteristic point in the knee angular velocity is applied to every stride. The concurrent validity and construct convergent validity of the GO algorithm was determined with two existing IMU-based algorithms, complementary and Kalman filters, and an optical motion capture system, respectively. Bland–Altman analysis indicated a high-level of agreement between the GO algorithm and other measures of knee angle.
机译:步态异常患者的远程康复可受益于对家庭和社区膝关节角度的连续监测。随身携带传感器的数量,信号带宽和操作算法的复杂性可能会限制使用移动设备进行连续监视。因此,本文提出了一种仅使用两个安装在腿上的陀螺仪获得的利用下肢角速度估计膝关节角度的新颖算法。这种仅陀螺仪(GO)算法通过对源自陀螺仪的膝盖角速度信号进行积分来计算膝盖角度,从而避免了对嘈杂的加速度计数据的依赖。为了消除陀螺仪数据中的漂移,将从膝部角速度的特征点得出的零角度更新应用于每个步幅。 GO算法的并发有效性和构造收敛性有效性分别由两个现有的基于IMU的算法,互补和卡尔曼滤波器以及光学运动捕获系统确定。 Bland–Altman分析表明,GO算法与其他膝盖角度测量方法之间具有高度的一致性。

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