Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA.
Department of Physical Therapy, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.
Sensors (Basel). 2018 Aug 22;18(9):2759. doi: 10.3390/s18092759.
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.
远程康复可以使步态异常患者受益，患者可以在家中或社区持续监测膝关节角度。使用移动设备进行连续监测可能会受到佩戴的传感器数量、信号带宽和操作算法复杂性的限制。因此，本文提出了一种使用仅两个腿部安装的陀螺仪测量下肢角速度来估算膝关节角度的新算法。该陀螺仪算法（GO 算法）通过对陀螺仪-derived 膝关节角速度信号进行积分来计算膝关节角度，从而避免了对噪声加速度计数据的依赖。为了消除陀螺仪数据的漂移，从膝关节角速度的特征点导出零角度更新，应用于每一步。GO 算法的即时有效性和结构收敛有效性分别通过两种现有的基于惯性测量单元的算法（互补滤波器和卡尔曼滤波器）和光学运动捕捉系统来确定。 Bland⁻Altman 分析表明，GO 算法与其他膝关节角度测量方法具有高度一致性。
