IEEE J Biomed Health Inform. 2021 Feb;25(2):465-474. doi: 10.1109/JBHI.2020.2988360. Epub 2021 Feb 5.
The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The system consists of merging data from two inertial measurement units (IMUs) and an intensity-variation based Polymer Optical Fiber (POF) curvature sensor using a quaternion-based Multiplicative Extended Kalman Filter (MEKF). The proposed data fusion method is magnetometer-free and deals with sensors' uncertainties through reliability intervals defined during gait. Walking trials were performed by twelve healthy participants using our knee sleeve system and results were validated against a gold standard motion capture system. Additionally, a comparison with other three knee angle estimation methods, which are exclusively based on IMUs, was carried out. The proposed system presented better performance (mean RMSE 3.3 , LFM coefficients, a = 0.99 ± 0.04, a = 0.70 ± 2.29, R = 0.98 ± 0.01 and ρ 0.99) when compared to the other evaluated methods. Experimental results demonstrate the usability and feasibility of our system to estimate knee motion with high accuracy, repeatability, and reproducibility. This wearable system may be suitable for motion assessment in rehabilitation labs in future studies.
膝关节屈伸角度是各种临床情况下需要监测的重要变量,例如在物理康复评估期间。本工作的目的是开发和验证一种基于膝关节护套的传感器融合系统,用于监测物理治疗。该系统包括使用基于四元数的乘法扩展卡尔曼滤波器(MEKF)融合来自两个惯性测量单元(IMU)和一个基于强度变化的聚合物光纤(POF)曲率传感器的数据。所提出的数据融合方法是无磁力计的,并通过在步态期间定义的可靠性间隔来处理传感器的不确定性。十二名健康参与者使用我们的膝关节护套系统进行了步行试验,结果与黄金标准运动捕捉系统进行了验证。此外,还与其他三种仅基于 IMU 的膝关节角度估计方法进行了比较。与其他评估方法相比,所提出的系统表现出更好的性能(平均 RMSE 为 3.3 , LFM 系数, a = 0.99 ± 0.04 , a = 0.70 ± 2.29 , R = 0.98 ± 0.01 和 ρ 0.99 )。实验结果证明了我们的系统具有高准确性、可重复性和可再现性来估计膝关节运动的可用性和可行性。这种可穿戴系统在未来的研究中可能适合在康复实验室中进行运动评估。