Predicting lower limb joint kinematics using wearable motion sensors
Predicting lower limb joint kinematics using wearable motion sensors
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DOI：
10.1016/j.gaitpost.2007.11.001
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发表时间：
2008-07-01
期刊：
影响因子：
2.4
通讯作者：
Kenney, L. P. J.
中科院分区：
文献类型：
--
作者：
Findlow, A.;Goulermas, J. Y.;Kenney, L. P. J.
本研究的目的是利用足部和小腿运动传感器的 3D 角速度和线性加速度数据来估计矢状面踝关节、膝关节和髋关节的步态运动学。我们探讨了受试者内预测（即，训练和测试使用同一受试者的试验）和受试者间（测试使用与训练所用受试者不同的受试者）预测的准确性，以及传感器数据丢失对预测准确性的影响。使用反射标记收集髋部、膝部和踝部运动数据。同时，使用小型集成加速度计/陀螺仪单元收集足部和小腿角速度和线性加速度数据。使用广义回归网络算法来预测后者。最好的结果来自受试者内预测，具有非常高的相关性（0.93-0.99）和低平均绝对偏差（
The aim of this study was to estimate sagittal plane ankle, knee and hip gait kinematics using 3D angular velocity and linear acceleration data from motion sensors on the foot and shank. We explored the accuracy of intra-subject predictions (i.e., where training and testing uses trials from the same subject) and inter-subject (where testing uses subjects different from the ones used for training) predictions, and the effect of loss of sensor data on prediction accuracy. Hip, knee and ankle kinematic data were collected using reflective markers. Simultaneously, foot and shank angular velocity and linear acceleration data were collected using small integrated accelerometers/gyroscope units. A generalised regression networks algorithm was used to predict the former from the latter.The best results were from intra-subject predictions, with very high correlations (0.93-0.99) and low mean absolute deviation (