一种用于估计人体步态参数和步行距离的扩展卡尔曼滤波器。
An Extended Kalman Filter to Estimate Human Gait Parameters and Walking Distance.
作者信息
Bennett Terrell, Jafari Roozbeh, Gans Nicholas
机构信息
Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75080-3021.
出版信息
Proc Am Control Conf. 2013 Jun;2013:752-757. doi: 10.1109/ACC.2013.6579926. Epub 2013 Aug 16.
Abstract
In this work, we present a novel method to estimate joint angles and distance traveled by a human while walking. We model the human leg as a two-link revolute robot. Inertial measurement sensors placed on the thigh and shin provide the required measurement inputs. The model and inputs are then used to estimate the desired state parameters associated with forward motion using an extended Kalman filter (EKF). Experimental results with subjects walking in a straight line show that distance walked can be measured with accuracy comparable to a state of the art motion tracking systems. The EKF had an average RMSE of 7 cm over the trials with an average accuracy of greater than 97% for linear displacement.
摘要
在这项工作中，我们提出了一种新颖的方法来估计人类行走时的关节角度和行进距离。我们将人类腿部建模为一个双连杆旋转机器人。放置在大腿和小腿上的惯性测量传感器提供所需的测量输入。然后，使用扩展卡尔曼滤波器（EKF），该模型和输入被用于估计与向前运动相关的期望状态参数。对在直线上行走的受试者进行的实验结果表明，所测量的行走距离的准确性可与最先进的运动跟踪系统相媲美。在这些试验中，EKF的平均均方根误差为7厘米，线性位移的平均准确率大于97%。
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