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PREDICTED THRESHOLD AGAINST BACKWARD BALANCE LOSS FOLLOWING A SLIP IN GAIT

机译：步态滑动后防止向后平衡损失的预期阈值

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The purpose of this study was to use a 7-link, moment-actuated human model to predict, at liftoff of the trailing foot in gait, the threshold of the center of mass (COM) velocity relative to the base of support (BOS) required to prevent backward balance loss during single stance recovery from a slip. Five dynamic optimization problems were solved to find the minimum COM velocities that would allow the simulation to terminate with the COM above the BOS when the COM started 0.25, 0.5, 0.75, 1.0, and 1.25 foot lengths behind the heel of the stance foot (i.e., behind the BOS). The initial joint angles of the model were based on averaged data from experimental trials. Foot-ground contact was modeled using 16 visco-elastic springs distributed under the stance foot. Slipping was modeled by setting the sliding coefficient of friction of these springs to 0.02. The forward velocity of the COM necessary to avoid a backward balance loss is nearly two times larger under slip conditions than under non-slip conditions. The predicted threshold for backward balance loss following a slip agreed well with experimental data collected from 99 young adults in response to 927 slips during walking. In all trials in which a subject’s COM had a velocity below the predicted threshold, the subject’s recovery foot landed posterior to the slipping foot as predicted. Finally, combining experimental data with optimization, we verified that the 7-link model could more accurately predict gait stability than a 2-link model.
机译：这项研究的目的是使用一个7连杆，力矩驱动的人体模型来预测步态中后脚抬起时相对于支撑基础（BOS）的质心（COM）速度阈值防止从滑移单姿态恢复过程中向后的平衡损失。解决了五个动态优化问题，以找到最小COM速度，当COM从站姿脚后跟开始的0.25、0.5、0.75、1.0和1.25英尺长度开始时，模拟将以BOS上方的COM终止。 ，位于BOS后面）。模型的初始关节角基于实验试验的平均数据。使用分布在姿势脚下的16个粘弹性弹簧对脚-地面接触进行建模。通过将这些弹簧的滑动摩擦系数设置为0.02，可以对滑动进行建模。为避免向后平衡损失，COM的正向速度在打滑条件下要比在非打滑条件下大两倍。滑倒后向后平衡损失的预测阈值与从99名年轻成年人收集的实验数据非常吻合，该数据是针对步行过程中的927次滑倒而做出的。在受试者的COM的速度低于预期阈值的所有试验中，受试者的恢复脚如预期那样落在滑脚的后方。最后，结合实验数据和优化，我们验证了7链接模型比2链接模型可以更准确地预测步态稳定性。

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