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A research on the postural stability of a person wearing the lower limb exoskeletal robot by the HAT model

机译:用HAT模型研究下肢外骨骼机器人的姿势稳定性。

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This paper proposes a method of detecting the postural stability of a person wearing the lower limb exoskeletal robot with the HAT(Head-Arm-Trunk) model. Previous studies have shown that the human posture is stable when the CoM(Center of Mass) of the human body is placed on the BoS(Base of Support). In the case of the lower limb exoskeletal robot, the motion data, which are used for the CoM estimation, are acquired by sensors in the robot. The upper body, however, does not have sensors in each segment so that it may cause the error of the CoM estimation. In this paper, the HAT(Head-Arm-Trunk) model which combines head, arms, and torso into a single segment is considered because the motion of head and arms are unknown due to the lack of sensors. To verify the feasibility of HAT model, the reflecting markers are attached to each segment of the whole human body and the exact motion data are acquired by the VICON to compare the COM of the full body model and HAT model. The difference between the CoM with full body and that with HAT model is within 20mm for the various motions of head and arms. Based on the HAT model, the XCoM(Extrapolated Center of Mass) which includes the velocity of the CoM is used for prediction of the postural stability. The experiment of making unstable posture shows that the XCoM of the whole body based on the HAT model is feasible to detect the instance of postural instability earlier than the CoM by 20–250 msec. This result may be used for the lower limb exoskeletal robot to prepare for any action to prevent the falling down.
机译:提出了一种利用HAT(Head-Arm-Trunk)模型检测下肢外骨骼机器人佩戴者姿势稳定性的方法。先前的研究表明,当人体的CoM(质心)放在BoS(支撑基础)上时,人体姿势是稳定的。在下肢外骨骼机器人的情况下,用于CoM估计的运动数据由机器人中的传感器获取。但是,上半身在每个段中都没有传感器,因此可能导致CoM估计错误。在本文中,考虑了将头,臂和躯干组合为单个段的HAT(头臂-躯干)模型,因为由于缺少传感器,头和臂的运动是未知的。为了验证HAT模型的可行性,将反射标记贴在整个人体的每个部分上,VICON采集精确的运动数据,以比较全身模型和HAT模型的COM。对于头部和手臂的各种动作,全身CoM与HAT模型之间的差异在20mm之内。基于HAT模型,将包含CoM速度的XCoM(外推质心)用于姿势稳定性的预测。做出不稳定姿势的实验表明,基于HAT模型的全身XCoM可以比CoM提前20–250毫秒来检测姿势不稳的情况。该结果可用于下肢外骨骼机器人准备任何防止跌倒的动作。

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