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首页> 外文期刊>IEEE transactions on neural systems and rehabilitation engineering >IMU-Based Locomotion Mode Identification for Transtibial Prostheses, Orthoses, and Exoskeletons
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IMU-Based Locomotion Mode Identification for Transtibial Prostheses, Orthoses, and Exoskeletons

机译:基于IMU的机置模式识别进行平静的假体,矫形器和外骨骼

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摘要

Active transtibial prostheses, orthoses, and exoskeletons hold the promise of improving the mobility of lower-limb impaired or amputated individuals. Locomotion mode identification (LMI) is essential for these devices precisely reproducing the required function in different terrains. In this study, a terrain geometry-based LMI algorithm is proposed. The environment should be built according to the inclination grade of the ground. For example, when the inclination angle is between 7 degrees and 15 degrees, the environment should be a ramp. If the inclination angle is around 30 degrees, the environment is preferred to be equipped with stairs. Given that, the locomotion mode/terrain can be classified by the inclination grade. Besides, human feet always move along the surface of terrain to minimize the energy expenditure for transporting lower limbs and get the required foot clearance. Hence, the foot trajectory estimated by an IMU was used to derive the inclination grade of the terrain that we traverse to identify the locomotion mode. In addition, a novel trigger condition (an elliptical boundary) is proposed to activate the decision-making of the LMI algorithm before the next foot strike thus leaving enough time for performing preparatory work in the swing phase. When the estimated foot trajectory goes across the elliptical boundary, the decision-making will be executed. Experimental results show that the average accuracy for three healthy subjects and three below-knee amputees is 98.5% in five locomotion modes: level-ground walking, up slope, down slope, stair descent, and stair ascent. Besides, all the locomotion modes can be identified before the next foot strike.
机译:活跃的变形假体,垂直和外骨骼具有改善下肢受损或截肢个体的流动性的承诺。 Locomotion模式识别(LMI)对于这些设备至关重要,精确地再现不同地形中所需的功能。在本研究中,提出了一种地形几何形状的LMI算法。环境应根据地面的倾向等级建立。例如,当倾斜角在7度和15度之间时,环境应该是斜坡。如果倾斜角度约为30度,则优选地配备楼梯。鉴于此,运动模式/地形可以通过倾斜度分类。此外,人脚总是沿着地形表面移动,以最大限度地减少运输下肢的能量消耗并获得所需的脚间隙。因此,由IMU估计的脚轨迹用于导出我们横穿以识别运动模式的地形的倾斜度等级。另外,提出了一种新颖的触发条件(椭圆边界)以在下一个脚部罢工之前激活LMI算法的决策,从而留下足够的时间来执行摆动阶段的准备工作。当估计的脚轨迹跨越椭圆边界时,将执行决策。实验结果表明,五种健康受试者和三个下面的3以下三个人的平均精度为5个机置模式:水平地面行走,上坡,下坡,楼梯血统和楼梯上升。此外,所有运动模式都可以在下一次脚击之前识别。

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