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Kinematic-based locomotion mode recognition for power augmentation exoskeleton

机译:基于运动的运动模式识别电力增强exoskeleton

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This article presents a kinematic-based method for locomotion mode recognition, for use in the control of an exoskeleton for power augmentation, to implement natural and smooth locomotion transition. The difference in vertical foot position between a foot already in contact with ground and a foot newly in contact with the ground was calculated via kinematics for the entire exoskeleton and used to identify the locomotion mode with other sensor data including data on the knee joint angle and inclination of the thigh, shank, and foot. Locomotion on five different types of terrain-level-ground walking, stair ascent, stair descent, ramp ascent, and ramp descent-were identified using two-layer decision tree classes. An updating process is proposed to improve identification of the transition and accuracy using the foot inclination at the mid-stance. An average identification accuracy of more than 99% was achieved in experiments with eight subjects for single terrains ( no terrain transitions) and hybrid terrains. The experimental results show that the proposed method can achieve high accuracy without significant misrecognition and minimize the delay in locomotion mode recognition of the exoskeleton.
机译:本文介绍了基于运动模式识别的基于运动的方法,用于控制电力增强的外骨骼,实现自然和平滑的运动转变。通过用于整个外骨骼的运动学计算已经与地面接触的脚与地与地面接触的脚之间的垂直脚位置的差异,并用于识别包括在膝关节角上的其他传感器数据的运动模式和包括在膝关节角的数据倾斜大腿,小腿和脚。使用双层决策树类识别出五种不同类型地形级地面行走,楼梯上升,楼梯下降,斜坡上升和斜坡下降的运动。提出了一种更新过程,以改善使用中间姿势的脚倾斜度的过渡和精度的识别。在实验中实现了超过99%的平均识别精度,其中八个受试者进行单个地形(没有地形过渡)和混合地形。实验结果表明,该方法可以达到高精度而无需显着误报,并最小化外骨骼的运动模式识别的延迟。

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