Gait pattern generation for robotic gait rehabilitation system on treadmill

Seo, Kap-Ho; Park, Yongsik; Yun, Sung Ho; Park, Sungho; Jeon, Kwang-Woo; Jun, Jungsoo; Kim, Dae–Hee

doi:10.1109/iccas.2014.6987939

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…In LOPES, trajectories are generated by a method based on regression analysis to reconstruct the body height and speed-dependent trajectories [31]; further, "complementary limb motion estimation" may be used to generate reference motion using the motion of healthy limbs [63]. When the collected number of samples is large enough, statistical learning techniques, such as the radial basis function neural networks (RBFs) [64] and multi-layer perceptron neural network (MLPNN) [65], are often used for motion prediction.…”

Section: Trajectory Planningmentioning

confidence: 99%

A Review on Lower Limb Rehabilitation Exoskeleton Robots

Shi

Zhang

et al. 2019

Chin. J. Mech. Eng.

285

116

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Lower limb rehabilitation exoskeleton robots integrate sensing, control, and other technologies and exhibit the characteristics of bionics, robotics, information and control science, medicine, and other interdisciplinary areas. In this review, the typical products and prototypes of lower limb exoskeleton rehabilitation robots are introduced and stateof-the-art techniques are analyzed and summarized. Because the goal of rehabilitation training is to recover patients' sporting ability to the normal level, studying the human gait is the foundation of lower limb exoskeleton rehabilitation robot research. Therefore, this review critically evaluates research progress in human gait analysis and systematically summarizes developments in the mechanical design and control of lower limb rehabilitation exoskeleton robots. From the performance of typical prototypes, it can be deduced that these robots can be connected to human limbs as wearable forms; further, it is possible to control robot movement at each joint to simulate normal gait and drive the patient's limb to realize robot-assisted rehabilitation training. Therefore human-robot integration is one of the most important research directions, and in this context, rigid-flexible-soft hybrid structure design, customized personalized gait generation, and multimodal information fusion are three key technologies.

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