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New Motion Intention Acquisition Method of Lower Limb Rehabilitation Robot Based on Static Torque Sensors

机译:基于静态扭矩传感器的下肢康复机器人的新运动意向采集方法

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摘要

The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a new motion intention acquisition method based on static torque sensors is proposed. This motion intention acquisition method is established through the dynamics modeling of human–machine coordination, which is built on the basis of Lagrangian equations. Combined with the static torque sensors installed on the mechanism leg joint axis, the LLR-Ro can obtain the active force from the patient’s leg. Based on the variation of the patient’s active force and the kinematic functional relationship of the patient’s leg end point, the patient motion intention is obtained and used in the proposed active rehabilitation training method. The simulation experiment demonstrates the correctness of mechanism leg dynamics equations through ADAMS software and MATLAB software. The calibration experiment of the joint torque sensors’ combining limit range filter with an average value filter provides the hardware support for active rehabilitation training. The consecutive variation of the torque sensors from just the mechanism leg weight, as well as both the mechanism leg and the patient leg weights, obtains the feasibility of lower limb motion intention acquisition.
机译:康复机器人是对肢体残疾人的机器人技术的应用。本文调查了一种新的适用和有效的坐姿/躺线下肢康复机器人(LLR-RO)。为了改善患者的培训倡议并加速康复过程,提出了一种基于静态扭矩传感器的新运动意向采集方法。通过人机协调的动力学建模建立了这种运动意图采集方法,该方法是在拉格朗日方程的基础上建立的。结合安装在机构腿部接头轴上的静电扭矩传感器,LLR-RO可以从患者的腿上获得主动力。基于患者主动力的变化和患者腿终点的运动功能关系,获得患者运动意图并用于提出的主动康复训练方法。仿真实验通过ADAMS软件和MATLAB软件演示了机理腿部动力学方程的正确性。关节扭矩传感器的校准实验与平均值滤波器的相结合限量滤波器为主动康复培训提供了硬件支持。扭矩传感器的连续变化来自机构腿部重量,以及机构腿和患者腿部重量,获得下肢运动意向采集的可行性。

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