This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human-robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman filter combined with clipping filtering is proposed for the control system to mitigate the noise and time delay of the collected signal. The clipping filtering method was used to filter the accidental error and avoid the safety problem caused by a mistrigger. A modified adaptive Kalman filter was used to account for the sudden change of the motion state during rehabilitation training. The results show that the intent recognition system designed herein can accurately recognize the human-robot interaction information, and estimate the intent of human motion in time. Therefore, it can be concluded that the designed system effectively follows the predicted motion intent with the proposed method, which is a significant improvement for human-robot interaction control of upper limb extremity rehabilitation robots.

中文翻译：

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用于上肢外骨骼康复的人机交互系统的设计和验证。

本文提出了一种基于高度信号传感器的运动意图识别系统的设计，以提高上肢外骨骼机器人在康复训练中的人机交互性能。提出了一种改进的自适应卡尔曼滤波器与限幅滤波相结合的控制系统，以减轻采集信号的噪声和时间延迟。削波滤波方法用于滤除偶然误差，避免了雾化器引起的安全问题。改进的自适应卡尔曼滤波器用于解决康复训练过程中运动状态的突然变化。结果表明，本文设计的意图识别系统可以准确地识别人机交互信息，并及时估计人的运动意图。因此，