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在机器人康复任务中使用两个无线传感器估计人体手臂关节

Estimation of Human Arm Joints Using Two Wireless Sensors in Robotic Rehabilitation Tasks.

作者信息

Bertomeu-Motos Arturo, Lledó Luis D, Díez Jorge A, Catalan Jose M, Ezquerro Santiago, Badesa Francisco J, Garcia-Aracil Nicolas

机构信息

Neuro-Bioengineering Research Group, Miguel Hernandez University, Avda. de la Universidad W/N, 03202 Elche, Spain.

出版信息

Sensors (Basel). 2015 Dec 4;15(12):30571-83. doi: 10.3390/s151229818.

Abstract

This paper presents a novel kinematic reconstruction of the human arm chain with five degrees of freedom and the estimation of the shoulder location during rehabilitation therapy assisted by end-effector robotic devices. This algorithm is based on the pseudoinverse of the Jacobian through the acceleration of the upper arm, measured using an accelerometer, and the orientation of the shoulder, estimated with a magnetic angular rate and gravity (MARG) device. The results show a high accuracy in terms of arm joints and shoulder movement with respect to the real arm measured through an optoelectronic system. Furthermore, the range of motion (ROM) of 50 healthy subjects is studied from two different trials, one trying to avoid shoulder movements and the second one forcing them. Moreover, the shoulder movement in the second trial is also estimated accurately. Besides the fact that the posture of the patient can be corrected during the exercise, the therapist could use the presented algorithm as an objective assessment tool. In conclusion, the joints' estimation enables a better adjustment of the therapy, taking into account the needs of the patient, and consequently, the arm motion improves faster.

摘要

本文提出了一种具有五个自由度的新型人体手臂链运动学重建方法,并在末端执行器机器人设备辅助的康复治疗过程中估计肩部位置。该算法基于通过使用加速度计测量的上臂加速度和利用磁角速率与重力(MARG)设备估计的肩部方向,对雅可比矩阵进行伪逆运算。结果表明,相对于通过光电系统测量的真实手臂,该算法在手臂关节和肩部运动方面具有很高的精度。此外,从两项不同的试验研究了50名健康受试者的运动范围(ROM),一项试验试图避免肩部运动,另一项试验则促使肩部运动。此外,第二项试验中的肩部运动也能被准确估计。除了在运动过程中可以纠正患者姿势这一事实外,治疗师还可以将所提出的算法用作客观评估工具。总之,关节估计能够根据患者的需求更好地调整治疗方案,从而使手臂运动更快得到改善。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78da/4721738/d2f12713cfc7/sensors-15-29818-g001.jpg

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