Estimating Joint Kinematics and Muscles Forces During Robotic Rehabilitation to Detect and Counteract Reduced Ankle Mobility.

Kim K Peper, Elisabeth R Jensen, Sami Haddadin
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引用次数: 0

Abstract

The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation (R=0.88±0.09) between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements (R = 0.62±0.27) are promising and a first application to a patient data set shows potential for future clinical application.

评估机器人康复过程中的关节运动学和肌肉力量,以检测和应对踝关节活动性下降。
本文提出了一种在患者接受治疗时检测主动踝关节运动的解决方案,该装置既不限制也不主动支持踝关节背屈或跖屈。所提出的方法只需要在系统中添加两个加速度计传感器以及下肢的肌肉骨骼模型。使用正向运动学和反向动力学,它能够在矢状面上跟踪膝关节和踝关节的运动学,并估计肌肉力量。这是之前工作的延伸,在之前的工作中,只有髋关节跟踪是可能的,因此,肌肉力量的估计是有限的。目前对12名健康受试者进行的验证研究的相关结果显示,用所提出的方法估计的运动学与用金标准运动捕捉装置计算的所有三个关节(髋关节、膝关节和踝关节)的运动学之间具有高度相关性(R=0.88±0.09)。估计的胫骨前肌力与肌电图测量的相关性结果(R=0.62±0.27)是有希望的,首次应用于患者数据集显示了未来临床应用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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