Developing a baseline for upper-body motor skill assessment using a robotic kinematic model

Sergio García-Vergara, M. Serrano, Yu-Ping Chen, A. Howard
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引用次数: 7

Abstract

In the rehabilitation field, determining the effectiveness of an intervention protocol begins by comparing the individual's movement characteristics against a baseline. In most settings, this baseline is determined through clinical studies involving a range of patients belonging to the same demographic group. Unfortunately, this leads to a process that is difficult to repeat for all patient demographics, or all movement characteristics, given the demands on clinicians' and patients' time for performing such clinical baseline measurement studies. To address this issue, we discuss a method that allows clinicians to objectively assess an individual's movements and compare the resulting outcome kinematic metrics to a kinematic baseline. Instead of collecting human patient data, we propose a robotic kinematic model that generates a baseline for different kinematic parameters in real-time as a function of the state of a given task. We evaluate our methodology on elbow and shoulder range of motion (ROM) angles obtained from eleven typically developing children. We compare the user's ROM angles to those generated by the proposed model, and discuss the potential of the model to be used in various intervention protocols.
使用机器人运动学模型开发上肢运动技能评估的基线
在康复领域,确定干预方案的有效性首先要将个体的运动特征与基线进行比较。在大多数情况下,这一基线是通过涉及属于同一人口统计学组的一系列患者的临床研究确定的。不幸的是,考虑到临床医生和患者进行此类临床基线测量研究的时间要求,这导致难以对所有患者人口统计数据或所有运动特征重复这一过程。为了解决这个问题,我们讨论了一种方法,该方法允许临床医生客观地评估个体的运动,并将结果运动学指标与运动学基线进行比较。我们提出了一个机器人运动学模型,它可以根据给定任务的状态实时生成不同运动学参数的基线,而不是收集人类患者的数据。我们评估了我们的方法肘关节和肩部的活动范围(ROM)角度从11个典型的发展儿童获得。我们将用户的ROM角度与所提出的模型产生的角度进行了比较,并讨论了该模型在各种干预协议中使用的潜力。
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