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首页> 外文期刊>Journal of NeuroEngineering Rehabilitation >Quantifying kinematics of purposeful movements to real, imagined, or absent functional objects: Implications for modelling trajectories for robot-assisted ADL tasks**
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Quantifying kinematics of purposeful movements to real, imagined, or absent functional objects: Implications for modelling trajectories for robot-assisted ADL tasks**

机译:量化有目的运动到真实,想象或不存在的功能对象的运动学:对机器人辅助ADL任务的轨迹建模的含义**

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Background Robotic therapy is at the forefront of stroke rehabilitation. The Activities of Daily Living Exercise Robot (ADLER) was developed to improve carryover of gains after training by combining the benefits of Activities of Daily Living (ADL) training (motivation and functional task practice with real objects), with the benefits of robot mediated therapy (repeatability and reliability). In combining these two therapy techniques, we seek to develop a new model for trajectory generation that will support functional movements to real objects during robot training. We studied natural movements to real objects and report on how initial reaching movements are affected by real objects and how these movements deviate from the straight line paths predicted by the minimum jerk model, typically used to generate trajectories in robot training environments. We highlight key issues that to be considered in modelling natural trajectories. Methods Movement data was collected as eight normal subjects completed ADLs such as drinking and eating. Three conditions were considered: object absent, imagined, and present. This data was compared to predicted trajectories generated from implementing the minimum jerk model. The deviations in both the plane of the table (XY) and the saggital plane of torso (XZ) were examined for both reaches to a cup and to a spoon. Velocity profiles and curvature were also quantified for all trajectories. Results We hypothesized that movements performed with functional task constraints and objects would deviate from the minimum jerk trajectory model more than those performed under imaginary or object absent conditions. Trajectory deviations from the predicted minimum jerk model for these reaches were shown to depend on three variables: object presence, object orientation, and plane of movement. When subjects completed the cup reach their movements were more curved than for the spoon reach. The object present condition for the cup reach showed more curvature than in the object imagined and absent conditions. Curvature in the XZ plane of movement was greater than curvature in the XY plane for all movements. Conclusion The implemented minimum jerk trajectory model was not adequate for generating functional trajectories for these ADLs. The deviations caused by object affordance and functional task constraints must be accounted for in order to allow subjects to perform functional task training in robotic therapy environments. The major differences that we have highlighted include trajectory dependence on: object presence, object orientation, and the plane of movement. With the ability to practice ADLs on the ADLER environment we hope to provide patients with a therapy paradigm that will produce optimal results and recovery.
机译:背景技术机器人疗法是中风康复的最前沿。日常活动锻炼机器人(ADLER)的开发旨在通过结合日常活动(ADL)训练的好处(动机和功能性任务练习与真实物体)以及机器人介导的治疗手段来改善训练后收益的结转。 (可重复性和可靠性)。通过结合这两种治疗技术,我们寻求开发一种用于轨迹生成的新模型,该模型将在机器人训练期间支持功能性移动到真实物体。我们研究了自然运动到真实物体的情况,并报告了真实物体如何影响最初的到达运动,以及这些运动如何偏离最小抖动模型预测的直线路径,该模型通常用于在机器人训练环境中生成轨迹。我们重点介绍了在自然轨迹建模中要考虑的关键问题。方法收集八名正常受试者完成的ADL(例如饮食)的运动数据。考虑了三个条件:对象缺失,想象和存在。将该数据与通过实施最小加加速度模型生成的预测轨迹进行比较。检查了到达杯子和勺子的桌子的平面(XY)和躯干的矢状平面(XZ)的偏差。还对所有轨迹的速度分布和曲率进行了量化。结果我们假设,在功能性任务约束和物体的作用下进行的运动比在假想或物体不存在的情况下进行的运动会更偏离最小的加速度曲线模型。这些距离与预测的最小冲击模型的轨迹偏差显示为取决于三个变量:物体存在,物体方向和运动平面。当受试者完成杯子到达时,他们的动作比勺子到达时更弯曲。杯子伸手可及的物体当前状态显示出比想象中和缺少的情况更大的曲率。对于所有运动,XZ运动平面中的曲率均大于XY平面中的曲率。结论实施的最小加加速度轨迹模型不足以生成这些ADL的功能轨迹。为了使对象能够在机器人治疗环境中进行功能任务训练,必须考虑由对象承受能力和功能任务约束引起的偏差。我们强调的主要差异包括轨迹依赖:对象存在,对象方向和运动平面。希望能够在ADLER环境下练习ADL,我们希望为患者提供一种能够产生最佳结果和康复的治疗范例。

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