Graduate Program in Biomechanics and Movement Science (BIOMS), University of Delaware, Newark, Delaware, United States of America.
Department of Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, United States of America.
PLoS One. 2024 Nov 18;19(11):e0311773. doi: 10.1371/journal.pone.0311773. eCollection 2024.
Robotic devices are commonly used to quantify sensorimotor function of the upper limb after stroke; however, the availability and cost of such devices make it difficult to facilitate implementation in clinical environments. Tablets (e.g. iPad) can be used as devices to facilitate rehabilitation but are rarely used as assessment tools for the upper limb. The current study aimed to implement a tablet-based Maze Navigation Task to examine complex upper-limb movement in individuals with chronic stroke. We define complex upper-limb movement as reaching movements that require multi-joint coordination in a dynamic environment. We predicted that individuals with stroke would have more significant spatial errors, longer movement times, and slower speeds compared to controls with increasing task complexity. Twenty individuals with chronic stroke who had a variety of arm and hand function (Upper extremity Fugl-Myer 52.8 ± 18.3) and twenty controls navigated eight pseudorandomized mazes on an iPad using a digitizing stylus. The task was designed to elicit reaching movements engaging both the shoulder and elbow joints. Each maze became increasingly complex by increasing the number of 90° turns. We instructed participants to navigate each maze as quickly and accurately as possible while avoiding the maze's boundaries. Sensorimotor behavior was quantified using the following metrics: Error Time (time spent hitting or outside boundaries), Peak Speed, Average Speed, and Movement Time, Number of Speed Peaks. We found that individuals with stroke had significantly greater Error Time for all maze levels (all, p < 0.01), while both speed metrics, Movement Time and Number of Speed Peaks were significantly lower for several levels (all, p < 0.05). As maze complexity increased, the performance of individuals with stroke worsened only for Error Time while control performance remained consistent (p < 0.001). Our results indicate that a complex movement task on a tablet can capture temporal and spatial impairments in individuals with stroke, as well as how task complexity impacts movement quality. This work demonstrates that a tablet is a suitable tool for the assessment of complex movement after stroke and can serve to inform rehabilitation after stroke.
机器人设备常用于量化中风后上肢的感觉运动功能；然而，此类设备的可用性和成本使其难以在临床环境中实施。平板电脑（例如 iPad）可作为促进康复的设备使用，但很少用作上肢评估工具。本研究旨在实施基于平板电脑的迷宫导航任务，以检查慢性中风患者的复杂上肢运动。我们将复杂的上肢运动定义为在动态环境中需要多关节协调的上肢运动。我们预测，与对照组相比，随着任务复杂性的增加，中风患者的空间误差会更大，运动时间更长，速度更慢。20 名上肢运动功能各不相同的慢性中风患者（上肢 Fugl-Meyer 评分为 52.8 ± 18.3）和 20 名对照组使用数字手写笔在 iPad 上导航 8 个伪随机迷宫。该任务旨在诱发涉及肩部和肘部关节的上肢运动。每个迷宫通过增加 90°转弯的数量变得越来越复杂。我们指示参与者尽可能快速准确地导航每个迷宫，同时避免迷宫的边界。使用以下指标量化感觉运动行为：误差时间（撞击或超出边界的时间）、峰值速度、平均速度和运动时间、速度峰值数。我们发现，中风患者在所有迷宫水平上的误差时间明显更大（所有，p < 0.01），而运动时间和速度峰值数的两个速度指标在几个水平上明显更低（所有，p < 0.05）。随着迷宫复杂性的增加，中风患者的表现仅在误差时间方面恶化，而对照组的表现保持一致（p < 0.001）。我们的结果表明，平板电脑上的复杂运动任务可以捕捉中风患者的时间和空间损伤，以及任务复杂性如何影响运动质量。这项工作表明，平板电脑是评估中风后复杂运动的合适工具，并可以为中风后的康复提供信息。
