Department of Physical Therapy Education, College of Health Professions, SUNY Upstate Medical University, Syracuse, NY, USA.
Sci Rep. 2023 Feb 1;13(1):1813. doi: 10.1038/s41598-023-29091-0.
We developed a computer vision-based three-dimension (3D) motion capture system employing two action cameras to examine fine hand motor skill by tracking an object manipulated by a hand. This study aimed to examine the accuracy and feasibility of this approach for detecting changes in a fine hand motor skill. We conducted three distinct experiments to assess the system's accuracy and feasibility. We employed two high-resolution, high-frame-rate action cameras. We evaluated the accuracy of our system in calculating the 3D locations of moving object in various directions. We also examined the system's feasibility in identifying improvement in fine hand motor skill after practice in eleven non-disabled young adults. We utilized color-based object detection and tracking to estimate the object's 3D location, and then we computed the object's kinematics, representing the endpoint goal-directed arm reaching movement. Compared to ground truth measurements, the findings demonstrated that our system can adequately estimate the 3D locations of a moving object. We also showed that the system can be used to measure the endpoint kinematics of goal-directed arm reaching movements to detect changes in fine hand motor skill after practice. Future research is needed to confirm the system's reliability and validity in assessing fine hand motor skills in patient populations.
我们开发了一种基于计算机视觉的三维(3D)运动捕捉系统,使用两个动作摄像机来通过跟踪手部操作的物体来检查精细手部运动技能。本研究旨在检验该方法检测精细手部运动技能变化的准确性和可行性。我们进行了三个不同的实验来评估系统的准确性和可行性。我们使用了两个高分辨率、高帧率的动作摄像机。我们评估了我们的系统在计算物体在不同方向上的 3D 位置的准确性。我们还检查了该系统在 11 名非残疾年轻成年人练习后识别精细手部运动技能提高的可行性。我们利用基于颜色的目标检测和跟踪来估计物体的 3D 位置,然后计算物体的运动学,代表端点目标导向的手臂运动。与地面实况测量相比,研究结果表明,我们的系统可以充分估计运动物体的 3D 位置。我们还表明,该系统可用于测量目标导向手臂运动的端点运动学,以检测练习后精细手部运动技能的变化。需要进一步的研究来确认该系统在评估患者人群中的精细手部运动技能方面的可靠性和有效性。