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<p>We developed a hand kinematics analysis method for manual dexterity in post-stroke patients, utilizing real-time tracking and three-dimensional (3D) visualization of movement performance through a virtual box and block test (BBT) with a leap motion controller; additionally, we validated its reliability and concurrent validity to propose it as a valuable evaluation tool. We performed virtual-BBT for 1&#xa0;min in a stroke patient group (SPG, n = 15) and a healthy adult group (HAG, n = 15). Virtual-BBT was tracked in real time, and cubes were counted by holding the virtual cube and performing hand movements. The test–retest reliability by number of trials showed significant correlation in the SPG (less paretic: intraclass correlation coefficient (ICC) = 0.582, 95% confidence interval (CI) [0.104–0.839], <i>p</i> &lt; 0.01; more paretic: ICC = 0.634, 95% CI [0.191–0.861], <i>p</i> &lt; 0.001) and in the HAG (dominant: ICC = 0.605, 95% CI [0.134–0.851], p &lt; 0.01; non-dominant: ICC = 0.739, 95% CI [0.379–0.904], <i>p</i> &lt; 0.01). The concurrent validity of virtual and real-BBT showed large correlation for less paretic (r = 0.76, <i>p</i> &lt; 0.001) and paretic (r = 0.86, <i>p</i> &lt; 0.001) hands in the SPG. The dominant (r = 0.73, <i>p</i> = 0.002) and non-dominant (r = 0.75, <i>p</i> &lt; 0.001) hands in the HAG showed large correlation. Virtual-BBT analyzes manual dexterity characteristics in 3D by holding a virtual cube and tracking movement performance, and reduces the subjectivity of classical evaluation.</p>

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Development, reliability, and concurrent validity of the virtual box and block test for manual dexterity and kinematic analysis in stroke patients and healthy adults

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Subok Kim,
  • Sunmin Kim,
  • Sanghun Jang,
  • Onseok Lee

摘要

We developed a hand kinematics analysis method for manual dexterity in post-stroke patients, utilizing real-time tracking and three-dimensional (3D) visualization of movement performance through a virtual box and block test (BBT) with a leap motion controller; additionally, we validated its reliability and concurrent validity to propose it as a valuable evaluation tool. We performed virtual-BBT for 1 min in a stroke patient group (SPG, n = 15) and a healthy adult group (HAG, n = 15). Virtual-BBT was tracked in real time, and cubes were counted by holding the virtual cube and performing hand movements. The test–retest reliability by number of trials showed significant correlation in the SPG (less paretic: intraclass correlation coefficient (ICC) = 0.582, 95% confidence interval (CI) [0.104–0.839], p < 0.01; more paretic: ICC = 0.634, 95% CI [0.191–0.861], p < 0.001) and in the HAG (dominant: ICC = 0.605, 95% CI [0.134–0.851], p < 0.01; non-dominant: ICC = 0.739, 95% CI [0.379–0.904], p < 0.01). The concurrent validity of virtual and real-BBT showed large correlation for less paretic (r = 0.76, p < 0.001) and paretic (r = 0.86, p < 0.001) hands in the SPG. The dominant (r = 0.73, p = 0.002) and non-dominant (r = 0.75, p < 0.001) hands in the HAG showed large correlation. Virtual-BBT analyzes manual dexterity characteristics in 3D by holding a virtual cube and tracking movement performance, and reduces the subjectivity of classical evaluation.