Kincaid Clay, Johnson Paula, Charles Steven K
Mechanical Engineering, Brigham Young University, Provo, Utah 84602, United States of America.
Neuroscience, Brigham Young University, Provo, Utah 84602, United States of America.
Biomed Phys Eng Express. 2023 Mar 10;9(3). doi: 10.1088/2057-1976/acb159.
Although upper-limb movement impairments are common, the primary tools for assessing and tracking impairments in clinical settings are limited. Markerless motion capture (MMC) technology has the potential to provide a large amount of quantitative, objective movement data in routine clinical use. Many past studies have focused on whether MMC are sufficiently accurate. However, another necessary step is to create meaningful clinical tests that can be administered via MMC in a robust manner. Four conventional upper-limb motor tests common in clinical assessments (visually guided movement, finger tapping, postural tremor, and reaction time) were modified so they can be administered via a particular MMC sensor, the Leap Motion Controller (LMC). In this proof-of-concept study, we administered these modified tests to 100 healthy subjects and present here the successes and challenges we encountered. Subjects generally found the LMC and the graphical user interfaces of the tests easy to use. The LMC recorded movement with sufficiently high sampling rate (>106 samples/s), and the rate of LMC malfunctions (mainly jumps in time or space) was low, so only 1.9% of data was discarded. However, administration of the tests also revealed some significant weaknesses. The visually guided movement test was easily implemented with the LMC; the modified reaction time test worked reasonably well with the LMC but is likely more easily implemented with other existing technologies; and the modified tremor and finger tapping tests did not work well because of the limited bandwidth of the LMC. Our findings highlight the need to develop and evaluate motor tests specifically suited for MMC. The real strength of MMC may not be in replicating conventional tests but rather in administering new tests or testing conditions not possible with conventional clinical tests or other technologies.
尽管上肢运动障碍很常见,但临床环境中用于评估和跟踪障碍的主要工具却很有限。无标记运动捕捉(MMC)技术有潜力在常规临床应用中提供大量定量、客观的运动数据。过去许多研究都集中在MMC是否足够准确上。然而,另一个必要步骤是创建有意义的临床测试,这些测试可以通过MMC以稳健的方式进行。对临床评估中常见的四项传统上肢运动测试(视觉引导运动、手指敲击、姿势性震颤和反应时间)进行了修改,以便可以通过特定的MMC传感器——Leap Motion控制器(LMC)进行测试。在这项概念验证研究中,我们对100名健康受试者进行了这些修改后的测试,并在此展示我们遇到的成功和挑战。受试者普遍认为LMC和测试的图形用户界面易于使用。LMC以足够高的采样率(>106样本/秒)记录运动,LMC故障发生率(主要是时间或空间上的跳跃)较低,因此仅1.9%的数据被丢弃。然而,测试的实施也揭示了一些明显的弱点。视觉引导运动测试很容易通过LMC实施;修改后的反应时间测试与LMC配合得还算不错,但可能用其他现有技术实施起来更容易;而修改后的震颤和手指敲击测试由于LMC的带宽有限而效果不佳。我们的研究结果强调了开发和评估特别适合MMC的运动测试的必要性。MMC的真正优势可能不在于复制传统测试,而在于实施传统临床测试或其他技术无法实现的新测试或测试条件。