背景：已经广泛研究了使用诸如双任务步态之类的双任务模型来评估老年人的认知运动表现。然而,空间限制和安全系数限制了其在远程评估中的应用。为了弥补差距，我们提出了一种基于视频处理的方法，使用20秒的重复肘部弯曲-伸展测试与双任务条件远程量化认知运动表现，称为基于视频的运动认知仪(MCM)。
方法：这项研究纳入了18名年龄较大的参与者（年龄：78.6±6.5岁），他们被临床诊断为轻度认知障碍（MCI）或痴呆。要求参与者通过从两位数数字向后计数，进行20秒的重复性肘部屈伸运动和记忆运动。在测试过程中,前臂的所有运动均由摄像机记录.作为一个比较器，使用了经过验证的手腕穿戴传感器，这允许量化上肢运动学。
结果：结果显示，从基于视频的MCM和临床验证的基于传感器的MCM得出的双任务上肢运动性能之间具有良好的一致性（r≥0.530和ICC2,1≥0.681）。我们还观察到基于视频的MCM(屈曲时间，延长时间,和屈伸时间)和临床认知量表(最低精神状态检查，缩写：MMSE）。此外,双任务上肢电机性能的一些衡量标准(速度，屈曲时间，延长时间,和屈伸时间)与双任务步态速度(|r|≥0.557)相关，已发现与认知障碍有关。最后,在线性回归分析中,选定的双任务运动表现指标(屈曲时间)对预测MMSE评分敏感,具有统计学意义(校正后的R2=0.306,p=0.025).
结论：本研究提出了一种基于视频处理的方法，从简单方便的上肢功能测试中分析双任务上肢运动表现。结果表明，与基于传感器的MCM相比，基于视频的MCM的并发有效性。以及双任务上肢运动表现和临床验证的认知标志物(MMSE评分和双任务步态)之间的关联。未来的研究有必要探索这种解决方案的敏感性，以促进远程医疗应用中老年人认知运动表现的远程评估。
The use of dual-task model such as dual-task gait has been extensively studied to assess cognitive-motor performance among older adults. However, space restriction and safety factor limit its applications in remote assessment. To address the gap, we propose a video processing-based approach to remotely quantify cognitive-motor performance using a 20-s repetitive elbow flexion-extension test with dual-task condition, called video-based motoric-cognitive meter (MCM).
Eighteen older participants (age: 78.6 ± 6.5 years) who were clinically diagnosed as having either mild cognitive impairment or dementia were included in this study. Participants were asked to perform 20-s repetitive elbow flexion-extension exercise with a memory exercise by counting backward from a two-digit number. During the test, all movements of the forearm were recorded by a video camera. As a comparator, a validated wrist-worn sensor was used, which allowed quantifying upper extremity kinematics.
The results showed a good agreement (r ≥ 0.530 and ICC2,1 ≥ 0.681) between the derived dual-task upper extremity motor performance from the proposed video-based MCM and a clinically validated sensor-based MCM. We also observed moderate correlations (r ≥ 0.496) between some measures of video-based MCM (flexion time, extension time, and flexion-extension time) and clinical cognitive scale (Mini-Mental State Examination [MMSE]). Additionally, some measures of dual-task upper extremity motor performance (speed, flexion time, extension time, and flexion-extension time) were associated with dual-task gait speed (r ≥ 0.557), which has been found to be correlated with cognitive impairment. Lastly, the selected dual-task motor performance metric (flexion time) was sensitive to predict MMSE scores in linear regression analyses with statistical significance (adjusted R2 = 0.306, p = 0.025).
This study proposes a video processing-based approach to analyze dual-task upper extremity motor performance from a simple and convenient upper extremity function test. The results indicate concurrent validity of the proposed video-based MCM compared with the sensor-based MCM, and associations between dual-task upper extremity motor performance and clinically validated cognitive markers (MMSE scores and dual-task gait). Future studies are warranted to explore sensitivity of this solution to promote remote assessment of cognitive-motor performance among older adults in telehealth applications.
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