无标记运动捕捉系统对 Landing Error Scoring System 进行自动量化分析。
Automated Quantification of the Landing Error Scoring System With a Markerless Motion-Capture System.
机构信息
Department of Exercise and Sport Science, The University of North Carolina, Chapel Hill.
Department of Kinesiology, University of Connecticut, Storrs.
出版信息
J Athl Train. 2017 Nov;52(11):1002-1009. doi: 10.4085/1062-6050-52.10.12. Epub 2017 Oct 19.
CONTEXT
The Landing Error Scoring System (LESS) can be used to identify individuals with an elevated risk of lower extremity injury. The limitation of the LESS is that raters identify movement errors from video replay, which is time-consuming and, therefore, may limit its use by clinicians. A markerless motion-capture system may be capable of automating LESS scoring, thereby removing this obstacle.
OBJECTIVE
To determine the reliability of an automated markerless motion-capture system for scoring the LESS.
DESIGN
Cross-sectional study.
SETTING
United States Military Academy.
PATIENTS OR OTHER PARTICIPANTS
A total of 57 healthy, physically active individuals (47 men, 10 women; age = 18.6 ± 0.6 years, height = 174.5 ± 6.7 cm, mass = 75.9 ± 9.2 kg).
MAIN OUTCOME MEASURE(S): Participants completed 3 jump-landing trials that were recorded by standard video cameras and a depth camera. Their movement quality was evaluated by expert LESS raters (standard video recording) using the LESS rubric and by software that automates LESS scoring (depth-camera data). We recorded an error for a LESS item if it was present on at least 2 of 3 jump-landing trials. We calculated κ statistics, prevalence- and bias-adjusted κ (PABAK) statistics, and percentage agreement for each LESS item. Interrater reliability was evaluated between the 2 expert rater scores and between a consensus expert score and the markerless motion-capture system score.
RESULTS
We observed reliability between the 2 expert LESS raters (average κ = 0.45 ± 0.35, average PABAK = 0.67 ± 0.34; percentage agreement = 0.83 ± 0.17). The markerless motion-capture system had similar reliability with consensus expert scores (average κ = 0.48 ± 0.40, average PABAK = 0.71 ± 0.27; percentage agreement = 0.85 ± 0.14). However, reliability was poor for 5 LESS items in both LESS score comparisons.
CONCLUSIONS
A markerless motion-capture system had the same level of reliability as expert LESS raters, suggesting that an automated system can accurately assess movement. Therefore, clinicians can use the markerless motion-capture system to reliably score the LESS without being limited by the time requirements of manual LESS scoring.
背景
着陆错误评分系统（LESS）可用于识别下肢受伤风险较高的个体。LESS 的局限性在于评分者从视频回放中识别运动错误，这既耗时又费力，因此可能限制了临床医生的使用。无标记运动捕捉系统可能能够实现 LESS 评分的自动化，从而消除这一障碍。
目的
确定自动无标记运动捕捉系统对 LESS 评分的可靠性。
设计
横断面研究。
地点
美国军事学院。
患者或其他参与者
共有 57 名健康、活跃的个体（47 名男性，10 名女性；年龄=18.6±0.6 岁，身高=174.5±6.7cm，体重=75.9±9.2kg）。
主要观察指标
参与者完成了 3 次跳跃着陆试验，这些试验由标准摄像机和深度摄像机记录。他们的运动质量由 LESS 评分专家（标准视频记录）和自动 LESS 评分软件（深度摄像机数据）根据 LESS 评分表进行评估。如果至少有 3 次跳跃着陆试验中的 2 次出现 LESS 项目，则我们会记录一个错误。我们计算了每个 LESS 项目的κ统计量、流行度和偏差调整κ（PABAK）统计量以及百分比一致性。我们评估了 2 名专家评分者之间的组内信度以及共识专家评分与无标记运动捕捉系统评分之间的组内信度。
结果
我们观察到 2 名专家 LESS 评分者之间具有可靠性（平均κ=0.45±0.35，平均 PABAK=0.67±0.34；百分比一致性=0.83±0.17）。无标记运动捕捉系统与共识专家评分具有相似的可靠性（平均κ=0.48±0.40，平均 PABAK=0.71±0.27；百分比一致性=0.85±0.14）。然而，在这两种 LESS 评分比较中，5 个 LESS 项目的可靠性都较差。
结论
无标记运动捕捉系统与 LESS 评分专家具有相同的可靠性水平，这表明自动化系统可以准确评估运动。因此，临床医生可以使用无标记运动捕捉系统可靠地对 LESS 进行评分，而不会受到手动 LESS 评分时间要求的限制。
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