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人体运动过程中比目鱼肌肌束长度的自动跟踪。

Automatic tracking of medial gastrocnemius fascicle length during human locomotion.

机构信息

Musculoskeletal Research Program, Griffith Health Institute, Griffith University, Queensland, Australia.

出版信息

J Appl Physiol (1985). 2011 Nov;111(5):1491-6. doi: 10.1152/japplphysiol.00530.2011. Epub 2011 Aug 11.

Abstract

During human locomotion lower extremity muscle-tendon units undergo cyclic length changes that were previously assumed to be representative of muscle fascicle length changes. Measurements in cats and humans have since revealed that muscle fascicle length changes can be uncoupled from those of the muscle-tendon unit. Ultrasonography is frequently used to estimate fascicle length changes during human locomotion. Fascicle length analysis requires time consuming manual methods that are prone to human error and experimenter bias. To bypass these limitations, we have developed an automatic fascicle tracking method based on the Lucas-Kanade optical flow algorithm with an affine optic flow extension. The aims of this study were to compare gastrocnemius fascicle length changes during locomotion using the automated and manual approaches and to determine the repeatability of the automated approach. Ultrasound was used to examine gastrocnemius fascicle lengths in eight participants walking at 4, 5, 6, and 7 km/h and jogging at 7 km/h on a treadmill. Ground reaction forces and three dimensional kinematics were recorded simultaneously. The level of agreement between methods and the repeatability of the automated method were quantified using the coefficient of multiple correlation (CMC). Regardless of speed, the level of agreement between methods was high, with overall CMC values of 0.90 ± 0.09 (95% CI: 0.86-0.95). Repeatability of the algorithm was also high, with an overall CMC of 0.88 ± 0.08 (95% CI: 0.79-0.96). The automated fascicle tracking method presented here is a robust, reliable, and time-efficient alternative to the manual analysis of muscle fascicle length during gait.

摘要

在人类运动过程中,下肢肌肉肌腱单元经历周期性的长度变化,这些变化以前被认为代表肌肉纤维长度的变化。此后,对猫和人类的测量表明,肌肉纤维长度的变化可以与肌肉肌腱单元的变化解耦。超声检查常用于估计人类运动过程中肌纤维长度的变化。肌纤维长度分析需要耗时的手动方法,容易出现人为错误和实验者偏见。为了克服这些限制,我们开发了一种基于 Lucas-Kanade 光流算法的自动肌纤维跟踪方法,并具有仿射光流扩展。本研究的目的是比较自动和手动方法在运动过程中腓肠肌肌纤维长度的变化,并确定自动方法的可重复性。使用超声检查 8 名参与者在跑步机上以 4、5、6 和 7 公里/小时的速度行走和以 7 公里/小时的速度慢跑时的腓肠肌肌纤维长度。同时记录地面反作用力和三维运动学。使用多重相关系数 (CMC) 来量化方法之间的一致性程度和自动方法的可重复性。无论速度如何,两种方法之间的一致性水平都很高,总体 CMC 值为 0.90 ± 0.09(95%CI:0.86-0.95)。算法的重复性也很高,总体 CMC 为 0.88 ± 0.08(95%CI:0.79-0.96)。这里提出的自动肌纤维跟踪方法是一种强大、可靠且高效的替代手动分析步态中肌纤维长度的方法。

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