Lidstone Daniel E, Porcher Louise M, DeBerardinis Jessica, Dufek Janet S, Trabia Mohamed B
J Am Podiatr Med Assoc. 2019 Nov;109(6):416-425. doi: 10.7547/17-118. Epub 2018 Nov 14.
Monitoring footprints during walking can lead to better identification of foot structure and abnormalities. Current techniques for footprint measurements are either static or dynamic, with low resolution. This work presents an approach to monitor the plantar contact area when walking using high-speed videography.
Footprint images were collected by asking the participants to walk across a custom-built acrylic walkway with a high-resolution digital camera placed directly underneath the walkway. This study proposes an automated footprint identification algorithm (Automatic Identification Algorithm) to measure the footprint throughout the stance phase of walking. This algorithm used coloration of the plantar tissue that was in contact with the acrylic walkway to distinguish the plantar contact area from other regions of the foot that were not in contact.
The intraclass correlation coefficient (ICC) demonstrated strong agreement between the proposed automated approach and the gold standard manual method (ICC = 0.939). Strong agreement between the two methods also was found for each phase of stance (ICC > 0.78).
The proposed automated footprint detection technique identified the plantar contact area during walking with strong agreement with a manual gold standard method. This is the first study to demonstrate the concurrent validity of an automated identification algorithm to measure the plantar contact area during walking.
在行走过程中监测足迹有助于更好地识别足部结构和异常情况。当前用于足迹测量的技术要么是静态的，要么是动态的，分辨率较低。这项工作提出了一种使用高速摄像技术在行走时监测足底接触面积的方法。
让参与者走过一条定制的丙烯酸步道，在步道正下方放置一台高分辨率数码相机来收集足迹图像。本研究提出了一种自动足迹识别算法（自动识别算法），用于测量行走支撑阶段的整个足迹。该算法利用与丙烯酸步道接触的足底组织的颜色来区分足底接触区域与足部其他未接触区域。
组内相关系数（ICC）表明，所提出的自动方法与金标准手动方法之间具有高度一致性（ICC = 0.939）。在支撑的每个阶段，两种方法之间也发现了高度一致性（ICC > 0.78）。
所提出的自动足迹检测技术在行走过程中识别出了足底接触区域，与手动金标准方法高度一致。这是第一项证明自动识别算法在测量行走过程中足底接触面积方面具有同时效度的研究。
