Feldhege Frank, Richter Katherina, Bruhn Sven, Fischer Dagmar-C, Mittlmeier Thomas
Department of Traumatology, Hand and Reconstructive Surgery, Rostock University Medical Centre, Rostock, Germany; Department of Paediatrics, Rostock University Medical Centre, Rostock, Germany.
Institute of Sports Science, University of Rostock, Germany.
Gait Posture. 2021 Oct;90:422-426. doi: 10.1016/j.gaitpost.2021.09.179. Epub 2021 Sep 16.
The ability for independent bipedal locomotion is an important prerequisite for autonomous mobility and participation in everyday life. Walking requires not only a functional musculoskeletal unit but relies on coordinated activation of muscles and may even require cognitive resources. The time-resolved monitoring of the position of joints, feet, legs and other body segments relative to each other alone or in combination with simultaneous recording of ground reaction forces and concurrent measurement of electrical muscle activity, using surface electromyography, are well-established tools for the objective assessment of gait.
The Gait Real-time Analysis Interactive Lab (GRAIL) has been introduced for gait analysis in a highly standardized and well-controlled virtual environment. However, apart from high computing capacity and sophisticated software required to run the system, handling of GRAIL data is challenging due to the utilization of different software packages resulting in a huge amount of data stored using different file formats and different sampling rates. These issues make gait analysis even with such a sophisticated instrument rather tedious, especially within the frame of an experimental or clinical study.
A user-friendly Matlab based toolset for automated processing of motion capturing data recorded using the GRAIL, with the inherent option for batch analysis was developed.
The toolset allows the reading, resampling, filtering and synchronization of data stored in different input files recorded with the GRAIL. It includes a coordinate-based algorithm for the detection of initial contact and toe-off events to split and normalize data relative to gait cycles. Batch processing of multiple measurements and automatic detection of outliers is possible.
The authors hope that the toolset will be useful to the research community and invite everyone to use, modify or implement it in their own work.
独立双足行走的能力是自主移动和参与日常生活的重要前提。行走不仅需要一个功能正常的肌肉骨骼单元，还依赖于肌肉的协调激活，甚至可能需要认知资源。单独或结合地面反作用力的同步记录以及使用表面肌电图同时测量肌肉电活动，对关节、足部、腿部和其他身体节段相对于彼此位置的时间分辨监测，是客观评估步态的成熟工具。
步态实时分析交互式实验室（GRAIL）已被引入用于在高度标准化和严格控制的虚拟环境中进行步态分析。然而，除了运行该系统所需的高计算能力和复杂软件外，由于使用了不同的软件包，GRAIL数据的处理具有挑战性，这导致大量数据以不同的文件格式和不同的采样率存储。这些问题使得即使使用如此精密的仪器进行步态分析也相当繁琐，尤其是在实验或临床研究的框架内。
开发了一个基于Matlab的用户友好型工具集，用于自动处理使用GRAIL记录的运动捕捉数据，并具有批量分析的固有选项。
该工具集允许读取、重新采样、过滤和同步存储在使用GRAIL记录的不同输入文件中的数据。它包括一种基于坐标的算法，用于检测初始接触和脚趾离地事件，以相对于步态周期分割和标准化数据。可以对多个测量进行批量处理并自动检测异常值。
作者希望该工具集将对研究界有用，并邀请每个人在自己的工作中使用、修改或实施它。
