Stanford University Department of Bioengineering, Stanford, CA, United States of America.
Stanford University Department of Mechanical Engineering, Stanford, CA, United States of America.
PLoS One. 2019 Jan 31;14(1):e0211466. doi: 10.1371/journal.pone.0211466. eCollection 2019.
Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. If clean force plate strikes are present, the events can be automatically detected. Otherwise, annotation of gait events is performed manually, since reliable automatic tools are not available. Automatic annotation methods have been proposed for normal gait, but are usually based on heuristics of the coordinates and velocities of motion capture markers placed on the feet. These heuristics do not generalize to pathological gait due to greater variability in kinematics and anatomy of patients, as well as the presence of assistive devices. In this paper, we use a data-driven approach to predict foot-contact and foot-off events from kinematic and marker time series in children with normal and pathological gait. Through analysis of 9092 gait cycle measurements we build a predictive model using Long Short-Term Memory (LSTM) artificial neural networks. The best-performing model identifies foot-contact and foot-off events with an average error of 10 and 13 milliseconds respectively, outperforming popular heuristic-based approaches. We conclude that the accuracy of our approach is sufficient for most clinical and research applications in the pediatric population. Moreover, the LSTM architecture enables real-time predictions, enabling applications for real-time control of active assistive devices, orthoses, or prostheses. We provide the model, usage examples, and the training code in an open-source package.
足触地和足离地事件的标注是大多数定量步态分析工作流程后处理的初始步骤。如果有干净的力板冲击，事件可以自动检测。否则，需要手动标注步态事件，因为目前还没有可靠的自动工具。已经为正常步态提出了自动标注方法，但通常基于放置在脚上的运动捕捉标记的坐标和速度的启发式方法。由于患者运动学和解剖结构的可变性更大，以及辅助设备的存在，这些启发式方法不适用于病理步态。在本文中，我们使用数据驱动的方法从正常和病理步态儿童的运动学和标记时间序列中预测足触地和足离地事件。通过对 9092 个步态周期测量的分析，我们使用长短时记忆（LSTM）人工神经网络构建了一个预测模型。表现最好的模型分别以平均 10 毫秒和 13 毫秒的误差识别足触地和足离地事件，优于流行的基于启发式的方法。我们得出结论，我们的方法的准确性足以满足儿科人群中大多数临床和研究应用的需求。此外，LSTM 架构可以实现实时预测，为主动辅助设备、矫形器或假肢的实时控制应用提供了可能。我们在一个开源软件包中提供了模型、使用示例和训练代码。
