Background

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability.

Research question

Can a structured machine learning approach achieve a more accurate prediction of running gait event timings from tibial accelerometry, compared to the previously utilised heuristic approaches?

Methods

Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. 3D tibial acceleration and ground reaction force data from 93 rearfoot runners were captured. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals.

Results

Both a structured perceptron model (median absolute error of stance time estimation: 10.00 ± 8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 ± 5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 ± 9.52 ms). Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running.

Significance

The machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory.

中文翻译：

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通过后足运动中的胫骨加速度来预测步态事件：一种结构化的机器学习方法

背景

初始接触和脚趾离开的步态事件检测对于进行步态分析至关重要，它允许派生诸如站立时间之类的参数。存在基于启发式的方法，可以根据胫骨加速度计估算这些关键步态事件。但是，这些方法是针对非常具体的加速度曲线量身定制的，在处理较大的数据集和固有的生物学变异性时可能会带来复杂性。

研究问题

与以前使用的启发式方法相比，结构化机器学习方法能否通过胫骨加速度计获得更准确的跑步步态时间预测？

方法

基于力的事件检测充当标准量度，以评估预测步态事件的准确性，可重复性和敏感性。捕获了来自93个后足跑步者的3D胫骨加速度和地面反作用力数据。启发式方法和两种结构化机器学习方法被用来从胫骨加速度信号中得出初始接触，脚趾离开和站立时间。

结果

结构化的感知器模型（站立时间估计的中位数绝对误差：10.00±8.73 ms）和结构化的递归神经网络模型（站立时间估计的中位数绝对误差：6.50±5.74 ms）都明显优于现有的启发式方法（中位数绝对误差）姿态时间估计：11.25±9.52 ms）。因此，结果表明，结构化的递归神经网络机器学习模型提供了在地面上水平跑步过程中步态事件及其派生的站立时间的最准确和一致的估计。

意义

机器学习方法似乎不受数据内对象间和对象间变化的影响，从而可以在后脚地面上跑步时准确而有效地自动输出数据。此外，即使在实验室外，也可以在长时间测量期间进行实时监控和生物反馈。