Department for Health, University of Bath, Bath, United Kingdom.
Department of Computer Science, University of Bath, Bath, United Kingdom.
PLoS One. 2021 Aug 9;16(8):e0248608. doi: 10.1371/journal.pone.0248608. eCollection 2021.
The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [-10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [-10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [-15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNet's source code publicly available for step event detection in treadmill running when force data are not available.
足部触地和离地的准确检测在跑步生物力学评估中通常至关重要。用于步骤事件检测的金标准方法需要力数据，但并非总是可用。虽然基于运动学的算法也可以使用，但它们的准确性和通用性有限，通常需要根据速度或足部触地模式进行校正。本研究旨在开发 FootNet，这是一种用于在跑步机跑步中检测步事件的新型运动学和深度学习算法。收集了五个跑步机跑步数据集并进行处理，以获得节段和关节运动学，并使用力数据识别每个步态周期内的接触阶段。所提出的算法基于长短期记忆递归神经网络，将远端胫骨前后速度、踝关节背屈/跖屈角度以及足部质心的前后和垂直速度作为输入特征，以预测给定步态周期内的接触阶段。选择的模型结构经过五折交叉验证，最终模型在每个数据集的参与者子集（30%）中进行了测试。非参数 Bland-Altman 分析（偏差和[95%一致性界限]）和均方根误差（RMSE）用于比较 FootNet 与力数据步事件检测方法。还研究了检测误差与跑步速度、足部触地角度和坡度之间的关系。FootNet 优于先前发表的算法（足部触地偏差=0[-10,7]ms，RMSE=5ms；离地偏差=0[-10,10]ms，RMSE=6ms；接触时间偏差=0[-15,15]ms，RMSE=8ms），并且对不同的跑步速度、足部触地角度和坡度具有鲁棒性。当力数据不可用时，我们公开了 FootNet 的源代码，用于在跑步机跑步中进行步事件检测。
