Automatic real-time gait event detection in children using deep neural networks

Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. 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. <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=1946#pack_2193">Training and test data </a> : Training and test data for the event detection algorithm </li> </ul>

足部接触与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首要步骤。通过对9092个步态周期测量数据的分析，我们利用长短期记忆（LSTM）人工神经网络构建了预测模型。该性能最优的模型在识别足部接触与离地事件时，平均误差分别达到10毫秒和13毫秒，超越了基于启发式方法的流行方法。本项研究涉及以下软件/数据包：

<ul>
<li>训练与测试数据<a href="https://simtk.org/frs?group_id=1946#pack_2193">链接</a>：事件检测算法的训练与测试数据</li>
</ul>