We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by incorporating a motion classifier that adaptively updates the detector’s threshold parameter. Our second technique uses a long short-term memory (LSTM) recurrent neural network to classify zero-velocity events from raw inertial data, in contrast to the majority of zero-velocity detection methods that rely on basic statistical hypothesis testing. We demonstrate that both of our proposed detectors achieve higher accuracies than existing detectors for trajectories including walking, running, and stair-climbing motions. Additionally, we present a straightforward data augmentation method that is able to extend the LSTM-based model to different inertial sensors without the need to collect new training data.

中文翻译：

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用于脚踏式惯性导航的稳健数据驱动零速度检测

我们提出了两种用于检测零速事件的新技术，以改进脚踏式惯性导航。我们的第一种技术通过结合自适应更新检测器阈值参数的运动分类器来增强经典的零速度检测器。与依赖基本统计假设检验的大多数零速度检测方法相比，我们的第二种技术使用长短期记忆 (LSTM) 循环神经网络从原始惯性数据中对零速度事件进行分类。我们证明，我们提出的两个检测器都比现有的检测器在包括步行、跑步和爬楼梯运动在内的轨迹方面实现了更高的准确度。此外，