The accurate identification of dynamic change of limb length discrepancy (LLD) in non-clinical settings is of great significance for monitoring gait function change in people's everyday lives. How to search for advanced techniques to measure LLD changes in non-clinical settings has always been a challenging endeavor in recent related research. In this study, we have proposed a novel approach to accurately measure the dynamic change of LLD outdoors by using deep learning and wearable sensors. The basic idea is that the measurement of dynamic change of LLD was considered as a multiple gait classification task based on LLD change that is clearly associated with its gait pattern. A hybrid deep learning model of convolutional neural network and long short-term memory (CNN-LSTM) was developed to precisely classify LLD gait patterns by discovering the most representative spatial-temporal LLD dynamic change features. Twenty-three healthy subjects were recruited to simulate four levels of LLD by wearing a shoe lift with different heights. The Delsys TrignoTM system was implemented to simultaneously acquire gait data from six sensors positioned on the hip, knee and ankle joint of two lower limbs respectively. The experimental results showed that the developed CNN-LSTM model could reach a higher accuracy of 93.24% and F1-score of 93.48% to classify four different LLD gait patterns when compared with CNN, LSTM, and CNN-gated recurrent unit(CNN-GRU), and gain better recall and precision (more than 92%) to detect each LLD gait pattern accurately. Our model could achieve excellent learning ability to discover the most representative LLD dynamic change features for classifying LLD gait patterns accurately. Our technical solution would help not only to accurately measure LLD dynamic change in non-clinical settings, but also to potentially find out lower limb joints with more abnormal compensatory change caused by LLD.

中文翻译：

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使用深度学习和可穿戴传感器的肢体长度差异动态变化的新颖测量方法

在非临床环境下准确识别肢体长度差异（LLD）的动态变化对于监测人们日常生活中的步态功能变化具有重要意义。如何寻找先进的技术来测量非临床环境下LLD的变化一直是近年来相关研究中的一个具有挑战性的工作。在这项研究中，我们提出了一种利用深度学习和可穿戴传感器准确测量户外 LLD 动态变化的新方法。基本思想是，LLD动态变化的测量被认为是基于与其步态模式明显相关的LLD变化的多步态分类任务。开发了卷积神经网络和长短期记忆的混合深度学习模型（CNN-LSTM），通过发现最具代表性的时空LLD动态变化特征，对LLD步态模式进行精确分类。招募了 23 名健康受试者，通过佩戴不同高度的增高鞋来模拟四个级别的 LLD。Delsys TrignoTM 系统可同时从分别位于两个下肢的髋关节、膝关节和踝关节的六个传感器获取步态数据。实验结果表明，与 CNN、LSTM 和 CNN 门控循环单元（CNN-GRU）相比，所开发的 CNN-LSTM 模型对四种不同的 LLD 步态模式的分类准确率高达 93.24%，F1 分数高达 93.48%。 ），并获得更好的召回率和精确度（超过 92%），以准确检测每个 LLD 步态模式。我们的模型可以实现出色的学习能力，发现最具代表性的LLD动态变化特征，从而准确地对LLD步态模式进行分类。我们的技术解决方案不仅有助于在非临床环境下准确测量LLD动态变化，而且有可能发现LLD引起的更多异常代偿性变化的下肢关节。