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首页> 外文会议>IEEE international conference on Autonomic and Trusted Computing;IEEE international conference on Ubiquitous Intelligence and Computing;IEEE international conference on Scalable Computing and Communications and Its Associated Workshops;IEEE international conference on cloud and big data computing;IEEE international conference on internet of people >Recognizing Gait Pattern of Parkinson's Disease Patients Based on Fine-Grained Movement Function Features
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Recognizing Gait Pattern of Parkinson's Disease Patients Based on Fine-Grained Movement Function Features

机译：基于细粒度运动功能特征的帕金森病患者步态识别

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摘要

Parkinson's Disease (PD) is one of the typical movement disorder diseases, which has a serious impact on the daily lives of elderly people. In this paper, we propose a novel framework for PD gait pattern recognition. The key idea of our approach is to distinguish PD gait patterns from healthy individuals by accurately extracting gait features that indicate three aspects of movement function, i.e., Stability, symmetry and harmony. Concretely, our framework contains three steps: gait phase discrimination, feature extraction and selection and pattern classification. In the first step, we put forward a key event based method to discriminate four gait phases from plantar pressure data. In the second step, based on the gait phases, we extract and select gait features that can indicate stability, symmetry and harmony of movement function. In the third step, we recognize PD gait pattern by employing BP neural network. We evaluate the framework using a real plantar pressure dataset that contains 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework outperforms the baseline approach by 32.7% on average in terms of Precision, 42.2% on average in terms of Recall, and 24.0% on average in terms of AUC.
机译：帕金森氏病（PD）是典型的运动障碍疾病之一，严重影响老年人的日常生活。在本文中，我们提出了一种新的PD步态模式识别框架。我们方法的关键思想是通过准确地提取指示运动功能三个方面的步态特征来区分PD步态模式与健康个体，即稳定性，对称性和和谐性。具体来说，我们的框架包含三个步骤：步态识别，特征提取和选择以及模式分类。第一步，我们提出了一种基于关键事件的方法，可以从足底压力数据中区分出四个步态阶段。在第二步中，基于步态阶段，我们提取并选择可以表明运动功能的稳定性，对称性和和谐性的步态特征。第三步，我们采用BP神经网络识别PD步态模式。我们使用包含93个PD患者和72个健康个体的真实足底压力数据集评估框架。实验结果表明，我们的框架在基准精度方面平均优于基准方法32.7％，在召回方面平均42.2％，在AUC基础上平均24.0％。

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