Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, UK.
Med Eng Phys. 2012 Jul;34(6):740-6. doi: 10.1016/j.medengphy.2011.09.018. Epub 2011 Oct 12.
Complex Regional Pain Syndrome (CRPS) is a condition that causes a long-term burning pain in a limb or part of a limb and it can cause various degrees of the physical functional performance deterioration. Objective assessment of physical functional performance of patients is one critical component to evaluate the therapy outcome for CRPS. This paper aims to investigate the feasibility of assessing the physical performance of patients with Complex Regional Pain Syndrome based on the analysis of gait data recorded by an accelerometer in short walking distances. Ten subjects with CRPS and ten control subjects were recruited. Thirty three features were extracted from each recording. A machine learning method, Multilayer perceptron neural-networks (MLP), was applied to classify the normal and abnormal gait patterns from data obtained on a 2.4m performance evaluation test. The best classification accuracy (99.38%) was achieved using 3 features selected by a step-wise-forward method. To further validate its performance, an independent test set including 14 cases extracted from data obtained on a 20 m performance evaluation test was adopted. A prediction accuracy of 85.7% was obtained.
复杂性区域疼痛综合征（CRPS）是一种导致肢体或肢体部分长期灼烧疼痛的疾病，它会导致不同程度的身体功能表现恶化。对患者身体功能表现的客观评估是评估 CRPS 治疗效果的一个关键组成部分。本文旨在研究基于加速度计记录的短距离行走过程中的步态数据来评估复杂性区域疼痛综合征患者身体性能的可行性。招募了 10 名 CRPS 患者和 10 名对照者。从每个记录中提取了 33 个特征。使用多层感知器神经网络（MLP）的机器学习方法对 2.4m 性能评估测试中获得的数据进行正常和异常步态模式的分类。通过逐步向前法选择的 3 个特征可实现最佳分类准确率（99.38%）。为了进一步验证其性能，采用了从 20m 性能评估测试中获得的数据中提取的包括 14 例的独立测试集。获得了 85.7%的预测准确率。
