Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
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DOI：
10.1186/1475-925x-10-99
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发表时间：
2011-11-10
影响因子：
3.9
通讯作者：
Tsang, Siny
中科院分区：
文献类型：
--
作者：
Chen, Shih-Wei;Lin, Sheng-Huang;Tsang, Siny
背景：特定步态模式的计算机辅助识别是帕金森病（PD）评估中的一个重要问题。本研究采用基于核函数的主成分分析（KPCA）方法，建立了一种基于计算机视觉的步态分析方法，用于PD的临床评估。方法：选取12例PD患者和12例健康成人，均无神经系统疾病史或运动障碍，并根据其“非PD”、“服药”和“停药”状态进行分类。参与者被要求穿着浅色衣服，以自然的速度通过一条装饰有海军窗帘的走廊进行三次步行试验。在稳态行走期间，参与者的步态表现由数码相机捕获用于步态分析。然后将收集的行走图像帧转换为二进制轮廓以进行降噪和压缩。使用开发的基于KPCA的方法，可以提取二进制轮廓内的特征，以定量确定步态周期时间、步长、步行速度和节奏。结果和讨论：基于KPCA的方法使用特征提取方法，经验证，该方法比传统的图像面积和主成分分析（PCA）方法在分类“非PD”对照和“停药/给药”方面更有效。PD患者。令人鼓舞的是，该方法具有较高的准确率，为80.51%，识别不同的步态。获得了定量的步态参数，并对步态进行了功率谱分析。我们发现，PD患者在行走过程中的缓慢和不规则的动作往往会将一些功率从主瓣频率转移到较低的频带。我们的研究结果表明，使用步态性能来评估运动功能的PD患者。结论：这种基于KPCA的方法只需要一个数码相机和装饰走廊设置的可行性。当前方法的易于使用和安装为临床医生和研究人员提供了一种低成本的解决方案来监测PD的进展和治疗。总之，所提出的方法提供了一种替代方案，以执行步态分析与PD患者。
Background: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA).Method: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence.Results and Discussion: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD.Conclusion: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.