Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

Shih Wei Chen, Sheng Huang Lin, Lun De Liao, Hsin Yi Lai, Yu Cheng Pei, Te Son Kuo, Chin Teng Lin, Jyh Yeong Chang, You Yin Chen, Yu Chun Lo, Shin Yuan Chen, Robby Wu, Siny Tsang

研究成果: 雜誌貢獻文章

18 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號99
期刊BioMedical Engineering Online
10
DOIs
出版狀態已發佈 - 十一月 10 2011
對外發佈Yes

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Biomaterials

指紋 深入研究「Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis」主題。共同形成了獨特的指紋。

  • 引用此

    Chen, S. W., Lin, S. H., Liao, L. D., Lai, H. Y., Pei, Y. C., Kuo, T. S., Lin, C. T., Chang, J. Y., Chen, Y. Y., Lo, Y. C., Chen, S. Y., Wu, R., & Tsang, S. (2011). Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis. BioMedical Engineering Online, 10, [99]. https://doi.org/10.1186/1475-925X-10-99