title: Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis.
abstract: 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).
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan. irradiance@so-net.net.tw
authors.forename: Shih-Wei
authors.initials: SW
authors.lastname: Chen
authors.forename: Sheng-Huang
authors.initials: SH
authors.lastname: Lin
authors.forename: Lun-De
authors.initials: LD
authors.lastname: Liao
authors.forename: Hsin-Yi
authors.initials: HY
authors.lastname: Lai
authors.forename: Yu-Cheng
authors.initials: YC
authors.lastname: Pei
authors.forename: Te-Son
authors.initials: TS
authors.lastname: Kuo
authors.forename: Chin-Teng
authors.initials: CT
authors.lastname: Lin
authors.forename: Jyh-Yeong
authors.initials: JY
authors.lastname: Chang
authors.forename: You-Yin
authors.initials: YY
authors.lastname: Chen
authors.forename: Yu-Chun
authors.initials: YC
authors.lastname: Lo
authors.forename: Shin-Yuan
authors.initials: SY
authors.lastname: Chen
authors.forename: Robby
authors.initials: R
authors.lastname: Wu
authors.forename: Siny
authors.initials: S
authors.lastname: Tsang
mesh_terms: D000465:Algorithms; D005684:Gait; D006801:Humans; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology*; D025341:Principal Component Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012107:Research Design; D014743:Videotape Recording; D016138:Walking
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