標題: | Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
作者: | Chen, Shih-Wei Lin, Sheng-Huang Liao, Lun-De Lai, Hsin-Yi Pei, Yu-Cheng Kuo, Te-Son Lin, Chin-Teng Chang, Jyh-Yeong Chen, You-Yin Lo, Yu-Chun Chen, Shin-Yuan Wu, Robby Tsang, Siny 電機工程學系 腦科學研究中心 Department of Electrical and Computer Engineering Brain Research Center |
關鍵字: | Parkinson's disease;Kernel-based principal component analysis;power spectrum;classification;quantification |
公開日期: | 10-十一月-2011 |
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. |
URI: | http://dx.doi.org/10.1186/1475-925X-10-99 http://hdl.handle.net/11536/15062 |
ISSN: | 1475-925X |
DOI: | 10.1186/1475-925X-10-99 |
期刊: | BIOMEDICAL ENGINEERING ONLINE |
Volume: | 10 |
Issue: | |
結束頁: | |
顯示於類別: | 期刊論文 |