BackgroundThe 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).MethodTwelve 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 DiscussionThe 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.ConclusionThis 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.

Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system that results from the degeneration of dopaminergic neurons in the substantia nigra. Abnormal gait begins in the early stage and becomes severe as the disease progresses; therefore, the assessment of gait becomes an important issue in evaluating the progression of PD and the effectiveness of treatment. To provide a clinically useful gait assessment in environments with budget and space limitations, such as a small clinic or home, we propose and develop a portable method utilizing the monocular image sequences of walking to track and analyze a Parkinsonian gait pattern. In addition, a centroid tracking algorithm is developed and used here to enhance the method of quantifying kinematic gait parameters of PD in different states. Twelve healthy subjects and twelve mild patients with PD participate in this study. This method requires one digital video camera and subjects with two joint markers attached on the fibula head and the lateral malleolus of the leg. All subjects walk with a natural pace in front of a video camera during the trials. Results of our study demonstrate the stride length and walking velocity significantly decrease in PD without drug compared to PD with drug in both proposed method and simultaneous gait assessment performed by GAITRite(®) system. In gait initiation, step length and swing velocity also decrease in PD without drug compared to both PD with drug and controls. Our results showed high correlation in gait parameters between the two methods and prove the reliability of the proposed method. With the proposed method, quantitative measurement and analysis of Parkinsonian gait could be inexpensive to implement, portable within a small clinic or home, easy to administer, and simple to interpret. Although this study is assessed Parkinsonian gait, the proposed method has the potential to help clinicians and researchers assess the gait of patients with other neuromuscular diseases, such as traumatic brain injury and stroke patients.