Gait Quantitative Analysis In Patients With Parkinson’s Disease Based On Ground Reaction Force

Parkinson’s disease (PD) is a kind of nervous system degenerative disease, most of whose patients are the middle-aged and the elderly. The main symptoms of this disease are static tremor, muscle rigidity, bradykinesia and gait disorders, among which gait disorders is characterized by lower extremity range of motion reducing, walking with small steps, difficulty of turns, and freezing of gait. These symptoms seriously affect the normal behavior and life of patients with Parkinson’s disease and bring a huge burden to them and their family. So it is very urgent to effectively diagnose and treat Parkinson’s disease. However, clinically speaking at the present stage, in the process of diagnosis and treatment for Parkinson’s disease patients physicians often depend on clinical experience which exists a certain degree of subjectivity, thus leading to the result that the evaluation and treatment on patients cannot be exact enough according to their own condition. So it is necessary to introduce the objective measuring device and method, which can quantitatively assess the degree of patient’s condition and rehabilitation, so as to assist physicians to diagnose and treat the patients effectively.Currently the objective measurement device applied to auxiliary diagnosis and treatment of Parkinson’s disease at home and abroad mainly include acceleration sensor, gyroscope sensor, hand-held camera, MRI instrument, the flexible force sensor, single three-dimensional force platform. The wear of the device based on the acceleration sensor and the gyroscope sensor is convenient, but generally there is a big error in the measurement process; Hand-held camera needing manual operation is not convenient, and easy to cause jitter error; The complexity of the MRI instrument based on image analysis is higher while the flexible force sensor only can measure the pressure distribution information, and they are both very expensive; The single three-dimensional force platform can only measure for static or single-step motion which is unable to get the complete information of the gait cycle. Aiming at these shortcomings, based on five single three-dimensional force platform this paper designed a gait channel which is used to obtain ground reaction force signal in the process of walking, and on this basis the writer combined traditional time domain analysis and nonlinear analysis method to study on the quantitative analysis of Parkinson’s disease patient’s gait. In this paper, the main content is as follows:(1) This paper introduced the main clinical symptoms of Parkinson’s disease and elaborated the significance of using objective measurement device to quantitatively analyze patient’s condition, respectively describing the research status of Parkinson’s disease based on gait feature and other feature.(2) In view of the symptoms of Parkinson’s disease patients’abnormal gait, this paper built a gait analysis system. The demanding analysis of the system and the basic structure of system hardware platform and software platform are mainly introduced in this paper, and then the data acquisition module, data playback module, database management module and software basic operation process of software platform are specifically introduced.(3) The gait feature extraction method based on ground reaction force is presented in detail in this paper, including using detrended fluctuation analysis for the calculation of fractal scaling exponent and phase space reconstruction for the calculation of recurrence period density entropy value, and the extraction of some of the traditional time domain feature parameter, among which fractal scaling exponent is used to reflect the long-range correlation of the walking gait and recurrence period density entropy value is used to quantitatively describe the repetition period of the walking gait.(4) Having collected the ground reaction force information of normal youth, normal elderly and elderly Parkinson’s patients in the natural walking state, this paper uses fractal scaling exponent to quantitatively analyze the long-range correlation of Parkinson’s disease patients’gait and recurrence period density entropy value to conduct the repeated quantitative analysis to Parkinson’s disease patients’gait, and does the contrast analysis with normal people in the control group. This paper also extracted some traditional time domain feature parameters from the gait signal and made correlation analysis with the scaling exponent and recurrence period density extracted by two kinds of nonlinear method. The analysis results show that the walking gait signal of Parkinson’s disease and normal control group have significant difference in terms of stability, long-range correlation and repetition period.(5) An identification test between Parkinson’s disease patients and normal control group was conducted based on the gait feature. First, stepwise regression analysis of multivariate linear regression was used for the gait feature selection, and then used support vector machine algorithm of supervised learning combining with cross validation to do the classification identification between Parkinson’s disease patients and normal control group, which got a good classification effect.