|关键词||脑卒中偏瘫 多模态信息融合 运动行为识别 运动异常分析 康复疗效评定|
|英文摘要||Post-stroke hemiparesis is a condition caused by cerebrovascular blockage or rupture that lead to movement disorders of one side of the body. Movement disorders seriously affect the patients' daily activities and quality of life. The motor rehabilitation treatment after hemiparesis is essential to recover the patients' motor function. In order to alleviate the contradiction between supply and demand of current rehabilitation medical resources, some intelligent rehabilitation aid systems have been introduced into the field of rehabilitation to assist patients in completing the rehabilitation training tasks. Clinical studies have confirmed that the rehabilitation training with patients' active participation has a more positive effect on their motor function recovery. This will derive two scientific issues: on the one hand, it is necessary to accurately decode the patient's motion behaviors so as to achieve a flexible interaction between the patient and the aid system; on the other hand, it is necessary to accurately assess the patient's rehabilitation effect so as to adjust the training mode in time. The key technique to solve the above problems is how to extract important information about human motion behavior and limb motor function from the data which are related to the patient's motions. This technique is also the primary prerequisite for the realization of intelligent rehabilitation, which has become a key research direction in the field of rehabilitation medicine and rehabilitation engineering. This thesis focuses on the perceptual monitoring of human lower limb motion behaviors and quantitative assessment of limb motor function by analyzing multimodal human motion data.|
The main research contents and innovation points are as follows:
(1) A motion pattern classification method based on the fusion of brain-muscle physiological signals is proposed to qualitatively distinguish human lower limb multi-joint movements. Different lower limb exercises are considered. They are cycling, walking, and going up and down a step, which are the most common activities of daily living. Electroencephalogram (EEG), electromyogram (EMG) and mechanomyogram (MMG) signals were simultaneously recorded from the subjects while performing these lower limb motions. Then, a decision fusion analysis framework based on multimodal physiological information is proposed to establish the mapping relationship between the brain-muscle physiological signals and motion patterns. In this framework, a lot of advanced pattern classification algorithms and decision fusion algorithms are implemented. Sufficient data analysis experiments are carried out and the results show that the proposed method can effectively take the advantages of the complementarity between the brain and muscular activities to improve the recognition accuracy of motion patterns. A support vector machine based three-modal fusion model can achieve an average accuracy of 98.61%, which is significant higher than the commonly used two-modal fusion based methods or single-modal based methods.
(2) A joint angle estimation method based on the fusion of brain-muscle physiological signals is proposed to quantitatively estimate the continuous movements of human lower limb joints. We mainly study how to use physiological signals to accurately estimate the subjects' continuously changing joint angle during cycling exercises. The collected data includes EEG, EMG and MMG signals, as well as the joint angle signals of the hip, knee and ankle. Then, an estimation method based on multimodal regression fusion is proposed to establish the mapping relationship between the brain-muscle physiological signals and joint angles. A lot of comparative experiments are conducted. The results show that the proposed method can effectively reduce the estimation errors of the joint angles. The least squares support vector regression fusion model based three physiological signals obtain the mean estimated errors of 2.51, 2.81, and 3.13 degrees under different cycling load conditions, which is significant better than the most commonly used estimation methods based on the EMG signal, and also has good robustness against changes in cycling loads.
(3) An intelligent gait analysis method is proposed to qualitatively detect and quantitatively assess the gait abnormality of the post-stroke hemiparetic patients. A clinical walking trial involving the post-stroke hemiparetic patients and healthy volunteers is designed. Three of the most representative gait data, i.e., marker motion position, ground reaction force and EMG signals, were simultaneously acquired from these subjects during walking. Then, a gait abnormality analysis system is established based on these different modal gait data by using information fusion theory, which can be used to qualitatively detect the gait abnormal patterns after stroke. In order to further quantify the patients' lower limb walking ability, a novel probability based custom-defined gait score is designed for this system. Extensive data analysis experiments have been carried out. The results show that for the detection performance, the accuracy is as high as 98.21%. For the estimation performance, there is a significant strong correlation between the probability based gait scores and the clinical gait scale ratings. The absolute value of the correlation coefficient between them is 0.84, which validates the clinical effectiveness of the proposed method to some extent.
(4) A kind of physical and mental compound rehabilitation therapy, and a comprehensive rehabilitation assessment method based on multivariate data are proposed to study new treatment methods and rehabilitation effect assessment for the post-stroke hemiparetic patients. The movement disorders and psychological problems of the post-stroke hemiparetic patients during walking rehabilitation are analyzed. On this basis, the physical rehabilitation therapy and psychological rehabilitation therapy is appropriately combined to propose a physical and mental compound rehabilitation therapy, which can provide the patients with a dual treatment experience from body and mind. A clinical rehabilitation trial is designed. Post-stroke patients are randomly divided into two groups. For the study group, physical and mental compound therapy is performed for every patient. For the control group, only traditional physical therapy is carried out. Various data of the two groups of patients are collected before and after rehabilitation. Then, the walking ability of patients before and after rehabilitation is comprehensively assessed from many aspects by using a lot of data analysis methods. The results show that physical and mental compound rehabilitation therapy has a very positive effect on improving the walking ability of the post-stroke patients and is significantly better than traditional physical therapy.
|崔承坤. 面向偏瘫康复的人体下肢运动数据智能分析方法与实验[D]. 北京. 中国科学院大学,2018.|