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Alternative Title
Name pinyin
DONG Xiaoge
School number
0701 数学
Subject category of dissertation
07 理学
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Parkinson’s disease (PD) and Dementia (DM) are common longterm
progressive nero-degenerative diseases caused by cognitive impairments. Patients often endure different levels of the motor system disturbance. It has been found that gait provides many measurable indicators of motor disability and impairment which could contribute to the diagnosis and classification of nerodegenerative diseases. Gait analysis using wearable
devices has been applied to analyze the gait disturbance of patients. Based on the 3axis acceleration data and 3-axis angular velocity data collected by a wearable device, instrumental movement unit (IMU) in a freeliving environment, this thesis studies the differences in gait patterns between patients with nerodegenerative diseases and healthy individuals. Starting from signal data preprocessing, each gait cycle will be identified.
Gait features in the temporal domain, spatial domain as well as signal features of angular velocity are extracted from each circle and its subphases and analyzed. We use the classification model to classify patients with neurodegenerative disease and healthy controls.
By performing feature selection, we obtain the best combination of gait features and the best classification model that could distinguish patients from healthy controls.
Similarly, the classification model is applied to classify patients with Parkinson’s disease and Dementia. And through the analysis of gait features, we obtain the differences in gait features of patients with PD, DM and healthy controls.
As the progress of Parkinson’s disease gets deeper, the motor impairments get worse, and the greater risk of falling they will endure which will cause great damage to their health or even risk their lives. Data from IMU could be also used to identify daily life activities such as falls. A fall detection algorithm combing threshold and classification model is proposed. Fall phase segmentation has been performed and signal features are extracted in each subphase. Combining functional principal component scores of fall data together with signal features extracted from each subphase, our algorithm has a very good performance.

Other Abstract

帕金森病(Parkinson’s disease) 和痴呆症(Dementia) 是由认知障碍引起的常见
装置(Instrumental Movement Unit) 采集的3 轴加速度数据和3 轴角加速度数据,研
我们识别了每个步态周期, 提取并分析了时域、空间上的步态特征,以及角速度的

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References List

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