Real-Time Estimation and Performance Evaluation of Human Motion Status for People with Hemiplegia

• Main Authors: Chien-Chih Chao, 趙建智
• Other Authors: Ching-Chih Tsai
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/47382780920352519404

碩士 === 國立中興大學 === 電機工程學系所 === 102 === The purposes of this thesis are to study the action of nerve hemiplegic patients by determining the physiological state of the movement via four designed physiological sensing modules, to propose two real-time estimation and performance evaluation methods of human states in movement by the four integrated sensing systems, and to design and implement the low-cost processing technologies. The measurements of muscle signals were analyzed, and the analytical results were applied to set the parameters, in order to control the drive motors to comply with bending of the knee and hip joints, and thus allow patients to return to normal gait without relying on assistive devices for walking. The main research methods in the thesis are divided into the following four aspects. The first one is to design, integrate and develop a smart sensing system that can detect the states of hemiplegia patient’s motion, This smart sensing system includes four subsystems: i). the foot pressure sensing subsystem placed on the bottom of feet; ii). the two-leg EMG sensing subsystem placed in the large muscle groups of thighs; iii). The pose posture and motion sensing subsystem to detect human activities; iv). the bending detection subsystem to detect bending knees. Via the interfacing technologies, these four sensing subsystems are integrated to understand the physiological responses and abnormal gait of nerve paralysis patients. The second one is to propose the real-time estimation and evaluation method to determine the states of hemiplegic patient’s motion. The third one is to propose the fuzzy real-time estimation and evaluation method to improve the correct detection rate and wide applicability to many patients. The fourth one is to capture and analyze the measurement signals by the LabVIEW software to verify the results of physiological intelligent sensing system with the two proposed real-time estimation and evaluation methods practically, in order to facilitate further the actual physiological signal intelligent sensing system to appropriately drive foot powered exoskeleton devices for hemiplegic patients. By detecting and analyzing the data of the experiments in real time, we confirm that the four physiological sensing systems together with both integrated real-time estimation and evaluation methods are effective in finding the states of movement of nerve hemiplegic patients.