Gait pattern detection for amputated prosthetic using fuzzy algorithm
Conventional gait rehabilitation treatment does not provide quantitative and graphical information on abnormal gait kinematics, and the match of the intervention strategy to the underlying clinical presentation may be limited by clinical expertise and experience. Amputated patient with prosthetic le...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2015
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| Online Access: | http://eprints.uthm.edu.my/1280/2/AHMAD%20FAISAL%20BIN%20ABDULLAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1280/1/24p%20AHMAD%20FAISAL%20BIN%20ABDULLAH.pdf http://eprints.uthm.edu.my/1280/3/AHMAD%20FAISAL%20BIN%20ABDULLAH%20WATERMARK.pdf |
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| Summary: | Conventional gait rehabilitation treatment does not provide quantitative and graphical information on abnormal gait kinematics, and the match of the intervention strategy to the underlying clinical presentation may be limited by clinical expertise and experience. Amputated patient with prosthetic leg suffered with gait deviation due to variety causes commonly alignment and fitting problem. Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. The work included in this project focuses on developing a system to measure the angular displacement of human joint of lower part with patients having this problem and then applying gait phase detection using intelligent algorithm. The developed prototype has three inertial measurement units (IMU) sensor to measure and quantify body gait on thigh, shank and foot. The data from specific placement sensor on body part was evaluated and process in Arduino and MATLAB via serial communication. IMU provides the orientation of two axes and from this, it determined elevated position of each joint by using well established trigonometry technique in board to generate displacement angle during walking. The data acquired from the motion tests was displayed graphically through GUI MATLAB. A fuzzy inference system (FIS) was implementing to improve precision of the detection of gait phase from obtained gait trajectories. The prototype and FIS system showed satisfactory performance and has potential to emerge as a tool in diagnosing and predicting the pace of the disease and a possible feedback system for rehabilitation of prosthetic patients. |
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