In this thesis, we focused on designing wearable human activity recognition (WHAR) systems. As the first step, we conducted a thorough research over the publications during the recent ten years in this area. Then, we proposed an all-purpose architecture for designing the software of WHAR systems. Afterwards, among various applications of these wearable systems, we decided to work on wearable virtual fitness coach device which can recognize various types and intensities of warm-up exercises that an athlete performs. We first proposed a basic hardware platform for implementing the WHAR software. Afterwards, the software design was done in two phases. In the first phase, we focused on four simple activities to be recognized by the wearable device. We used Weka machine learning tool to build a mathematical model which could recognize the four activities with the accuracy of 99.32%. Moreover, we proposed an algorithm to measure the intensity of the activities with the accuracy of 93%. In the second phase, we focused on eight complex warm-up exercises. After building the mathematical model, the WHAR system could recognize the eight activities with the accuracy of 95.60%.
|Advisor:||Mozumdar, Mohammad M. R.|
|Commitee:||Chassiakos, Anastasios, Khoo, I-Hung|
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 55/01M(E), Masters Abstracts International|
|Subjects:||Electrical engineering, Computer science|
|Keywords:||Decision tree, Machine learning, Ubiquitous computing, Virtual fitness coach, Wearable human activity recognition, Wearable technology|
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