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National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
Acquisition and classification of motion
 Tichá, Petra ; Čmiel, Vratislav (referee) ; Janoušek, Oto (advisor)
This diploma thesis deals with the acquisition and classification of movement using accelerometer and gyroscope data. The theoretical part contains biomechanics of movement, sensors used in the motion analysis and customization options and classification of measured data. A description of the components of the acquisition system, its implementation and placement on the body of the measured person are introduced in this work as well. To verify the functionality of the device, measured data was compared with the data measured by mobile application Sense-it. Classification of motion was inplemented by two methods in the Matlab software environment. The first one uses a vector of three parameters, the other classifies the movement by the absolute value of the difference in signal deflections.
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Acquisition And Classification Of Motion
 Tichá, Petra
This article deals with the acquisition and classification of human movement using accelerometer and gyroscope data. First part of the work summarizes the design and implementation of the acquisition system and its placement on the body of the measured person. Second part describes classification of accelerometer and gyroscope data (recorded using the acquisition system) on the three basic types of movement – sitting, walking and running. Classification algorithm is realized in Matlab workspace.
Detailed record
Acquisition and classification of motion
 Tichá, Petra ; Čmiel, Vratislav (referee) ; Janoušek, Oto (advisor)
This diploma thesis deals with the acquisition and classification of movement using accelerometer and gyroscope data. The theoretical part contains biomechanics of movement, sensors used in the motion analysis and customization options and classification of measured data. A description of the components of the acquisition system, its implementation and placement on the body of the measured person are introduced in this work as well. To verify the functionality of the device, measured data was compared with the data measured by mobile application Sense-it. Classification of motion was inplemented by two methods in the Matlab software environment. The first one uses a vector of three parameters, the other classifies the movement by the absolute value of the difference in signal deflections.
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