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Recognition of vehicles using signals sensed by smartphone
Nevěčná, Leona ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Thanks to the development in recent years, the placement of miniaturized sensors such as accelerometers, gyroscopes, magnetometers, global positioning system receivers (GPS), microphones or others to commercially sold smartphones is increasing. Use of these sensors (which are to be found in the smartphone) for human activity recognition with health care improvement in mind is a discussed theme. Advantages of the use of smartphone for human movement monitoring lies in the fact that it is a device that the person measured carries with them and there are no additional costs. The disadvantages are a limited storage and battery. Therefore, only accelerometer, gyroscope, magnetometer, and microphone were chosen because their combination achieves best results. GPS sensor was excluded for its lack of reliability in sampling and for being energy demanding. Features were computed from the measured data and used for learning of the classification model. The highest accuracy was achieved with the use of a machine learning method called Random Forest. The main goal of this work was to create an algorithm for transportation mode recognition using signals sensed by a smartphone. The created algorithm succeeds in classification of walk, car, bus, tram, train, and bike in 97.4 % with 20 % holdout validation. When tested on a new set of data from the tenth volunteer, the resulting accuracy counted as average form classification recall for each transportation mode reached 90.49 %.
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Methods for the evaluation of postural stability
Nevěčná, Leona ; Janoušek, Oto (referee) ; Koťová, Markéta (advisor)
This study deals with methods for evaluation of postural stability. The main problem of posturography is lack of standardization in measurement and evaluation. There are presented some parameters affecting postural stability that are often taken into account. Those are parameters that might be changed while measuring, for example standing position, sampling duration or visual control. And others that are altered when evaluating is taking place, variability of those parameters, is much greater. Measurement protocol taking into account the most promising parameters was put together based on literary search. Changes in COP of ten subjects were measured with Wii Balance Board, according to the proposed measurement protocol. The data acquired from a particular subject were used to count values of parameters used in evaluation. After that the values were statistically analyzed. To compare each parameter, trajectory with good results according to other literature was chosen to pose as a reference. Final result is comparison firstly between importance of changes in parameters of measuring and secondly between success of detection of those changes of separate parameters.
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Recognition of vehicles using signals sensed by smartphone
Nevěčná, Leona ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Thanks to the development in recent years, the placement of miniaturized sensors such as accelerometers, gyroscopes, magnetometers, global positioning system receivers (GPS), microphones or others to commercially sold smartphones is increasing. Use of these sensors (which are to be found in the smartphone) for human activity recognition with health care improvement in mind is a discussed theme. Advantages of the use of smartphone for human movement monitoring lies in the fact that it is a device that the person measured carries with them and there are no additional costs. The disadvantages are a limited storage and battery. Therefore, only accelerometer, gyroscope, magnetometer, and microphone were chosen because their combination achieves best results. GPS sensor was excluded for its lack of reliability in sampling and for being energy demanding. Features were computed from the measured data and used for learning of the classification model. The highest accuracy was achieved with the use of a machine learning method called Random Forest. The main goal of this work was to create an algorithm for transportation mode recognition using signals sensed by a smartphone. The created algorithm succeeds in classification of walk, car, bus, tram, train, and bike in 97.4 % with 20 % holdout validation. When tested on a new set of data from the tenth volunteer, the resulting accuracy counted as average form classification recall for each transportation mode reached 90.49 %.
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Methods for the evaluation of postural stability
Nevěčná, Leona ; Janoušek, Oto (referee) ; Koťová, Markéta (advisor)
This study deals with methods for evaluation of postural stability. The main problem of posturography is lack of standardization in measurement and evaluation. There are presented some parameters affecting postural stability that are often taken into account. Those are parameters that might be changed while measuring, for example standing position, sampling duration or visual control. And others that are altered when evaluating is taking place, variability of those parameters, is much greater. Measurement protocol taking into account the most promising parameters was put together based on literary search. Changes in COP of ten subjects were measured with Wii Balance Board, according to the proposed measurement protocol. The data acquired from a particular subject were used to count values of parameters used in evaluation. After that the values were statistically analyzed. To compare each parameter, trajectory with good results according to other literature was chosen to pose as a reference. Final result is comparison firstly between importance of changes in parameters of measuring and secondly between success of detection of those changes of separate parameters.
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