|
|
Processing Sensor Data from a Wearable Device by Machine Learning
Hlavačka, Martin ; Dobeš, Petr (referee) ; Herout, Adam (advisor)
The goal of this master's thesis is to analyze the situation of wearable devices with the Android Wear operating system and recognition capabilities of various movement activities using neural networks. The primary focus is therefore on identifying and describing the most appropriate tool for recognizing dynamic movements using machine learning methods based on data obtained from this type of devices. The practical part of the thesis then comments on the implementation of a stand-alone Android Wear application capable of recording and formatting data from sensors, training the neural network in a designed external desktop tool, and then reusing trained neural network for motion recognition directly on the device.
|
|
|
Activity Recognition from Moving Object Trajectories
Schwarz, Ivan ; Zendulka, Jaroslav (referee) ; Pešek, Martin (advisor)
The aim of this thesis is a development of a system for trajectory-based periodic pattern recognition and following GPS trajectory classification. This system is designed according to a performed analysis of techniques of data mining in moving object data and furthermore, on recent research on a subject of a trajectory-based activity recognition. This system is implemented in C++ programming language and experiments addresing its effectiveness are performed.
|
|
|
Processing Sensor Data from a Wearable Device by Machine Learning
Hlavačka, Martin ; Dobeš, Petr (referee) ; Herout, Adam (advisor)
The goal of this master's thesis is to analyze the situation of wearable devices with the Android Wear operating system and recognition capabilities of various movement activities using neural networks. The primary focus is therefore on identifying and describing the most appropriate tool for recognizing dynamic movements using machine learning methods based on data obtained from this type of devices. The practical part of the thesis then comments on the implementation of a stand-alone Android Wear application capable of recording and formatting data from sensors, training the neural network in a designed external desktop tool, and then reusing trained neural network for motion recognition directly on the device.
|
|
|
Activity recognition in a smart home setting
Fiklík, Vladimír ; Kadlec, Rudolf (advisor) ; Brom, Cyril (referee)
The aim of this work was to implement and compare several activity recognition algorithms which could be used in a smart home environment and would be able to determine the current activity of an observed subject (virtual agent) in the smart home using only data gathered by elementary observations of the environment. Such algorithms are useful in several areas, for example to improve behavior of various virtual agents, making them more aware of actions of the other agents. The algorithms used in this thesis are based on Dynamic Bayesian Networks and have ability to determine whether the observed activity has been completed or just interrupted. An easily extensible 3D interactive simulator of a smart home environment was created to meet the needs of activity recognition and used to gather data for the learning and testing phases of the algorithms. The test subjects were human-controlled virtual agents.
|
|
|
Activity Recognition from Moving Object Trajectories
Schwarz, Ivan ; Zendulka, Jaroslav (referee) ; Pešek, Martin (advisor)
The aim of this thesis is a development of a system for trajectory-based periodic pattern recognition and following GPS trajectory classification. This system is designed according to a performed analysis of techniques of data mining in moving object data and furthermore, on recent research on a subject of a trajectory-based activity recognition. This system is implemented in C++ programming language and experiments addresing its effectiveness are performed.
|
guest ::
