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ECG biometrics using deep learning
Repčík, Tomáš ; Mézl, Martin (oponent) ; Vičar, Tomáš (vedoucí práce)
This diploma thesis makes a comprehensive overview of various approaches to the usage of ECG as biometry. ECG of people was measured with ECG capable wristband for training and testing purposes. The measurements were gathered during the four-month period. The neural networks of various types were trained on these data, and the feedforward convolutional neural networks have the best performance. These models reached a true acceptance rate 98,9% and a true rejection rate 99,5% on average. After training, the models have been visualised with a variety of techniques and essentials parts of ECG for verification have been described. The thesis also describes the first implementation of the Android application.
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Detection of car accident and collapse by Android smartphone
Repčík, Tomáš ; Ronzhina, Marina (oponent) ; Maděránková, Denisa (vedoucí práce)
Bachelor thesis “Detection of car accident and collapse by Android smartphone” describes theoretical basics of a collapse and a car accident. With help of 23 volunteers, data has been collected from daily tasks, simulating collapses and driving. Based on that a model with capability to classify a car accident was created. The detection of collapse uses neural network with accuracy of 86 %. The detection of car accident uses accelerometer and GPS. To indicate the car accident a high acceleration created by the accident and absence of moving car is required. Next, the application was tested by all available data and by the volunteers in daily life, who used the application for 520 h.
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Detection Of Collapse By Android Smartphone
Repčík, Tomáš
The bachelor’s study is focused to design and build an Android application for the detection of collapse, which is enhanced by new techniques coming from a sphere of the artificial intelligence modified for smartphones. The application uses accelerometer outputs which are in suspicious moments analysed by the neural network. The artificial intelligence is based on simulated events of collapse and events which resemble a fall of a person. The study describes data collected from 20 people. To provide the best results of training, the most convenient and useful features were selected by multiple approaches. Total accuracy of the collapse detection reached 93 %, with 9 % and 13 % of false positive and false negative detections, respectively.
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Detection of car accident and collapse by Android smartphone
Repčík, Tomáš ; Ronzhina, Marina (oponent) ; Maděránková, Denisa (vedoucí práce)
Bachelor thesis “Detection of car accident and collapse by Android smartphone” describes theoretical basics of a collapse and a car accident. With help of 23 volunteers, data has been collected from daily tasks, simulating collapses and driving. Based on that a model with capability to classify a car accident was created. The detection of collapse uses neural network with accuracy of 86 %. The detection of car accident uses accelerometer and GPS. To indicate the car accident a high acceleration created by the accident and absence of moving car is required. Next, the application was tested by all available data and by the volunteers in daily life, who used the application for 520 h.
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