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Advanced sleep quality estimation
Benáček, Petr ; Ředina, Richard (referee) ; Filipenská, Marina (advisor)
This thesis deals with the assessment of sleep quality using modern deep learning methods. The thesis describes metrics for automatic classification of sleep stages. A selected database of sleep data is discussed. Due to the low number of data in the wakefulness phase, different methods of data augmentation are described and implemented. Models based on 1D convolutional networks are the basis for the classification. As a result, models for binary classification and classification of 3 and 4 sleep phases are prepared. Finally, sleep quality metrics are calculated using these models and the results are compared with the literature.
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Smartwatch App for Sports Training and Competitions
Dohnalík, Pavel ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of the work is to create an application for a smart watch, which will allow you to measure races and trainings, or create localization data for this activity. The application is implemented for mobile devices with the Android and iOS operating systems. The Wear OS operating system is supported for smart watches. The thesis describes the theory of programming for mobile operating systems and programming for the operating system Wear OS. The practical part describes the design, implementation and testing. For the implementation of the mobile application as well as for the smart watch application I decided to choose Flutter framework and programming language Dart. The resulting application allow users to measure races and workouts.
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Wearables Development Technologies for YSoft SafeQ
Stárek, Jan ; Goldmann, Tomáš (referee) ; Orság, Filip (advisor)
Wearable devices grew in popularity in recent years. This fact contributes to increasing efforts to expand mobile and other applications to wearable devices. Exploring these possibilities, this thesis summarizes information on wearable devices and their typical operating systems, including available tools and restrains these systems offer. Based on the conclusion of theoretical part of this thesis an application for wearable devices is designed and implemented, specifically an application for printers that operate via SafeQ system developed in Y Soft Corporation.
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ECG based human authentication and identification
Waloszek, Vojtěch ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
In the past years, utilization of ECG for verification and identification in biometry is investigated. The topic is investigated in this thesis. Recordings from ECG ID database from PhysioNet and our own ECG recordings recorded using Apple Watch 4 are used for training and testing this method. Many of the existing methods have proven the possibility of using ECG for biometry, however they were using clinical ECG devices. This thesis investigates using recordings from wearable devices, specifically smart watch. 16 features are extracted from ECG recordings and a random forest classifier is used for verification and identification. The features include time intervals between fiducial points, voltage difference between fiducial points and PR intervals variability in a recording. The average performance of verification model of 14 people is TRR 96,19 %, TAR 84,25 %.
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Advanced methods for sleep quality assessment
Doležalová, Anna ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
This diploma thesis is focused on advanced sleep assessment using deep learning. Metrics for sleep assessment and their use are described here. There are hearth rate and accelerometer data from Apple Watch used for classification. The basis for the classification was a model composed of 1D convolution networks in combination with recurrent neural network. LSTM and GRU were used as recurrent networks. Models were taught to classify into two, three and five phases. At last the resulting methods are compared.
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Advanced sleep quality estimation
Benáček, Petr ; Ředina, Richard (referee) ; Filipenská, Marina (advisor)
This thesis deals with the assessment of sleep quality using modern deep learning methods. The thesis describes metrics for automatic classification of sleep stages. A selected database of sleep data is discussed. Due to the low number of data in the wakefulness phase, different methods of data augmentation are described and implemented. Models based on 1D convolutional networks are the basis for the classification. As a result, models for binary classification and classification of 3 and 4 sleep phases are prepared. Finally, sleep quality metrics are calculated using these models and the results are compared with the literature.
|
|
|
Advanced methods for sleep quality assessment
Doležalová, Anna ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
This diploma thesis is focused on advanced sleep assessment using deep learning. Metrics for sleep assessment and their use are described here. There are hearth rate and accelerometer data from Apple Watch used for classification. The basis for the classification was a model composed of 1D convolution networks in combination with recurrent neural network. LSTM and GRU were used as recurrent networks. Models were taught to classify into two, three and five phases. At last the resulting methods are compared.
|
|
|
Smartwatch App for Sports Training and Competitions
Dohnalík, Pavel ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of the work is to create an application for a smart watch, which will allow you to measure races and trainings, or create localization data for this activity. The application is implemented for mobile devices with the Android and iOS operating systems. The Wear OS operating system is supported for smart watches. The thesis describes the theory of programming for mobile operating systems and programming for the operating system Wear OS. The practical part describes the design, implementation and testing. For the implementation of the mobile application as well as for the smart watch application I decided to choose Flutter framework and programming language Dart. The resulting application allow users to measure races and workouts.
|
|
|
ECG based human authentication and identification
Waloszek, Vojtěch ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
In the past years, utilization of ECG for verification and identification in biometry is investigated. The topic is investigated in this thesis. Recordings from ECG ID database from PhysioNet and our own ECG recordings recorded using Apple Watch 4 are used for training and testing this method. Many of the existing methods have proven the possibility of using ECG for biometry, however they were using clinical ECG devices. This thesis investigates using recordings from wearable devices, specifically smart watch. 16 features are extracted from ECG recordings and a random forest classifier is used for verification and identification. The features include time intervals between fiducial points, voltage difference between fiducial points and PR intervals variability in a recording. The average performance of verification model of 14 people is TRR 96,19 %, TAR 84,25 %.
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|
|
Wearables Development Technologies for YSoft SafeQ
Stárek, Jan ; Goldmann, Tomáš (referee) ; Orság, Filip (advisor)
Wearable devices grew in popularity in recent years. This fact contributes to increasing efforts to expand mobile and other applications to wearable devices. Exploring these possibilities, this thesis summarizes information on wearable devices and their typical operating systems, including available tools and restrains these systems offer. Based on the conclusion of theoretical part of this thesis an application for wearable devices is designed and implemented, specifically an application for printers that operate via SafeQ system developed in Y Soft Corporation.
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