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Using smart watches and their functions to measure health parameters
Uher, Vojtěch ; Křivánková, Markéta (advisor) ; Mahrová, Andrea (referee)
Title: Utilization and preferences of smartwatches and their features for measuring health parameters in the adult population Objectives: The aim of this master's thesis is to analyze the utilization and preferences of smartwatches in the adult population and to examine their potential for measuring health parameters through a questionnaire survey. Methods: The research was conducted through a questionnaire survey. A questionnaire of our own design was utilized. The research section also included an analysis of parameters and functions of smartwatches. Results: The average age of the respondents who filled out the questionnaire for this diploma thesis was 31.2 years. 63 out of 103 probands use smartwatches to monitor their health status, with the most frequently measured parameter being heart rate. All 63 respondents follow here. The most used function associated with physical activity is tracking the number of steps. 91 respondents are interested in this data. Factors such as quality, accuracy, and design play a key role when choosing a smartwatch. According to the respondents, the most used smartwatches are from Apple and Garmin. Apple Watch is used by 42% of probands and the Garmin brand by 26% of probands. Respondents often prefer the Apple brand for its compatibility with other devices, and...
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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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Advanced sleep scoring
Dokoupilová, Daniela ; Novotná, Petra (referee) ; Filipenská, Marina (advisor)
This diploma thesis focuses on classification of sleep stages using a smart watch. Two signals were used – heart rate and acceleration. A model called TinySleepNet composed of convolutional neural network and LSTM was chosen for this task. The model was first trained for the classification of five sleep stages using only heart rate, achieving F1 score of 49%. Acceleration was converted into an SVM vector, on which the second model was trained. Due to the lack of information in the SVM vector, the model was trained only for binary classification of wake/sleep, achieving F1 score of 62.3%. Both SVM and heart rate were combined in the last model. The classification of heart rate and SVM vector into five sleep stages achieved F1 score of 51%. The calculated parameters evaluating sleep quality were then compared with data evaluated by a sleep expert.
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Validation of commercially available smartwatches as a human health/activity monitoring tool
Běhunčíková, Vendula ; Janoušek, Oto (referee) ; Němcová, Andrea (advisor)
This master's thesis deals with the topic of health and activity monitoring of individuals using smartwatches. The aim of the thesis was to collect a set of data according to a measurement protocol using various types of smartwatches, along with reference data from the Faros 180 device. The collected data from a total of nine smartwatches was synchronized, and the synchronized heart rate courses were evaluated using the mean absolute error (MAE) metric. The oxygen saturation and blood pressure parameters were statistically evaluated. In the final part of the thesis, the quality of ECG records obtained from the smartwatches and their diagnostic utility were assessed.
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Advanced security for blockchain transactions
Tran, Minh ; Člupek, Vlastimil (referee) ; Dzurenda, Petr (advisor)
Blockchain technology, especially Bitcoin, has revolutionized how we think about and manage financial transactions. However, with the increasing demand and usage of blockchain technology, the security of cryptocurrency wallets has become a critical concern. Threshold signatures offer a promising solution to this problem, allowing multiple parties to sign a transaction without revealing their private keys. This article presents an Android mobile Bitcoin wallet application that uses Schnorr-based threshold signatures. The application also deploys smartwatch integration for enhanced security and usability. This integration provides an additional layer of security by requiring physical confirmation from the user before approving any transaction. Our implementation provides a secure and efficient platform for managing Bitcoin assets using threshold signatures while also providing an intuitive and easy-to-use interface for interacting with the application.
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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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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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Sleep quality assessment
Dokoupilová, Daniela ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
The topic of this bachelor thesis is quality sleep assessment using signals acquired from Apple Watch. From measured signals – heart rate and motion, were extracted parameters for sleep classification using statistical testing. These parameters were later used for training the support vector machine model. The model was first trained to classify Wake and Sleep, then also to classify Wake, REM and NREM stages. The accuracy of Wake/Sleep classification was about 80 %. The accuracy of Wake/REM/NREM classification exceeded 58 %. Finally, sleep quality parameters were calculated and compared to the data rated by a sleep expert. The outcome for Wake/Sleep classification was close to the expert evaluation. The model for Wake/REM/NREM classification was less accurate and differed mainly in parameters concerning Wake stage.
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Advanced scoring of sleep data
Jagošová, Petra ; Novotná, Petra (referee) ; Ronzhina, Marina (advisor)
The master´s thesis is focused on advanced scoring of sleep data, which was performed using deep neural network. Heart rate data and the movement information were used for scoring measured using an Apple Watch smartwatch. After appropriate pre-processing, this data serves as input parameters to the designed networks. The goal of the LSTM network was to classify data into either two groups for sleep and wake or into three groups for wake, Non-REM and REM. The best results were achieved by network doing classification of sleep vs. wake using the accelerometer. The statistical evaluation of this best-designed network reached the values of sensitivity 71,06 %, specificity 57,05 %, accuracy 70,01 % and F1 score 81,42 %.
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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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