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Stress recognition using biological signals measured by wearable devices
Surkoš, Ondřej ; Vítek, Martin (referee) ; Smital, Lukáš (advisor)
With the growing importance of mental health in society and the increasing availability of wearable technology, biological signals offer a unique opportunity to monitor and manage stress in everyday life. The diploma thesis focuses on the automatic stress recognition of biological signals measured by wearable devices. Therefore, in the theoretical part, key terms related to stress and wearable devices are defined and selected biological signals relevant for stress detection are described. The work also presents several publicly available datasets and describes current stress recognition methods, together with the achieved results. The practical part of the work is devoted to the construction of the dataset, data preprocessing and the development of an algorithm for recognizing stress in the MATLAB program environment. In particular, machine learning techniques are used both for feature extraction and selection, as well as for the classics themselves. The performance of the proposed models, which reached an accuracy of up to 81.1 % in the case of the unified dataset, 97.1 % in the case of the WESAD dataset and 80 % in the case of the Non-EEG Biosignals dataset, are presented and discussed in the final part of the work, together with by finding a great influence of the methodology and the equipment used during data acquisition on the performance of individual models.
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Study of Using Wearable Devices
Hlavačka, Martin ; Dalecký, Štěpán (referee) ; Samek, Jan (advisor)
In recent years, the amount of wearables and level of their interaction with users is constantly increasing and deepening. This fact is followed by this thesis, in which I aimed on creating a overview study of best known and available devices on the market. In subsequent chapters the various options available for developing applications for these devices are analyzed. Based on the acquired knowledge content also contains development of demonstration app for the selected smartwatch Motorola Moto 360 sport. Created overview and analysis of development options provide knowledge for orientating and information required either for selection of wearable device or software development for it.
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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.
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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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Applications for Wearable Devices based on Android Wear OS
Šmejkal, Petr ; Zeman, Kryštof (referee) ; Hošek, Jiří (advisor)
The semestral project "Application for Android Wear wearables” describes basic principles of the communication network - M2M (machine to machine), H2H (human to human) and D2D (device to device). It is also focused on wearables especially smart watches, available operation systems for smart watches and system Android, Android Wear. In the practical part is described functionality, design and structure of developed application for Android wear smartwatch.
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Machine Learning-Aided Monitoring and Prediction of Respiratory and Neurodegenerative Diseases Using Wearables
Skibińska, Justyna ; Esposito, Anna (referee) ; Faundez-Zanuy, Marcos (referee) ; Hošek, Jiří (advisor)
This thesis focuses on wearables for health status monitoring, covering applications aimed at emergency solutions to the COVID-19 pandemic and aging society. The methods of ambient assisted living (AAL) are presented for the neurodegenerative disease Parkinson's disease (PD), facilitating 'aging in place' thanks to machine learning and around wearables - solutions of mHealth. Furthermore, the approaches using machine learning and wearables are discussed for early-stage COVID-19 detection. Firstly, a publicly available dataset containing COVID-19, influenza, and healthy control data was reused for research purposes. The solution presented in this thesis is considering the classification problem and outperformed the state-of-the-art methods, whereas the original paper introduced just anomaly detection. The proposed model in the thesis for early detection of COVID-19 achieved 78 % for the k-NN classifier. Moreover, a second dataset available on request was utilized for recognition between COVID-19 cases and two types of influenza. The scrutinisation in the form of the classification between the COVID-19 and Influensa groups is proposed as the extension to the research presented in the original paper. The accuracy of the distinction between COVID-19 cases and influenza in the middle of the pandemic was equal to 73 % thanks to the k-NN. Furthermore, the contribution as the classification model of two aforementioned combined datasets was provided, and COVID-19 cases were able to be distinguished from healthy controls with 73 % accuracy thanks to XGBoost algorithm. The undeniable advantage of the illustrated approaches is taking into consideration the incubation period and contagiousness of the disease. In addition, some solutions for the detection of the aforementioned aging society phenomenon are presented. This study explores the possibility of fusing computerised analysis of hypomimia and hypokinetic dysarthria for the spectrum of Czech speech exercises. The introduced dataset is unique in this field because of its diversity and myriad of speech exercises. The aim is to introduce a new techniques of PD diagnosis that could be easily integrated into mHealth systems. A classifier based on XGBoost was used, and SHAP values were used to ensure interpretability. The presented interpretability allows for the identification of clinically valuable biomarkers. Moreover, the fusion of video and audio modalities increased the balanced accuracy to 83 %. This methodology pointed out the most indicative speech exercise – tongue twister from the clinical point of view. Furthermore, this work belongs to just a few studies which tackle the subject of utilising multimodality for PD and this approach was profitable in contrast with a single modality. Another study, presented in this thesis, investigated the possibility of detecting Parkinson's disease by observing changes in emotion expression during difficult-to-pronounce speech exercises. The obtained model with XGBoost achieved 69 % accuracy for a tongue twister. The usage of facial features, emotion recognition, and computational analysis of tongue twister was proved to be successful in PD detection, which is the key novelty and contribution of this study. Additionally, the unique overview of potential methodologies suitable for the detection of PD based on sleep disorders was depicted.
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Secret Sharing Authentication Key Agreement
Ryšavá, Pavla ; Dzurenda, Petr (referee) ; Ricci, Sara (advisor)
Práce se zabývá implementací a vytvořením kryptografické knihovny a grafického uživatelského rozhraní (GUI) pro nově navržený protokol "Smlouva o autentizačním klíči na základě Shamirova sdílení tajemnství" (ang. "Shamir’s Secret Sharing-based Authenticated Key Agreement", zkráceně ShSSAKA). Protokol je založený na principu AKA (autentizovaná domluva klíče), Schnorrově podpisu a rozšířen Paillierovým schématem pro možnost podílení se více zařízení na podpisu a autentizaci. Prezentovány jsou také benchmarky na osobním počítači a RaspberryPi.
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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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Access and Backhaul Solutions for Cellular-Enabled Industrial Wearables
Saafi, Salwa ; Bečvář, Zdeněk (referee) ; Tölli, Antti (referee) ; Hošek, Jiří (advisor)
Smartphones are no longer the only portable devices changing the lives and daily routines of today’s digitally connected consumers. Smart glasses, watches, headsets, cameras, bands, trackers, monitors, and scanners are all examples of hands-free inherently mobile wearable devices that enable the emerging consumer and industrial applications. Similarly to customers who are ready to embrace life-changing experiences with new devices, companies and industries are also employing smart helpers and intelligent assistant systems to improve the efficiency of their automated processes and the productivity and safety of their workers. Not limited to the employment of smart helpers, the industrial digital transformation relies heavily on the deployment of communication infrastructures that utilize efficient cellular technologies to meet the dissimilar requirements of industrial applications. Motivated by these intelligent assistant systems and communication technologies, this dissertation focuses on the role of wearable technology and cellular connectivity in enabling the automation of vertical domains. Aiming to address the current technology gap behind cellular-enabled industrial wearables, the present work is dedicated to assessing the applicability of cellular connectivity to industrial wearables and developing efficient access and backhaul solutions for the support of the requirements of emerging industrial applications. The following outline of this dissertation is built around the main objectives as highlighted above and presents the main outcomes of this work, which include (i) a concise technology review capturing the evolution of the recent solutions proposed by the 3rd Generation Partnership Project (3GPP) for wearable devices and communications, (ii) an introduction to novel categories of industrial wearable applications with mid-end requirements that fall in-between the two extremes of high-end and low-end Fifth-Generation (5G) service classes, (iii) an assessment of the applicability of the emerging Reduced-Capability New Radio (NR RedCap) technology to the newly introduced wearable applications, (iv) an extension of the RedCap wearable communications with Device-to-Device (D2D) and Supplementary Uplink (SUL) capabilities for enhanced access network performance, (v) a cost-efficient backhaul selection solution based on Markov Decision Processes (MDPs) for time-sensitive wearable applications in an integrated terrestrial and non-terrestrial communication scenario, and (vi) a data-driven Artificial Intelligence (AI)-aided approach for the management of complex industrial networks with dissimilar device capabilities, communication solutions, and application requirements. A set of simulation and analytical models is developed to assess the relevant key performance indicators as part of the above contributions. Beyond indicating the need for technology improvement demanded by the efficient integration of wearable devices into cellular networks and the satisfaction of industrial application requirements, the numerical results reported in this dissertation confirm the network performance enhancements achieved by the access and backhaul solutions contributed in this work.
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Secret Sharing Authentication Key Agreement
Ryšavá, Pavla ; Dzurenda, Petr (referee) ; Ricci, Sara (advisor)
Práce se zabývá implementací a vytvořením kryptografické knihovny a grafického uživatelského rozhraní (GUI) pro nově navržený protokol "Smlouva o autentizačním klíči na základě Shamirova sdílení tajemnství" (ang. "Shamir’s Secret Sharing-based Authenticated Key Agreement", zkráceně ShSSAKA). Protokol je založený na principu AKA (autentizovaná domluva klíče), Schnorrově podpisu a rozšířen Paillierovým schématem pro možnost podílení se více zařízení na podpisu a autentizaci. Prezentovány jsou také benchmarky na osobním počítači a RaspberryPi.
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