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Use of Physiological Data to Analyze and Improve the User Experience
Štefeková, Nina ; Beran, Vítězslav (referee) ; Materna, Zdeněk (advisor)
This thesis is concerned with the research of the physiological symptoms of stress, the design and execution of an experiment that can induce short-term stress. Subsequently, the goal was to create a dataset from the experimentally obtained data and use it to train a machine learning model. Stress detection by such a model can be used to analyze the user experience. The proposed experiment uses a combination of stimuli, consisting of a relaxation part, a Stroop test and a web game that deliberately uses an unfriendly user interface. During it, data was recorded using an Empatica E4 device and then processed by an implemented application into the final dataset. A machine learning model that can detect short-term stress was then trained and evaluated. The KNN algorithm model evaluated by cross-validation achieves an accuracy of 84\% when the subject of the is known prior to the model. For an unknown subject, it is on average 78\%. The thesis provides this model and the resulting dataset, for further use. These results show that short-term stress detection is more challenging without prior knowledge of the subject.
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Comparison of heart activity sensing devices
Babicová, Martina ; Smital, Lukáš (referee) ; Smíšek, Radovan (advisor)
The goal of this work is comparison of heart activity sensing devices. However, an ECG record cannot be evaluated with the presence of muscle interference. Removing this noise is one of the needs for device success. The theoretical part represents electrophysiology of the heart, electrocardiography, various interferences types, theoretical basis for recording of biosignals including used devices and methods signal quality estimation. The practical part is SNR (signal-to-noise ratio) calculation. The Wavelet filter and Wiener filter-based wavelet domain are used to separate the useful and noise component.
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Comparison of specialized actigraphs with wearable devices in quantitative sleep analysis
Čech, Vladimír ; Zvončák, Vojtěch (referee) ; Mikulec, Marek (advisor)
Actigraphy and smart wearable devices provide similar functionality in quantitative sleep analysis. This work aims to verify how common wearable devices stand in comparison with a specialized actigraph used in clinical practice. The actigraph used in this work was Geneactiv Original and it was used to compare the sleep analysis results of eleven commercially available devices. The measurements for nine of the measured devices lasted for one week. Two devices were measured for six days. During sleep, the device was worn on the same hand as the actigraph, and at the same time a sleep diary was kept, in which data on the time of lying down, the time of awakening and the number of awakenings per night were recorded. After a week of measurement, the data obtained from the actigraph were evaluated by software and then compared with the data provided by the second device. In this work, data of total sleep time, sleep onset latency, number of awakenings during the night and sleep efficiency are compared. Ten of the eleven devices measured higher total sleep time and higher sleep efficiency than the actigraph. These devices measured higher total sleep time by 1.21 % – 12.06 % and measured higher sleep efficiency by 2.86 % - 13.86 %. One device, namely Fossil Sport, measured lower total sleep time by 9.02 % and lower sleep efficiency by 9.13 %. Sleep onset latency was the most distinct parameter. Wearable devices measured higher sleep time by 86.72 % – 1225.95 %. Neither device nor actigraph could reliably determine the number of awakenings during the night unless it was a significant physical activity during the night. From the results of the work, it is not possible to say in general that all commercially available devices would be a reliable substitute for actigraphy.
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Body Gestures Recognition With Using Wearable Devices
Kajzar, Aleš ; Zbořil, František (referee) ; Samek, Jan (advisor)
The goal of this master's thesis is to describe the possibilites of devices with operating system Android Wear, there is a description of Android Wear API and components, which are nowadays widely used in smart wearable devices. The thesis contains a description of recognition of dynamic gestures with the use of machine learning methods applied on data, which are provided by a smart device. In the practical part of this master's thesis is described an implemented library, which allows to train gestures and recognize them using FastDTW algorithm and inform a connected device about the recognized movement. Use of the library is shown on a demo application.
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Using Wearables for Medicine Applications
Abraham, Lukáš ; Rozman, Jaroslav (referee) ; Samek, Jan (advisor)
This bachelor thesis contains digest of interesting weareable devices which focus or are usedin medicine. Information about the applications for mobile devices and medicine which aremade for usual wearable devices can be found in this thesis as well. The solution which isapplied on weareable devices which are used in medicine is also described here. The solutionis made for wearable device (watch) Garmin VivoActive Hr and a mobile phone which runson Android platform. Watch VivoActive Hr is placed around the wrist and it takes a shotof the movement and the pulse of the person. Thanks to the accelerometer it can identifythe number of steps which were taken during the day by the person. These data are sentfrom the watch to the smart phone which runs on the Android platform and the applicationwhich evaluates these data is able to send an SMS or an email if the situation is evaluatedas an emergency.
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Physiological Data to Analyze and Improve the User Experience
Štěpánek, Daniel ; Beran, Vítězslav (referee) ; Materna, Zdeněk (advisor)
The goal of this thesis is to obtain dataset of physiological data for user emotions in order to analyze and improve human-computer interaction. This paper proposes of the use Empatica wristband to capture physiological data and Python for data processing. After evaluation, 94 % of intact data was obtained from the expected number of samples. Based on the obtained data, it is possible to better analyze the user experience and thus improve it.
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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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Wearable Devices: Possibilities of Using and Applications
Herec, Jan ; Luža, Radim (referee) ; Samek, Jan (advisor)
This thesis focuses on wearable devices that are currently actual topic. It presents their history, communication methods and capabilities of application. It further describes created solution that demonstrates the application of wearable devices for controlling a robot. The solution consists of a wearable device named Myo armband, an Android application and a robot on the platform ROS (Robot operating system). A Myo armband has a form of a bracelet that is placed on a forearm and contains sensors such as a magnetometer, a gyroscope, an accelerometer and an EMG (electromyographic sensors for sensing muscle activity on which base certain gestures are recognized). The application is designed for controlling a mobile robot capable of moving forward, backward and veer based on gestures and movements of a hand on which a Myo armband is attached. For this purpose, the application reads the relevant data from a wearable device using Bluetooth LE (Low Energy), then evaluates them and sends control messages via Wi-Fi technology to a robot. The description of a robot is discussed in more detail further below. The usage of the application (which is the main contribution of the author) and a Myo armband enable to control a set of mobile robots driven by the Robotic operating system (ROS).
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Classification of free living data
Rychtárik, Martin ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
The topic of this bachelor thesis is classification of free living data, captured by the accelerometer sensor of a smart phone. The first part of the thesis deals with the possibilities of recording daily activity using accelerometer and subsequent classification by neural network. In the next section, the data of eight different daily activities were recorded on ten people. An algorithm containing a neural network was created for the data in the MATLAB programming environment to automatically identify the activities. In the last part of the work the algorithm classification was compared with manually recorded reference and the results were statistically evaluated.
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Comparison of specialized actigraphs with wearable devices in quantitative sleep analysis
Čech, Vladimír ; Zvončák, Vojtěch (referee) ; Mikulec, Marek (advisor)
Actigraphy and smart wearable devices provide similar functionality in quantitative sleep analysis. This work aims to verify how common wearable devices stand in comparison with a specialized actigraph used in clinical practice. The actigraph used in this work was Geneactiv Original and it was used to compare the sleep analysis results of eleven commercially available devices. The measurements for nine of the measured devices lasted for one week. Two devices were measured for six days. During sleep, the device was worn on the same hand as the actigraph, and at the same time a sleep diary was kept, in which data on the time of lying down, the time of awakening and the number of awakenings per night were recorded. After a week of measurement, the data obtained from the actigraph were evaluated by software and then compared with the data provided by the second device. In this work, data of total sleep time, sleep onset latency, number of awakenings during the night and sleep efficiency are compared. Ten of the eleven devices measured higher total sleep time and higher sleep efficiency than the actigraph. These devices measured higher total sleep time by 1.21 % – 12.06 % and measured higher sleep efficiency by 2.86 % - 13.86 %. One device, namely Fossil Sport, measured lower total sleep time by 9.02 % and lower sleep efficiency by 9.13 %. Sleep onset latency was the most distinct parameter. Wearable devices measured higher sleep time by 86.72 % – 1225.95 %. Neither device nor actigraph could reliably determine the number of awakenings during the night unless it was a significant physical activity during the night. From the results of the work, it is not possible to say in general that all commercially available devices would be a reliable substitute for actigraphy.
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