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Computer simulations are used to assess the impact of thermal insulation on the transfer of heat and moisture in the exterior walls of residential buildings. The effects of internal and external thermal insulation on a room in a terraced family house are compared using the COMSOL Multiphysics software package, evaluating the influence of the placement of expanded polystyrene thermal insulation on the distribution of heat and moisture in the external wall while monitoring the indoor air temperature during the heating and cooling regimes. The internal thermal insulation is more efficient in terms of room heating, while the external insulation is preferable in reducing the risk of the condensation water vapor within the wall. The efficiency of heating the room to 20°C increased by 12% when internal thermal insulation was added to the walls, compared to heating without insulation. The maximum value of relative humidity in the critical wall section decreased by 10% when external thermal insulation was added. Implementing internal thermal insulation needs the right choice of insulation material and its installation to prevent air leakage. The results suggest that the proper selection of the placement of the thermal insulation is critical for optimizing the thermal performance and reducing moisture-related problems in residential buildings. Author

Motion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body, along with signals from additional sensors, for monitoring of physical activity and analyzing heart rate dynamics during running on route segments of different slopes and speeds. This method provides an alternative to the heart monitoring on the treadmill ergometer in the cardiology laboratory. The proposed computational methodology involves digital data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The motion patterns detection involves segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This classification method compares neural networks, support vector machine, Bayesian, and k-nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface demonstrate the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems.

The study of physiological changes recorded by wearable devices during physical exercises belongs to very important research topics in neurology for the detection of motion disorders or monitoring of the fitness level during sports activities. This paper contributes to this area with studies of the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes. Experimental data sets include 296 segments of their total length of 60 hours, recorded on a home exercise bike under different motion conditions. Wearable instruments with oximetric, heart rate, accelerometric, and thermal camera sensors were used to fill the own database of signals recorded with selected sampling frequencies. The proposed methodology includes fundamental signal and image processing methods for signal analysis and machine learning tools for labeling image components and detecting facial temperature changes. Results show the minimal effect of mask wearing on blood oxygen concentration but its substantial influence on the breathing frequency and the heart rate. The use of a respirator substantially increased the respiratory rate for the given set of experiments under the load. This indicates how wearable sensors, computational intelligence, and machine learning can be used for motion monitoring and data analysis of signals recorded in different conditions.

Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k-nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments.

Motion pattern analysis uses methods for the recognition of physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents the motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. The set of real cycling experiments was recorded in a hilly area with each route about 12 km long. The associated signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data including the heart rate recovery delay studied as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The accuracy of the classification of downhill and uphill cycling based upon accelerometric data achieved 93.9 % and 95.0 % for the training and testing sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. The use of wearable sensors and the proposed methodology finds a wide range of applications in rehabilitation and the diagnostics of neurological disorders as well. Author

Infrared thermography has a wide range of applications both in engineering and biomedicine. Resulting video-images provide immediate information about thermal conditions on the surface of the observed object but for the more sophisticated analysis the detail evaluation of separate images is necessary. The processing of thermal images is based upon data acquisition by special non-invasive sensors, efficient communication systems, and the application of selected machine learning methods in many cases. The present paper is devoted to the recognition of thermal regions in the facial area, detection of the body temperature, and evaluation of breathing frequency and its possible disorders. Data include video-sequences acquired on the home exercise bike and recorded during different load conditions. The proposed general methodology combines the use of neural networks and machine learning methods for the detection of the changing temperature ranges of the thermal camera. Selected digital signal processing methods are then used to find the mean body temperature and breathing frequency during the specified time period. Results show the temperature changes and breathing frequency between 0.48 and 0.56 Hz for selected experiments and different body loads. © 2021, Springer Nature Switzerland AG.

Student se v rámci disertační práce komplexně zabýval oblastí biometrické identifikace osob s využitím kontury lidské ruky a systematické korekce vad distorze optických soustav biometrických scannerů pro účely pořizování obrazů rukou. Cíl disertační práce byl návrh nových, případně inovovaných, metod a postupů v oblasti identifikace osob s využitím kontury lidské ruky v návaznosti na technické vybavení potřebné pro snímání obrazů lidské ruky. Předmětem výzkumu bylo sestavit návrh nových metod pro odstraňování distorse optických soustav biometrických scannerů. Byly uvažovány různé metody polynomiální korekce centrické distorse s využitím pokročilých evolučních algoritmů. Další oblastí výzkumu byly nové postupy a metody pro identifikaci osob s využitím kontury lidské ruky založené na pokročilých evolučních algoritmech. Výsledky návrhu korektorů distorze vad optických soustav byly ověřeny s využitím komparativních obrazů ze zvolené optické soustavy biometrického scanneru a také na komparativních obrazech obecného charakteru. Výsledky návrhu algoritmů pro identifikaci osob s využitím kontury lidské ruky byly prakticky ověřeny na veřejně dostupné komparativní databázi obrazů kontur rukou. Všechny navržené postupy byly porovnány s výsledky dosahovanými v rámci state-of-the-art v dané vědní oblasti., The student (a problem solver) dealt in the framework of his doctoral thesis with a method of a biometric identification of the persons with use the human hand contours. The systematic correction of the centric distortion defects of the biometric scanner was also part of the thesis. Such system (biometric scanner) serves to capture the hand images. Aim of the thesis was to propose the new or innovative methods in the area of the persons identification in connection with the technical equipment which is necessary in the hand images capturing process. Topic of the research was to create the proposals of the new methods enabling to repair the images which are burdened with the distortion defect. Both barrel or pillow. Various methods which are based on so called polynomial centric correction algorithm were used. All proposed methods utilize advanced evolutionary optimizers. Next research area was presented by the new techniques and methods which enable to identify the persons with use of the hand contour. All these methods are also based on advanced evolutionary optimizers. The results of proposals of the correctors of the optical defects was verified with use of a set of comparative images which were obtained with use of the optical system of the biometric scanner. A set of images from publicly accessible database were used as well and this set served as unique source of valuable information. All proposed methods were compared with the results reached by other authors in accordance with the current state-of-the-art in the given scientific area., Fakulta elektrotechniky a informatiky, V souvislosti s opatřeními ke snížení rizika nákazy koronavirem se obhajoba disertační práce konala distančním způsobem prostřednictvím MS Teams. Všichni zúčastnění vyslovili souhlas s nahráváním průběhu obhajoby v prostředí MS Teams. Po představení doktoranda Ing. Jaroslava Moravce byla komise seznámena se stanoviskem školitele k disertační práci a osobě disertanta. Doktorand seznámil komisi se svojí disertační prací formou prezentace. Poté byly předneseny posudky oponentů a doktorand reagoval na připomínky oponentů. V následné veřejné diskusi disertant odpověděl na otázky členů komise, které jsou uvedeny na samostatných listech a v záznamu. Komise posoudila disertační práci a rozhodla, že disertační práce není plagiát. Na závěr proběhlo tajné hlasování prostřednictvím aplikace ADoodle. Protokol o výsledcích hlasování tvoří samostatnou přílohu., Dokončená práce s úspěšnou obhajobou

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈3, 8〉 and 〈8, 15〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

The paper is devoted to computer simulations of the distribution and time evolution of the temperature in a wooden house in summer. The goal of simulations was to assess the effect of covering walls inside the house with a PCM (phase change material) on its passive cooling, which prevents the undesired overheating of the house and provides the required thermal comfort for the occupants under warm summer days. Computer simulations were performed by the COMSOL Multiphysics software (COMSOL Inc., Stockholm, Sweden). A model of a house without the PCM coverage was compared with models of houses in which the PCM was located on all walls, except a floor, and on a wall opposite the window. Results of simulations proved that the wood wall thickness and PCMs location influence overheating the wooden house. Under studied conditions, the coverage of a wall opposite the window best eliminated extremes of the air temperature inside the house. The maximum temperature decrease was 3.9 degrees C (i.e., drop of 31.1%) comparing the house which wall opposite the window was covered by the PCM and the house without the PCM coverage.

The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the contribution to this topic by (1) analysis of accelerometric signals recorded by wearable sensors located at specific body positions and by (2) implementation of deep learning methods to classify signal features. This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system. The experimental dataset includes 850 observations that were recorded by a mobile device in the spine area (L3 verterbra) for cycling routes with the different slope. The proposed methodology includes the use of deep learning convolutional neural networks with five layers applied to signal values transformed into the frequency domain without specification of any signal features. The accuracy of discrimination between different motion patterns for the uphill and downhill cycling and recognition of 4 classes associated with different route slopes was 96.6% with the loss criterion of 0.275 for sigmoidal activation functions. These results were compared with those evaluated for selected sets of features estimated for each observation and classified by the support vector machine, Bayesian methods, and the two-layer neural network. The best cross-validation error of 0.361 was achieved for the two-layer neural network model with the sigmoidal and softmax transfer functions. Our methodology suggests that deep learning neural networks are efficient in the assessment of motion activities for automated data processing and have a wide range of applications, including rehabilitation, early diagnosis of neurological problems, and possible use in engineering as well. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

Augmented reality has a wide range of applications in many areas that can extend the study of real objects into the digital world, including stomatology. Real dental objects that were previously examined using their plaster casts are often replaced by their digital models or three-dimensional (3D) prints in the cyber-physical world. This paper reviews a selection of digital methods that have been applied in dentistry, including the use of intra-oral scanning technology for data acquisition and evaluation of fundamental features of dental arches. The methodology includes the use of digital filters and morphological operations for spatial objects analysis, their registration, and evaluation of changes during the treatment of specific disorders. The results include 3D models of selected dental arch objects, which allow a comparison of their shape and position during repeated observations. The proposed methods present digital alternatives to the use of plaster casts for semiautomatic evaluation of dental arch measures. This paper describes some of the advantages of 3D digital technology replacing real world elements and plaster cast dental models in many areas of classical stomatology.

Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use ofmicroelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities. © 2019 by the authors.

This paper is devoted to modelling of temperature distribution and its time evolution in rooms with specific thermal insulation and heat transfer for different external conditions. The simulation results should help to design the room architecture and wall materials to reduce energy losses due to heating or cooling, and to increase the inside thermal comfort. For this purpose, a methodological procedure using real data processing in the COMSOL Multiphysics modelling environment and spatial visualization of temperature evolution is proposed. This paper describes a mathematical model for simulation of the temperature evolution inside a space with thermally insulated walls under selected outside conditions. Computer simulations are then used to assess the temperature distribution inside the room and the heat flow through the room walls. Results of the simulations are used for subsequent determination of the time needed for the desired decrease of air temperature inside the tested room during its cooling due to the low ambient temperature, which is related to the thermal stability of the building, specific heat capacity, and thickness of the thermal insulation. Under the studied conditions, the time to reach the temperature drops by 20 percent in a room with windows was from 1.4 to 1.8 times lower than that in the room without windows. The proposed methodology shows the flexibility of computer modelling in the design of insulated building systems. The mesh density testing was performed by comparing the air temperature evolution in the model of the selected mesh density and the model with its maximum value enabled by the size of computer memory. The maximum temperature deviation calculated for the mesh of the presented model was 0.57%. © 2018 by the authors.

Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s). © 2001-2011 IEEE.

The paper presents specific methods of processing of multimodal data recorded during physical activities by depth MS Kinect cameras, thermal imaging cameras and heart rate sensors. All video data and heart rate signals used in the present study were recorded in the home environment. The proposed methodology includes the detection of the chest breathing area for breathing motion analysis used by the MS Kinect. For the thermal image processing the static and dynamic selection of regions of interests was performed in associated sets of images to find time evolution of respiratory signals and their temperature changes. Signal de-noising by finite impulse filters is applied both for breathing and heart rate data. Correlation analysis is used in the data processing stage to find the time relation between individual physiological variables. Results include relations between signals acquired during physical activities and they show how simple sensors can be used to increase the accuracy of standard diagnostical tools in biomedicine as well. © 2017 IEEE.

This paper addresses the use of multichannel signal processing methods in analysis of heart rate changes during cycling using the global positioning system (GPS) to record the route conditions. The main objectives of this work are in monitoring of physiological activities, cycling features extraction, their classification and visualization. Real data were acquired from 41 cycling rides of the same 11.48-km long route divided into 2460 segments of approximately 60 s. The data were recorded with a varying sampling period within the range of 1–22 s depending on the route profile. The pre-processing stage included preparatory analysis, filtering and resampling of the data to a constant sampling rate. The proposed algorithm includes the evaluation of the cross-correlation between the heart rate and the altitude gradient as recorded by a GPS satellite system. A Bayesian approach was then applied to classify the cycling segment features into two classes (specifying cycling up and down) with the classification accuracy better than 93 %. A comparison with other classification methods is presented in the paper as well. The results include the following relationships: (1) the heart rate and altitude gradient, which shared a positive correlation coefficient of 0.62; (2) the heart rate and speed, which shared a negative correlation coefficient of −0.72 over all of the analysed segments; and (3) the mean heart rate change delay (6.8–11.5 s) in relation to the changes in the altitude gradients associated with cycling up and down. The paper forms a contribution to the use of computational intelligence and visualization for data processing both in cycling and fitness physical activities as well. © 2016, Springer-Verlag London.

General methods of video processing and three dimensional modelling have a wide range of applications in engineering, archaeology and spacial objects study. The paper is devoted to applications of these methods in biomedicine and neurology using MS Kinect depth sensor for non-contact monitoring of breathing. A special attention is paid to visualization of results and motion mapping over the selected chest area. The proposed methodology applies digital signal processing methods and functional transforms for acquired data de-noising, spectral analysis, and feature selection. Suggested method uses further the local polynomial approxima-tion to detect extremal values of spectral components. The results verify the correspondence between the evaluations of the breathing frequency obtained from the thorax movement recorded by the depth sensor. The study proves that simple depth sensors can be used for non-contact detection of breathing frequency and for the three dimensional modelling of the chest movement. The proposed non-contact method enables to analyse breathing for diagnostic purposes and monitoring in the home environment as a component of assisted living technologies. General methodology studied form a contribu-tion to the use of video sequences or sets of images for spacial objects modelling, their recognition, possible three dimen-sional printing or analysis of time evolution of their features. © 2016 IEEE.

The paper deals with fusion of physiological and GPS data acquired during cycling and their analysis using general methods of multichannel signal processing. Experimental data were acquired during 17 identical cycling routes each about 12 km long including more then 1100 segments of the length 60 s recorded with the varying sampling period. The proposed algorithm includes their initial analysis, de-nosing using selected digital filters, interpolation and resampling in the first stage followed by evaluation of the cross-correlation between the heart rate and the altitude gradient of positioning data recorded by the GPS system. Results obtained present (i) relation between the heart rate and the slope with the positive regression coefficient 6.04 and (ii) the heart rate and speed with the negative regression coefficient-1.67 over all segments analyzed. © 2015 IEEE.