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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., Ministry of Health of the Czech Republic [FN HK 00179906]; Charles University in Prague, Czech Republic [PROGRES Q40]; project PERSONMED - European Regional Development Fund (ERDF) [CZ.02.1.010.00.017_0480007441]; governmental budget of the Czech Republic

This paper presents improvements in image gap restoration through the incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed: 1) the local edges or textures, inferred from the gradients of the neighboring pixels and 2) the global edges between image objects or segments, inferred using a Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer, an edge skeleton image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of upsampling), up-sampled, and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves peak-signal-to-noise-ratio by 1–5 dB compared with a range of best-published works.

This paper presents the contactless measuring of breathing using the MS Kinect depth sensor and compares the results obtained with records of breathing taken by polysomnography (PSG). We explore the methods of signal denoising, resampling, and spectral analysis of acquired data as well as feature extraction and their Bayesian classification. The proposed methodology was applied for analysis of the long-term monitoring of individuals who were observed simultaneously by PSG and MS Kinect in the sleep laboratory. After time synchronization of polysomnographic and MS Kinect video data, features were extracted from both signals and compared. The average error of the frequency while being evaluated by MS Kinect that was related to that obtained by PSG was 3.75 %. The mean accuracy of the Bayesian classification of features into two classes (i.e. wake or sleep) was 88.90 and 88.95 % for the PSG and MS Kinect measurements, respectively. The strong likeness of features supports the hypothesis that contactless techniques may represent a valid alternative to the present approach of sleep monitoring, thereby allowing data acquisition in the home environment as well.

This paper presents a novel method of Bayesian gait recognition using Microsoft (MS) Kinect image and depth sensors and skeleton tracking in three-dimensional space. Although video sequences acquired by a complex camera system enable a very precise data analysis, it is possible to use much simpler technical devices to analyze video frames with sufficient accuracy for many applications. The use of the MS Kinect allows a simple 3-D modeling using its image and depth sensors for data acquisition, resulting in a matrix of 640 × 480 elements used for spatial modeling of a moving body. The experimental part of the paper is devoted to the study of three data sets: (i) 18 individuals with Parkinson's disease, (ii) 18 healthy age-matched controls, and (iii) 15 trained young individuals forming the second reference set. The proposed algorithm involves methods for the estimation of the average stride length and gait speed of individuals in these sets. Digital signal processing methods and Bayesian probability classification algorithms are then used for gait feature analysis to recognize individuals suspected of having Parkinson's disease. The results include the estimation of the characteristics of selected gait features for patients with Parkinson's disease and for individuals from the reference sets, presentation of decision boundaries, and comparison of classification efficiency for different features. The achieved accuracy of the probabilistic classification was 94.1%.

This paper presents a novel method of gait recognition that uses the image and depth sensors of the Microsoft (MS) Kinect to track the skeleton of a moving body and allows for simple human---machine interaction. While video sequences acquired by complex camera systems enable very precise data analyses and motion detection, much simpler technical devices can be used to analyze video frames with sufficient accuracy in many cases. The experimental part of this paper is devoted to gait data acquisition from 18 individuals with Parkinson's disease and 18 healthy age-matched controls via the proposed MS Kinect graphical user interface. The methods designed for video frame data processing include the selection of gait segments and data filtering for the estimation of chosen gait characteristics. The proposed computational algorithms for the processing of the matrices acquired by the image and depth sensors were then used for spatial modeling of the moving bodies and the estimation of selected gait features. Normalized mean stride lengths were evaluated for the individuals with Parkinson's disease and those in the control group and were determined to be 0.38 and 0.53 m, respectively. These mean stride lengths were then used as features for classification. The achieved accuracy was >90 %, which suggests the potential of the use of the image and depth sensors of the MS Kinect for these applications. Further potential increases in classification accuracy via additional biosensors and body features are also discussed.

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.

This paper presents the analysis of dental cast images and new mathematical methods for their evaluation. While depth sensors of sophisticated scanners allow precise spatial data processing, it is possible to use a multi-camera system for direct data acquisition and recognition of selected objects as well. The main goal of this paper is the analysis of three-dimensional objects to enable numerical evaluation of measurements important for proposals of the appropriate treatment after dental operations. Methods presented include (1) computer analysis of a single de-noised and thresholded image used for detection of its components and (2) presentation of tools for three-dimensional modeling using a double camera system. The proposed algorithms allow semi-automatic evaluation of measurements between selected objects. The resulting general algorithm can be used both in biomedical applications and engineering to detect measurements in static image frames or videosequences.

This paper presents new methods of orthodontic body segmentation using digital records of their plaster cast models under different types of illumination. Selected light conditions are used for the data acquisition to provide more clearly defined contours of the image components. The preliminary stage of the data processing uses the circular Hough transform, digital de-noising, and a separation of the orthodontic objects from their backgrounds employing Otsu’s thresholding method. The region-growing method using multiple seed points in a convex hull is then applied. The proposed general method identifies the common boundary of two neighboring and overlapping orthodontic objects with results enabling the efficient segmentation of digital data and their analysis through the computer network.

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 approxima­tion 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 contribu­tion to the use of video sequences or sets of images for spacial objects modelling, their recognition, possible three dimen­sional printing or analysis of time evolution of their features.

This experimental study investigated possibility of face symmetry and symmetric face movements evaluation using MS Kinects HD face tracking. The main motivation for this research is facial paralysis or partial loss of muscle control caused by stroke. Evaluation of face symmetry can be used as an indicator of positive improvement of such disability. Precision of face model acquisition of MS Kinect v2 was estimated and two data sets of four facial exercises were recorded. Differences between data sets are evaluated with comparison of left part of face to the right one, with comparison of base frame with face at rest to frames during exercises, and with changes of angles of symmetric points on face. Results show significant differences between both sets, even though face tracking is affected by lightning, distance from camera and position angle of recorded person in view of sensor. Best results are achieved when comparing changes of angles (up to 6°) and differences of in symmetry of face (56% total sym­metry in normal set to 13% symmetry in simulated set).

Measuring of breathing with contact methods, like respiratory belts, is very uncomfortable for patients and in case of complex sleep analysis, cables from different sensors can substantially affect the quality of the sleep. This paper presents the contactless measuring of breathing using the MS Kinect depth sensor, and it compares the results obtained with records of breathing observed by the flowmetry. The methodological part of the paper is devoted to spectral analysis of data acquired, feature extraction, and their Bayesian classification. The proposed classifier is able to distinguish the Sleep and Wake classes with the accuracy of 100% (cross-validation: 0) for given data. The achived accuracy of classification into 3 classes (Sleep, Falling Asleep and Wake) is 97% (cross-validation: 0.0248) in the given case.

The paper presents a new method for separation of overlapping dental objects allowing more precise analysis of the dental arch using image processing methods and computational intelligence. The methodological part of the paper is devoted to the separation of dental objects using the watershed and region growing methods applied to denoised images of dental bodies. The main part of the paper is devoted to analysis of image components and to the use of normal vectors to image segments boundaries to detect separate dental arch components in the complex environment of overlapping bodies. Experimental part of the paper is devoted to the application of the proposed method to the processing of real images of dental plaster casts acquired with the different illumination. Results include the proposed general algorithm for image components separation allowing further evaluation of dental arch parameters during the operation to allow the efficient treatment in the clinical environment.

Non-contact methods for the tracking of breathing have found noticeable interest in research in recent years motivated by the obtrusiveness of the traditional approach to sleep disordered breathing diagnosis. The low-priced Kinect device released by Microsoft has emerged as a possible alternative hardware in the field of subject's monitoring aimed at sleep disorders analysis. In this paper we present a method for the reconstruction of the patient's breathing during sleep using the depth maps acquired by Kinect. Preliminary operations of resampling and denoising were performed on the images. A reconstruction of the breathing is then obtained by means of image processing and filtering operations; it is synchronized with the corresponding polysomnographic record, features are extracted from both signals and compared. The strong likeness in the mean of the features extracted from the two records (with mean error of 0.87% in frequency and 9.17% in regularity) supports the view that enhancements of this technique may represent a valid alternative to the present approach to sleep monitoring.

The paper presents in its initial part historical notes to the development of digital signal and image processing methods. The following case studies are devoted to their application in biomedicine and they include the use of computational intelligence in EEG and EMG signal processing, image segmentation and registration in orthodontia, the human-machine interaction and the three-dimensional modelling using MS Kinect in diagnostics and treatment of human motion disorders and neurological diseases. Associated comments include remarks and references to the development of modern computational tools, biosensors, wireless communication and data fusion used in assistive technologies and robotic systems. Mathematical tools common to all these applications form the next part of the paper that includes notes to spectral analysis, functional transforms, digital filtering, image enhancement, classification of multi-dimensional signal components and optimization using neural networks. Final remarks emphasize the interdisciplinary significance of the digital signal processing forming the integrating basis of many diverse research areas.

The paper presents in its initial part fundamental mathematical tools for analysis of retinal images using methods of digital signal processing and enhancement and methods for their registration to monitor changes during the treatment. Bloodvessel trees are then extracted to analyse their specific structures to find retina disorders. The proposed graphical user interface is then used for analysis and monitoring of the set of 20 patients observed during the treatment using the optical coherence tomography. Features obtained are then analysed using selected statistical methods to compare features obtained and to propose the method for classification of healthy and diseased patients. The accuracy achieved was 79 %.

Movement disorders, problems with motion and gait stability related to aging form a very intensively studied research area. The paper presents a contribution to these topics through the use of data acquired by motion sensors and namely image and depth sensors of the MS Kinect. While video sequences obtained by complex camera systems can be used for the precise gait features evaluation, it is possible to use much cheaper devices for diagnostic purposes accurate enough in many cases. The experimental part of the study presents video sequences and depth sensors data acquisition for 18 individuals with the Parkinson's disease and 18 healthy age-matched controls using the proposed graphical user interface in the clinical environment. Results presented include the estimation of gait features to distinguish gait disorders and to classify individuals in the early stage of possible serious diseases. The accuracy achieved was higher then 90 % for given sets of individuals.

A new tool has been developed for comparison and evaluation of medical images based on image registration and edge detection algorithms. The tool performance has been demonstrated on the processing of the ortopantomograms taken before and after treatment. The accuracy of the performance has been discussed and the possible origins of tool performance errors have been explained. The results obtained using presented tool have been discussed by an expert. The expert has confirmed that the tool is well suited for comparison and evaluation of ortopantomograms. The expert has come to the conclusion that our tool can be useful in other specializations of dentistry, not only in orthodontics.

Image denoising represents a crucial initial step in biomedical image processing and analysis. Denoising belongs to the family of image enhancement methods (Bovik, 2009) which comprise also blur reduction, resolution enhancement, artefacts suppression, and edge enhancement. The motivation for enhancing the biomedical image quality is twofold. First, improving the visual quality may yield more accurate medical diagnostics, and second, analytical methods, such as segmentation and content recognition, require image preprocessing on the input. Gradually, noise reduction methods developed in other research fields find their usage in biomedical applications. However, biomedical images, such as images obtained from computed tomography (CT) scanners, are quite specific. Modelling noise based on the first principles of image acquisition and transmission is a too complex task (Borsdorf et al., 2009), and moreover, the noise component characteristics depend on the measurement conditions (Bovik, 2009). Additionally, noise reduction must be carried out with extreme care to avoid suppression of the important image content. For this reason, the results of biomedical image denoising should be consulted with medical experts.

Presented paper is devoted to application of selected thresholding algorithms and it compares obtained results. Studied methods were verified on a set of microscopic images of aluminum alloy. The goal of this project is satisfying image segmentation as a part of a preprocessing algorithm. This procedure is necessary for the following images processing based on application of Hough Transform.

Problems of multi-dimensional signal enhancement, segmentation, feature extraction and components classification is essential in many engineering and biomedical applications. The paper is devoted to the use of watershed transform and wavelet transform for MR image components detection and discussion of over-segmentation problems. The goal of the paper is in (i) analysis of image de-noising, (ii) discussion of image enhancement, and (iii) multi-resolutional approach application for reduction of over-segmentation problems. Proposed algorithms include the use of wavelet transform and gradient methods in the preprocessing stage and application of the watershed transform for enhanced images. Resulting algorithms are verified for simulated images and applied for a selected MR biomedical images containing different structures.

Signal analysis of multi-channel data form a specific area of general digital signal processing methods. The paper is devoted to application of these methods for electroencephalogram (EEG) signal processing including signal de-noising, evaluation of its principal components and segmentation based upon feature detection both by the discrete wavelet transform (DWT) and discrete Fourier transform (DFT). The self-organizing neural networks are then used for pattern vectors classification using a specific statistical criterion proposed to evaluate distances of individual feature vector values from corresponding cluster centers. Results achieved are compared for different data sets and selected mathematical methods to detect and to classify signal segments features. Proposed methods are accompanied by the appropriate graphical user interface (GUI) designed in the MATLAB environment.

Presented paper deals with processing of electron-microscope images of Picea Abies stomas. A stoma character strongly depends on the level of air pollution in the area where the tree grows - as the stoma epidermis covers with epiticular waxes to protect itself against the negative environmental influences. According to the level of incrustation it is possible to distinguish five classes of stoma structure. An automatic algorithm recognizing a degree of stoma damage, based on the microscopic image processing can be useful during the process of environmental assessment. This work is devoted to a solution of the problem of stoma evaluation by means of texture classification. There are two principles discussed in the paper: The first principle is based on gradient methods while the second one uses a wavelet transform. Possibilities of application of mentioned attitudes were investigated and the classification criteria distinguishing the stoma character were suggested, as well. The resulting algorithm was verified on a library of four hundred real images and achieved results were compared with an expert's sensual classification. Selected methods of image preprocessing as noise reduction, brightness correction and resampling were used as well.

Segmentation, feature extraction and classification of signal components belong to very common problems in various engineering, economical and biomedical applications. The paper is devoted to the use of discrete wavelet transform (DWT) both for signal preprocessing and signal segments feature extraction as an alternative to the commonly used discrete Fourier transform (DFT). Feature vectors belonging to separate signal segments are then classified by a competitive neural network as one of methods of cluster analysis and processing. The paper provides a comparison of classification results using different methods of feature extraction most appropriate for EEG signal components detection. Problems of multichannel segmentation are mentioned in this connection as well.

Image segmentation, feature extraction and image components classification form a fundamental problem in many applications of multi-dimensional signal processing. The paper is devoted to the use of watershed transform for image segmentation in connection with wavelet transform allowing image de-noising and image components feature extraction. Proposed methods are applied for biomedical image analysis and processing. The study of MR image segmentation devoted to the detection of its specific components results in the proposal of the appropriate image preprocessing to reduce problems of its oversegmentation. Resulting algorithms include the use of wavelet transform and gradient methods in the preprocessing stage. Proposed algorithms are verified for simulated images and applied for a selected MR biomedical images containing different structures.

Image de-noising and enhancement form two fundamental problems in many engineering and biomedical applications. The paper is devoted to the study of the multi-resolution approach to this topic employing the Haar wavelet transform and its application to processing of volumetric magnetic resonance image sets corrupted with additional noise. The resulting coefficients are thresholded and exploited for subsequent reconstruction. The Haar transform is evaluated using both the two-dimensional approach applied individually to each image layer, and the three-dimensional technique performed on the image volume as a whole. In noise reduction, the latter approach profits from similarities between the neighbouring image layers and shows a considerable improvement over the former method. Results are presented in numerical and graphical forms using three-dimensional visualization tools.

The contribution presents the process of the space localization of the moving object using a multicamera system, possible remote data transmission through communication links and image acquisition problems. The moving body recognition and its mathematical modelling including spline interpolation of its trajectory in the three-dimensional space form the main part of the paper. Final results are presented in the computer environment of virtual reality allowing the detail study of the whole system that can represent an engineering or a biomedical process.

Texture segmentation and classification form a very important topic of the interdisciplinarg area of signal processing with many applications in diflerent areas including satellite image processing, biomedical image analysis and microscopic image processing. The paper presents selected mathematical methods used for image segmentation and the following segments classification using multiresolution decomposition of segments boundary szgnals. The wavelet transform has been applied here for feature extraction and image de-noising. Results of feature extraction obtained by the discrete wavelet transform are compared with that evaluated by the discrete Fourier transform. For the following feature classification the self-organizing neural networks are applied. Proposed methods have been verified for simulated structures and then used for analysis of microscopic images of crystals of diflerent shapes and sizes.

Image de-noising and restoration represent basic problems in image processing with many different applications including engineering, reconstruction of missing data during their transmission and enhancement of biomedicai structures as well. This problem occurs also in filling-in blocks of missing or corrupted data. The paper presents the use of Wavelet transform in this area including its application for image decomposition and rejection of its components at first. The main part of the paper is devoted (i) to algorithms of restoration of missing image blocks by the search of similar structures of a given image in the Wavelet domain space and (ii) to comparison of this approach with iterated Wavelet interpolation and predictive image modelling. Proposed methods are verified for simulated data and then applied for processing of magnetic resonance images.