depth sensors Dotaz Zobrazit nápovědu
This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.
- MeSH
- dechová frekvence fyziologie MeSH
- dospělí MeSH
- dýchání MeSH
- lidé středního věku MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- polysomnografie metody MeSH
- senzitivita a specificita MeSH
- spánek fyziologie MeSH
- syndromy spánkové apnoe patofyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.
- MeSH
- audiovizuální záznam MeSH
- časové faktory MeSH
- dýchání * MeSH
- lidé MeSH
- monitorování fyziologických funkcí přístrojové vybavení MeSH
- pohyb MeSH
- srdeční frekvence fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The paper is devoted to the study of facial region temperature changes using a simple thermal imaging camera and to the comparison of their time evolution with the pectoral area motion recorded by the MS Kinect depth sensor. The goal of this research is to propose the use of video records as alternative diagnostics of breathing disorders allowing their analysis in the home environment as well. The methods proposed include (i) specific image processing algorithms for detecting facial parts with periodic temperature changes; (ii) computational intelligence tools for analysing the associated videosequences; and (iii) digital filters and spectral estimation tools for processing the depth matrices. Machine learning applied to thermal imaging camera calibration allowed the recognition of its digital information with an accuracy close to 100% for the classification of individual temperature values. The proposed detection of breathing features was used for monitoring of physical activities by the home exercise bike. The results include a decrease of breathing temperature and its frequency after a load, with mean values -0.16 °C/min and -0.72 bpm respectively, for the given set of experiments. The proposed methods verify that thermal and depth cameras can be used as additional tools for multimodal detection of breathing patterns.
- MeSH
- algoritmy MeSH
- dýchání * MeSH
- počítačové zpracování obrazu MeSH
- pohyb těles MeSH
- umělá inteligence MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVES: The aim of this study is to investigate depth dependent changes of polymerization process and kinetics of visible light-curing (VLC) dental composites in real-time. The measured quantity - "ion viscosity" determined by dielectric analysis (DEA) - provides the depth dependent reaction rate which is correlated to the light intensity available in the corresponding depths derived from light transmission measurements. METHODS: The ion viscosity curves of two composites (VOCO Arabesk Top and Grandio) were determined during irradiation of 40s with a light-curing unit (LCU) in specimen depths of 0.5/0.75/1.0/1.25/1.5/1.75 and 2.0mm using a dielectric cure analyzer (NETZSCH DEA 231 with Mini IDEX sensors). The thickness dependent light transmission was measured by irradiation composite specimens of various thicknesses on top of a radiometer setup. RESULTS: The shape of the ion viscosity curves depends strongly on the specimen thickness above the sensor. All curves exhibit a range of linear time dependency of the ion viscosity after a certain initiation time. The determined initiation times, the slopes of the linear part of the curves, and the ion viscosities at the end of the irradiation differ significantly with depth within the specimen. The slopes of the ion viscosity curves as well as the light intensity values decrease with depth and fit to the Lambert-Beer law. The corresponding attenuation coefficients are determined for Arabesk Top OA2 to 1.39mm(-1) and 1.48mm(-1), respectively, and for Grandio OA2 with 1.17 and 1.39mm(-1), respectively. For thicknesses exceeding 1.5mm a change in polymerization behavior is observed as the ion viscosity increases subsequent to the linear range indicating some kind of reaction acceleration. SIGNIFICANCE: The two VLC composites and different specimen thicknesses discriminate significantly in their ion viscosity evolution allowing for a precise characterization of the curing process even with respect to the polymerization mechanism.
In this study, a highly sensitive, fast, and selective enzyme-free electrochemical sensor based on the deposition of Ni cavities on conductive glass was proposed for insulin detection. Considering the growing prevalence of diabetes mellitus, an electrochemical sensor for the determination of insulin was proposed for the effective diagnosis of the disease. Colloidal lithography enabled deposition of nanostructured layer (substrate) with homogeneous distribution of Ni cavities on the electrode surface with a large active surface area. The morphology and structure of conductive indium tin oxide glass modified with Ni cavities (Ni-c-ITO) were characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). The diameter of the resulting cavities was approximately 500 nm, while their depth was calculated at 190 ± 4 nm and 188 ± 18 nm using AFM and SEM, respectively. The insulin assay performance was evaluated by cyclic voltammetry. Ni-c-ITO exhibited excellent analytical characteristics, including high sensitivity (1.032 μA μmol-1 dm3), a low detection limit (156 μmol dm-3), and a wide dynamic range (500 nmol dm-3 to 10 μmol dm-3). Finally, the determination of insulin in buffer with interferents and in real blood serum samples revealed high specificity and demonstrated the practical potential of the method.
The importance of cellular metabolic adaptation in inducing robust T cell responses is well established. However, the mechanism by which T cells link information regarding nutrient supply to clonal expansion and effector function is still enigmatic. Herein, we report that the metabolic sensor adenosine monophosphate-activated protein kinase (AMPK) is a critical link between cellular energy demand and translational activity and, thus, orchestrates optimal expansion of T cells in vivo. AMPK deficiency did not affect T cell fate decision, activation, or T effector cell generation; however, the magnitude of T cell responses in murine in vivo models of T cell activation was markedly reduced. This impairment was global, as all T helper cell subsets were similarly sensitive to loss of AMPK which resulted in reduced T cell accumulation in peripheral organs and reduced disease severity in pathophysiologically as diverse models as T cell transfer colitis and allergic airway inflammation. T cell receptor repertoire analysis confirmed similar clonotype frequencies in different lymphoid organs, thereby supporting the concept of a quantitative impairment in clonal expansion rather than a skewed qualitative immune response. In line with these findings, in-depth metabolic analysis revealed a decrease in T cell oxidative metabolism, and gene set enrichment analysis indicated a major reduction in ribosomal biogenesis and mRNA translation in AMPK-deficient T cells. We, thus, provide evidence that through its interference with these delicate processes, AMPK orchestrates the quantitative, but not the qualitative, manifestation of primary T cell responses in vivo.
- MeSH
- adenylátkinasa genetika metabolismus MeSH
- aktivace lymfocytů MeSH
- buňky Th17 fyziologie MeSH
- CD4-pozitivní T-lymfocyty MeSH
- DNA vazebné proteiny genetika metabolismus MeSH
- fyziologická adaptace MeSH
- kolitida imunologie MeSH
- messenger RNA genetika metabolismus MeSH
- myši knockoutované MeSH
- myši MeSH
- převzatá imunita MeSH
- regulace genové exprese enzymů MeSH
- regulační T-lymfocyty fyziologie MeSH
- T-lymfocyty pomocné-indukující fyziologie MeSH
- Th1 buňky fyziologie MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson's disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space. METHODS: The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson's disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data. RESULTS: The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson's disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications. CONCLUSIONS: Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.
- MeSH
- algoritmy MeSH
- chůze (způsob) * MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- nervová síť MeSH
- Parkinsonova nemoc patofyziologie MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- studie případů a kontrol MeSH
- zobrazování trojrozměrné metody MeSH
- zrychlení MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Teoretický článek pojednává o možnostech využití multimediální techniky ve vzdĕlávání, konkrétnĕ ve školní tĕlesné výchovĕ. Autoři analyzují roli technologií v motivaci k učení a k pohybu u školní mládeže a zabývají se inovativními metodami ve výuce tĕlesné výchovy (active gaming, blended learning, flip teaching, digital game based learning). V závĕru je objasnĕna role metodického portálu pro učitele, nabízejícího využití výukových videí, mobilních aplikací či hloubkových senzorů.
The theoretical article deals with the opportunity for using of multimedia techniques in the field of education, specifically in school physical education. The authors analyse the role of technology in motivation for learning and moving of Youth in school age and they concern with innovative methods in sport education (active gaming, blended learning, flip teaching, digital game based learning). The role of the methodical portal for teachers is clarified at the end, which offers applications of educational video, mobile applications or depth sensors.
- Klíčová slova
- multimediální technika, metodický portál,
- MeSH
- dítě MeSH
- experimentální hry MeSH
- kurikulum MeSH
- lidé MeSH
- mladiství MeSH
- motivace MeSH
- počítačové komunikační sítě MeSH
- tělesná výchova * metody organizace a řízení trendy MeSH
- učební pomůcky * MeSH
- uživatelské rozhraní počítače MeSH
- výchova a vzdělávání MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mladiství MeSH
Cell volume and its regulation are key factors for cellular integrity and also serve as indicators of various cell pathologies. SPR sensors represent an efficient tool for real-time and label-free observations of changes in cell volume and shape. Here, we extend this concept by employing the use of long-range surface plasmons (LRSP). Due to the enhanced penetration depth of LRSP (~1μm, compared to ~0.4μm of a conventional surface plasmon), the observation of refractive index changes occurring deeper inside the cells is possible. In this work, the responses of a confluent normal rat kidney (NRK) epithelial cell layer to osmotic stress are studied by both conventional and long-range surface plasmons. Experiments are conducted in parallel using cell layers grown and stimulated under the same conditions to enable direct comparison of the results and discrimination of the osmotic stress-induced effects in different parts of the cell.
- MeSH
- analýza selhání vybavení MeSH
- barvení a značení MeSH
- biosenzitivní techniky přístrojové vybavení MeSH
- buněčné linie MeSH
- design vybavení MeSH
- krysa rodu rattus MeSH
- ledviny cytologie fyziologie MeSH
- osmotický tlak MeSH
- počítačové systémy MeSH
- povrchová plasmonová rezonance přístrojové vybavení MeSH
- reprodukovatelnost výsledků MeSH
- senzitivita a specificita MeSH
- velikost buňky MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: Turning in place is a challenging motor task and is used as a brief assessment test of lower limb function and dynamic balance. This review aims to examine how research of instrumented analysis of turning in place is implemented. In addition to reporting the studied population, we covered acquisition systems, turn detection methods, quantitative parameters, and how these parameters are computed. METHODS: Following the development of a rigorous search strategy, the Web of Science and Scopus were systematically searched for studies involving the use of turning-in-place. From the selected articles, the study population, types of instruments used, turn detection method, and how the turning-in-place characteristics were calculated. RESULTS: Twenty-one papers met the inclusion criteria. The subject groups involved in the reviewed studies included young, middle-aged, and older adults, stroke, multiple sclerosis and Parkinson's disease patients. Inertial measurement units (16 studies) and motion camera systems (5 studies) were employed for gathering measurement data, force platforms were rarely used (2 studies). Two studies used commercial software for turn detection, six studies referenced previously published algorithms, two studies developed a custom detector, and eight studies did not provide any details about the turn detection method. The most frequently used parameters were mean angular velocity (14 cases, 7 studies), turn duration (13 cases, 13 studies), peak angular velocity (8 cases, 8 studies), jerkiness (6 cases, 5 studies) and freezing-of-gait ratios (5 cases, 5 studies). Angular velocities were derived from sensors placed on the lower back (7 cases, 4 studies), trunk (4 cases, 2 studies), and shank (2 cases, 1 study). The rest (9 cases, 8 studies) did not report sensor placement. Calculation of the freezing-of-gait ratio was based on the acceleration of the lower limbs in all cases. Jerkiness computation employed acceleration in the medio-lateral (4 cases) and antero-posterior (1 case) direction. One study did not reported any details about jerkiness computation. CONCLUSION: This review identified the capabilities of turning-in-place assessment in identifying movement differences between the various subject groups. The results, based on data acquired by inertial measurement units across studies, are comparable. A more in-depth analysis of tests developed for gait, which has been adopted in turning-in-place, is needed to examine their validity and accuracy.
