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.
- Klíčová slova
- breathing analysis, computational intelligence, depth sensors, human-machine interaction, image processing, signal processing,
- 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.
- Klíčová slova
- MS Kinect data acquisition, big data processing, breathing analysis, computational intelligence, human–machine interaction, image and depth sensors, neurological disorders, visualization,
- 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.
This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73⁻1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.
- Klíčová slova
- UAV, canopy closure, disturbance, forest, leaf area index, snow depth,
- 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.
- Klíčová slova
- Composite resin, Curing depth, Dental material, Dielectric analysis, Light attenuation, Real-time measurement, Restorative composite, Visible light-curing,
- MeSH
- složené pryskyřice * MeSH
- stomatologické polymerizační lampy * MeSH
- vazba zubní * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- složené pryskyřice * MeSH
We report on a highly sensitive measurement of the relative humidity (RH) of moist air using both the surface plasmon resonance (SPR) and Bloch surface wave resonance (BSWR). Both resonances are resolved in the Kretschmann configuration when the wavelength interrogation method is utilized. The SPR is revealed for a multilayer plasmonic structure of SF10/Cr/Au, while the BSWR is resolved for a multilayer dielectric structure (MDS) comprising four bilayers of TiO2/SiO2 with a rough termination layer of TiO2. The SPR effect is manifested by a dip in the reflectance of a p-polarized wave, and a shift of the dip with the change in the RH, or equivalently with the change in the refractive index of moist air is revealed, giving a sensitivity in a range of 0.042-0.072 nm/%RH. The BSWR effect is manifested by a dip in the reflectance of the spectral interference of s- and p-polarized waves, which represents an effective approach in resolving the resonance with maximum depth. For the MDS under study, the BSWRs were resolved within two band gaps, and for moist air we obtained sensitivities of 0.021-0.038 nm/%RH and 0.046-0.065 nm/%RH, respectively. We also revealed that the SPR based RH measurement is with the figure of merit (FOM) up to 4.7 × 10-4 %RH-1, while BSWR based measurements have FOMs as high as 3.0 × 10-3 %RH-1 and 1.1 × 10-3 %RH-1, respectively. The obtained spectral interferometry based results demonstrate that the BSWR based sensor employing the available MDS has a similar sensitivity as the SPR based sensor, but outperforms it in the FOM. BSW based sensors employing dielectrics thus represent an effective alternative with a number of advantages, including better mechanical and chemical stability than metal films used in SPR sensing.
This work presents a novel transformer-based method for hand pose estimation-DePOTR. We test the DePOTR method on four benchmark datasets, where DePOTR outperforms other transformer-based methods while achieving results on par with other state-of-the-art methods. To further demonstrate the strength of DePOTR, we propose a novel multi-stage approach from full-scene depth image-MuTr. MuTr removes the necessity of having two different models in the hand pose estimation pipeline-one for hand localization and one for pose estimation-while maintaining promising results. To the best of our knowledge, this is the first successful attempt to use the same model architecture in standard and simultaneously in full-scene image setup while achieving competitive results in both of them. On the NYU dataset, DePOTR and MuTr reach precision equal to 7.85 mm and 8.71 mm, respectively.
- Klíčová slova
- hand pose estimation, multi-stage, neural network, transformer,
- MeSH
- benchmarking MeSH
- horní končetina * MeSH
- ruka * diagnostické zobrazování MeSH
- zdroje elektrické energie MeSH
- znalosti MeSH
- Publikační typ
- časopisecké články MeSH
In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies on simulations and demands high-quality real-world traffic data. To address this need, we monitored and analyzed LoRaWAN traffic in four European cities, making the obtained data and post-processing scripts publicly available. For monitoring purposes, we developed an open-source sniffer capable of capturing all LoRaWAN communication within the EU868 band. Our analysis discovered significant issues in current LoRaWAN deployments, including violations of fundamental security principles, such as the use of default and exposed encryption keys, potential breaches of spectrum regulations including duty cycle violations, SyncWord issues, and misaligned Class-B beacons. This misalignment can render Class-B unusable, as the beacons cannot be validated. Furthermore, we enhanced Wireshark's LoRaWAN protocol dissector to accurately decode recorded traffic. Additionally, we proposed the passive reception of Class-B beacons as an alternative timebase source for devices operating within LoRaWAN coverage under the assumption that the issue of misaligned beacons can be addressed or mitigated in the future. The identified issues and the published dataset can serve as valuable resources for researchers simulating real-world traffic and for the LoRaWAN Alliance to enhance the standard to facilitate more reliable Class-B communication.
- Klíčová slova
- Class-B, IoT, LoRa, LoRaWAN, dataset, network sniffer, time synchronization, traffic monitoring,
- Publikační typ
- časopisecké články MeSH
The most promising and utilized chemical sensing materials, WO3 and SnO2 were characterized by means advanced synchrotron based XPS, UPS, NAP-XPS techniques. The complementary electrical resistance and sensor testing experiments were also completed. A comparison and evaluation of some of the prominent and newly employed spectroscopic characterization techniques for chemical sensors were provided. The chemical nature and oxidation state of the WO3 and SnO2 thin films were explored at different depths from imminent surface to a maximum of 1.5 nm depth from the surface with non-destructive depth profiling. The adsorption and amount of chemisorbed oxygen species were precisely analyzed and quantified as a function of temperature between 25-400 °C under realistic operating conditions for chemical sensors employing 1-5 mbar pressures of oxygen (O2) and carbon monoxide (CO). The effect of realistic CO and O2 gas pressures on adsorbed water (H2O), OH- groups and chemisorbed oxygen species ( O 2 ( a d s ) - , O ( a d s ) , - O 2 ( a d s ) 2 - ) and chemical stability of metal oxide surfaces were evaluated and quantified.
- Klíčová slova
- NAP-XPS, XPS, characterization techniques, gas sensors, metal oxides, spectroscopy, synchrotron,
- Publikační typ
- časopisecké články MeSH
Knowledge of sap flow variability in tree trunks is important for up-scaling transpiration from the measuring point to the whole-tree and stand levels. Natural variability in sap flow, both radial and circumferential, was studied in the trunks and branches of mature olive trees (Olea europea L., cv Coratina) by the heat field deformation method using multi-point sensors. Sapwood depth ranged from 22 to 55 mm with greater variability in trunks than in branches. Two asymmetric types of sap flow radial patterns were observed: Type 1, rising to a maximum near the mid-point of the sapwood; and Type 2, falling continuously from a maximum just below cambium to zero at the inner boundary of the sapwood. The Type 1 pattern was recorded more often in branches and smaller trees. Both types of sap flow radial patterns were observed in trunks of the sample trees. Sap flow radial patterns were rather stable during the day, but varied with soil water changes. A decrease in sap flow in the outermost xylem was related to water depletion in the topsoil. We hypothesized that the variations in sap flow radial pattern in a tree trunk reflects a vertical distribution of water uptake that varies with water availability in different soil layers.
- MeSH
- cirkadiánní rytmus MeSH
- Olea metabolismus MeSH
- stonky rostlin anatomie a histologie metabolismus MeSH
- voda metabolismus MeSH
- xylém anatomie a histologie metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- voda MeSH
