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
• Respiratory Rate physiology   MeSH
• Adult MeSH
• Respiration MeSH
• Middle Aged MeSH
• Humans MeSH
• Signal Processing, Computer-Assisted MeSH
• Polysomnography methods   MeSH
• Sensitivity and Specificity MeSH
• Sleep physiology   MeSH
• Sleep Apnea Syndromes physiopathology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article 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
• Video Recording MeSH
• Time Factors MeSH
• Respiration * MeSH
• Humans MeSH
• Monitoring, Physiologic instrumentation   MeSH
• Movement MeSH
• Heart Rate physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article 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
• Algorithms MeSH
• Respiration * MeSH
• Image Processing, Computer-Assisted MeSH
• Motion MeSH
• Artificial Intelligence MeSH
• Publication type
• Journal Article 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
• Algorithms MeSH
• Gait * MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Nerve Net MeSH
• Parkinson Disease physiopathology   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Case-Control Studies MeSH
• Imaging, Three-Dimensional methods   MeSH
• Acceleration MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Objective.This work presents a method for enhanced detection, imaging, and measurement of the thermal neutron flux.Approach. Measurements were performed in a water tank, while the detector is positioned out-of-field of a 20 MeV ultra-high pulse dose rate electron beam. A semiconductor pixel detector Timepix3 with a silicon sensor partially covered by a6LiF neutron converter was used to measure the flux, spatial, and time characteristics of the neutron field. To provide absolute measurements of thermal neutron flux, the detection efficiency calibration of the detectors was performed in a reference thermal neutron field. Neutron signals are recognized and discriminated against other particles such as gamma rays and x-rays. This is achieved by the resolving power of the pixel detector using machine learning algorithms and high-resolution pattern recognition analysis of the high-energy tracks created by thermal neutron interactions in the converter.Main results. The resulting thermal neutrons equivalent dose was obtained using conversion factor (2.13(10) pSv·cm2) from thermal neutron fluence to thermal neutron equivalent dose obtained by Monte Carlo simulations. The calibrated detectors were used to characterize scattered radiation created by electron beams. The results at 12.0 cm depth in the beam axis inside of the water for a delivered dose per pulse of 1.85 Gy (pulse length of 2.4μs) at the reference depth, showed a contribution of flux of 4.07(8) × 103particles·cm-2·s-1and equivalent dose of 1.73(3) nSv per pulse, which is lower by ∼9 orders of magnitude than the delivered dose.Significance. The presented methodology for in-water measurements and identification of characteristic thermal neutrons tracks serves for the selective quantification of equivalent dose made by thermal neutrons in out-of-field particle therapy.

• MeSH
• Algorithms * MeSH
• Electrons * MeSH
• Calibration MeSH
• Neutrons MeSH
• Gamma Rays MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

PURPOSE: The aim of this paper is to investigate the limits of LET monitoring of therapeutic carbon ion beams with miniaturized microdosimetric detectors. METHODS: Four different miniaturized microdosimeters have been used at the 62 MeV/u 12C beam of INFN Southern National Laboratory (LNS) of Catania for this purpose, i.e. a mini-TEPC and a GEM-microdosimeter, both filled with propane gas, and a silicon and a diamond microdosimeter. The y-D (dose-mean lineal energy) values, measured at different depths in a PMMA phantom, have been compared withLET¯D (dose-mean LET) values in water, calculated at the same water-equivalent depth with a Monte Carlo simulation setup based on the GEANT4 toolkit. RESULTS: In these first measurements, no detector was found to be significantly better than the others as a LET monitor. The y-D relative standard deviation has been assessed to be 13% for all the detectors. On average, the ratio between y-D and LET¯D values is 0.9 ± 0.3, spanning from 0.73 ± 0.08 (in the proximal edge and Bragg peak region) to 1.1 ± 0.3 at the distal edge. CONCLUSIONS: All the four microdosimeters are able to monitor the dose-mean LET with the 11% precision up to the distal edge. In the distal edge region, the ratio of y-D to LET¯D changes. Such variability is possibly due to a dependence of the detector response on depth, since the particle mean-path length inside the detectors can vary, especially in the distal edge region.

• MeSH
• Radiotherapy Dosage MeSH
• Equipment Design MeSH
• Phantoms, Imaging MeSH
• Carbon Isotopes therapeutic use   MeSH
• Calibration MeSH
• Monte Carlo Method MeSH
• Miniaturization MeSH
• Computer Simulation MeSH
• Polymethyl Methacrylate MeSH
• Radiometry instrumentation   MeSH
• Heavy Ion Radiotherapy instrumentation   MeSH
• Water MeSH
• Publication type
• Journal Article MeSH
• Comparative Study MeSH

• MeSH
• Behavior MeSH
• Molecular Biology MeSH
• Nervous System Diseases MeSH
• Nervous System MeSH
• Neurochemistry MeSH
• Neurophysiology MeSH
• Neurons MeSH
• Neurosciences MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Fyziologie člověka a srovnávací fyziologie
• NML Fields
• neurovědy
• biologie