Breathing Analysis Using Thermal and Depth Imaging Camera Video Records

Procházka, Aleš; Charvátová, Hana; Vyšata, Oldřich; Kopál, Jakub; Chambers, Jonathon A.

doi:10.3390/s17061408

Cited by 64 publications

(50 citation statements)

References 34 publications

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“…It is assumed that the future work will include studies related to visualization in engineering [39], modelling of temperature distribution both inside the room and in the walls [30,32,34], and the study of comfortable indoor environment to keep emotional and physical states of individuals healthy [40]. Simulations or thermal imaging [39] for breathing analysis using thermal and depth imaging camera video records will be included in the future research too.…”

Section: Discussionmentioning

confidence: 99%

Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

2018

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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%.

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Section: Discussionmentioning

confidence: 99%

Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

2018

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“…In general, the use of image and depth sensors [1,7,[9][10][11][12] allow sleep monitoring and 3D modeling of the chest and abdomen volume changes during breaths as an alternative to conventional spirometry with the use of red-green-blue (RGB) cameras [13,14], infrared cameras [15,16], or Doppler multi-radar systems [17] in addition to ultrasonic and biomotion sensors [18][19][20][21]. The paper provides motivation for further studies of methods related to big data processing problems, dimensionality reduction, principal component analysis, and parallel signal processing to increase the speed, accuracy, and reliability of the results.…”

Section: Introductionmentioning

confidence: 99%

Sleep Apnea Detection with Polysomnography and Depth Sensors

Schätz

Procházka

Kuchynka

et al. 2020

Sensors

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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 apnea 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 apneas by a sleep specialist. The resulting classifier can mark all apnea events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. When compared to the classification of polysomnographic breathing signal segments by a sleep specialist, which is used for calculating length of the event, the classifier has an 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 apnea events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

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“…Extensive attention has been paid to the analysis of these signals and their multimodal processing with further biomedical data [7,8] for feature extraction, classification, and human-computer interactions. Methods of motion detection and its analysis by accelerometers and global positioning systems (GPS) are also used for studies of physical activities including cycling [9][10][11][12][13][14], as assessed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Charvátová

Procházka

Vyšata

2020

Sensors

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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.

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Breathing Analysis Using Thermal and Depth Imaging Camera Video Records

Cited by 64 publications

References 34 publications

Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

Sleep Apnea Detection with Polysomnography and Depth Sensors

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

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