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CN-122004826-A - Personnel respiration non-contact detection method and system based on single reflection schlieren

CN122004826ACN 122004826 ACN122004826 ACN 122004826ACN-122004826-A

Abstract

The invention discloses a personnel respiration contactless detection method based on single reflection schlieren, which comprises the steps of collecting schlieren video streams of an oral-nasal area of a subject by utilizing a single reflection schlieren imaging device, enabling light spots which are focused by a light source through a reflector to fall on the edge of an entrance pupil diaphragm of an imaging lens, partially shielding the light spots by utilizing the physical edge of the entrance pupil diaphragm to form a virtual knife edge effect, processing the schlieren video streams frame by frame, calculating characteristic statistics representing air flow disturbance intensity, namely respiratory air flow intensity signals, processing baseline drift removal and frequency domain filtering on the respiratory air flow intensity signals to obtain standardized respiratory waveforms, calculating multidimensional respiratory parameters including real-time respiratory frequency, relative respiratory depth and respiratory ratio based on the respiratory waveforms through a self-adaptive waveform characteristic extraction algorithm, analyzing transient characteristics and periodicity rules of the respiratory waveforms, and identifying respiratory pauses, coughs or sneeze events.

Inventors

  • SHEN XIONG
  • MING SHILIN

Assignees

  • 天津大学

Dates

Publication Date
20260512
Application Date
20260205

Claims (10)

  1. 1. A method for the non-contact detection of the breathing of a person based on a single reflection schlieren, characterized by comprising: S1, collecting a schlieren video stream of an oral-nasal area of a subject by using a single reflection schlieren imaging device, wherein the single reflection schlieren imaging device is configured to enable a light spot which is focused back by a light source through a reflector to fall on the edge of an entrance pupil diaphragm of an imaging lens, and the physical edge of the entrance pupil diaphragm is utilized to partially shield the focused back light spot so as to form a virtual knife edge effect; s2, processing the schlieren video stream frame by frame, and calculating characteristic statistics representing air flow disturbance intensity, namely respiratory air flow intensity signals, in an interested region ROI below the mouth and nose; S3, baseline drift removal and frequency domain filtering processing are carried out on the respiratory airflow intensity signals, and standardized respiratory waveforms are obtained; s4, calculating multidimensional breathing parameters including real-time breathing frequency, relative breathing depth and breathing ratio through a self-adaptive waveform feature extraction algorithm based on the breathing waveform; S5, analyzing transient characteristics and periodicity of the respiration waveform, and identifying an apnea event, a cough event or a sneeze event.
  2. 2. The method for contactless detection of respiration of a person according to claim 1, wherein in step S2, the calculation process of the characteristic statistic is: Inter-frame difference is carried out on images of adjacent frames in the region of interest (ROI) to obtain a difference image ; Building a two-dimensional spatial weight mask adapting to the diffusion characteristics of the jet of exhaled air ; Calculating the weighted energy integral of the differential image in the region of interest (ROI) and taking the weighted energy integral as the moment Is a signal of the intensity of the respiratory airflow, the method comprises the following steps: ; The peak of the value corresponds to the moment of maximum flow velocity in the expiration phase, and the trough corresponds to the inspiration or breath-hold phase; The weight mask The weight attenuation range of the two-dimensional asymmetric Gaussian distribution taking the airflow source point as the center is adaptively adjusted according to the nasal breathing mode or the oral breathing mode.
  3. 3. The method according to claim 1, wherein in step S4, the calculation process of the real-time respiratory rate includes: Calculating an analytical signal of a respiratory waveform using a Hilbert transform to obtain a real-time amplitude envelope ; Setting a dynamic decision threshold according to the real-time amplitude envelope ; Traversing the respiration waveform when the signal value is greater than the dynamic determination threshold And at local maxima, identify a valid exhalation peak ; Calculating the time interval between two adjacent exhalation peaks Thereby calculating the real-time respiratory rate.
  4. 4. The method for contactless detection of respiration of a person according to claim 1, wherein in step S4, the method for calculating the relative respiration depth is: And performing time integration on the respiration waveform in the single respiration period, namely calculating an area value surrounded by a waveform curve and a zero baseline, and taking the area value as a quantization index of relative tidal volume to evaluate the variation trend of the respiration depth.
  5. 5. The method for contactless detection of respiration of person according to claim 1, wherein in step S4, the method for calculating the respiration rate is as follows: In a respiratory waveform, locating the expiratory starting point in a single respiratory cycle by zero crossing detection or threshold determination End of expiration point Starting point of next expiration Defining expiration time Suction and dwell time Calculating the ratio As an indicator of the inhalation-exhalation ratio.
  6. 6. The method of claim 1, wherein in step S5, the method of identifying cough or sneeze events is: calculating a first derivative of the respiratory waveform with respect to time as a waveform transient slope and monitoring a waveform amplitude; When the instantaneous slope of the waveform is detected to exceed a preset impact threshold value and the waveform amplitude exceeds a set multiple of the average value of normal respiration peaks, judging that the waveform is a cough or sneeze event; The method for identifying the apnea event comprises the step of judging the apnea event when the amplitude envelope of the breathing waveform is continuously lower than a resting respiration threshold value in a preset duration.
  7. 7. A single reflection schlieren-based personal respiration contactless detection system for implementing the personal respiration contactless detection method according to any one of claims 1 to 6, characterized by comprising a single reflection schlieren imaging device and a data processing terminal; The single reflection schlieren imaging device comprises an LED point light source, a main reflector and a high-speed camera, wherein an imaging lens is arranged on the high-speed camera, and the main reflector is arranged in front of the imaging lens; The LED point light source is arranged on one side of the high-speed camera, the front end of the LED point light source is provided with a pinhole diaphragm, and the pinhole diaphragm is positioned on a focal plane where an entrance pupil diaphragm of the imaging lens is positioned; the main reflector is used for reflecting and condensing light rays emitted by the light source to form a condensing spot; the light path is adjusted, so that the light return spot falls on the edge of an entrance pupil diaphragm of the imaging lens, the light return spot is partially shielded by utilizing the physical edge of the entrance pupil diaphragm, a virtual knife edge effect is formed, and the refractive index change caused by air flow is converted into the light intensity change entering the camera.
  8. 8. The personal respiratory non-contact detection system of claim 7, wherein the data processing terminal comprises: The signal extraction module is used for carrying out frame-by-frame processing on the video stream of the schlieren, and calculating characteristic statistics representing the disturbance intensity of the air flow, namely a respiratory air flow intensity signal, in a region of interest (ROI) below the mouth and the nose; The waveform analysis module is used for carrying out baseline drift removal and frequency domain filtering treatment on the respiratory airflow intensity signals to obtain standardized respiratory waveforms; The parameter calculation module is used for calculating multidimensional breathing parameters including real-time breathing frequency, relative breathing depth and breathing ratio through a self-adaptive waveform characteristic extraction algorithm based on the breathing waveform; And the abnormality alarm module is used for analyzing transient characteristics and periodicity of the respiration waveform, identifying an apnea, cough or sneeze event and triggering an alarm when the apnea or the cough is detected.
  9. 9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the single reflection schlieren based person respiration contactless detection method according to any one of claims 1 to 6 when the computer program is executed by the processor.
  10. 10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the single reflection schlieren-based person respiration contactless detection method according to any one of claims 1 to 6.

Description

Personnel respiration non-contact detection method and system based on single reflection schlieren Technical Field The invention relates to the technical field of biomedical signal processing and optical precision measurement intersection, in particular to a non-contact vital sign monitoring technology, and in particular relates to a detection method and a detection system for extracting human respiratory airflow characteristics through an image sequence space-time analysis algorithm based on a single reflection schlieren imaging principle, so as to invert and quantify multidimensional physiological parameters such as respiratory frequency, relative tidal volume, respiratory ratio and the like. Background Respiratory function is an important physiological indicator for assessing the health status of the human body. The respiratory rate, depth of respiration and time ratio of inspiration to expiration are of great clinical value for diagnosing sleep apnea syndrome, chronic obstructive pulmonary disease, asthma and monitoring the real-time status of critically ill patients. In addition, in the scene of preventing and controlling infectious diseases, the non-contact type screening of abnormal respiratory events for public places has important health epidemic prevention significance. At present, clinically common human breath detection methods are mainly divided into two types, namely contact type and non-contact type. The contact method mainly comprises a nasal catheter air flow sensor, chest and abdomen strap type respiration induction plethysmography technology and the like. Although the measurement is accurate, the method requires the sensor to be worn by a subject, can generate strong uncomfortable feeling after long-term use, has the risk of cross infection, and is not suitable for burn patients, newborns or people needing long-term noninductive monitoring. The non-contact method mainly comprises a temperature detection method based on infrared thermal imaging and a micro Doppler detection method based on radar. The infrared thermal imaging method relies on weak temperature difference of nostril area, is easily interfered by ambient temperature and has expensive equipment, while the radar detection method can penetrate clothes, is easily interfered by limb non-respiratory motion, has high signal extraction difficulty and is difficult to accurately restore fine waveforms of respiratory airflow. Schlieren imaging is an optical technique that can visualize refractive index changes in transparent flow fields. The gas exhaled by the human body has density difference with the surrounding air due to higher temperature and humidity, thereby generating refractive index gradient. Such a macroscopic exhalation flow can be clearly captured using schlieren techniques. However, existing schlieren breath detection studies mostly stay in the qualitative observation phase, i.e. only flow pattern images of the airflow are displayed, and a complete and automatic set of analysis algorithms from images to data is lacking. How to extract stable one-dimensional respiratory signals from complex schlieren video streams, quantitatively calculate multidimensional physiological parameters such as frequency, depth, respiratory rate and the like, and automatically identify abnormal events such as coughs, sneezes and the like is a technical problem to be solved in the field at present. Disclosure of Invention Aiming at overcoming the defects of the prior art that the prior contact type respiration detection technology has poor comfort and cross infection risk and the prior non-contact type detection technology has difficult signal extraction and difficult multi-parameter quantitative analysis, the invention provides a personnel respiration non-contact detection method and a system based on single reflection schlieren. The invention aims at realizing the following technical scheme: a single reflection schlieren-based person respiration contactless detection method, comprising: S1, collecting a schlieren video stream of an oral-nasal area of a subject by using a single reflection schlieren imaging device, wherein the single reflection schlieren imaging device is configured to enable a light spot which is focused back by a light source through a reflector to fall on the edge of an entrance pupil diaphragm of an imaging lens, and the physical edge of the entrance pupil diaphragm is utilized to partially shield the focused back light spot so as to form a virtual knife edge effect; s2, processing the schlieren video stream frame by frame, and calculating characteristic statistics representing air flow disturbance intensity, namely respiratory air flow intensity signals, in an interested region ROI below the mouth and nose; S3, baseline drift removal and frequency domain filtering processing are carried out on the respiratory airflow intensity signals, and standardized respiratory waveforms are obtained; s4, calculating multidimensional breat