Patent Publication Number: US-2022233096-A1

Title: Systems and methods for non-contact respiratory monitoring

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/142,298, entitled “Systems and Methods for Non-Contact Respiratory Monitoring”, filed Jan. 27, 2021, the entirety of which is incorporated herein by reference. 
    
    
     BACKGROUND 
     Many conventional medical monitors require attachment of a sensor to a patient in order to detect physiologic signals from the patient and transmit detected signals through a cable to the monitor. These monitors process the received signals and determine vital signs such as the patient&#39;s pulse rate, respiration rate, and arterial oxygen saturation. For example, a pulse oximeter is a finger sensor that may include two light emitters and a photodetector. The sensor emits light into the patient&#39;s finger and transmits the detected light signal to a monitor. The monitor includes a processor that processes the signal, determines vital signs (e.g., pulse rate, respiration rate, arterial oxygen saturation), and displays the vital signs on a display. 
     Other monitoring systems include other types of monitors and sensors, such as electroencephalogram (EEG) sensors, blood pressure cuffs, temperature probes, air flow measurement devices (e.g., spirometer), and others. Some wireless, wearable sensors have been developed, such as wireless EEG patches and wireless pulse oximetry sensors. 
     Video-based monitoring is a new field of patient monitoring that uses a remote video camera to detect physical attributes of the patient. This type of monitoring may also be called “non-contact” monitoring in reference to the remote video sensor, which does not contact the patient. 
     SUMMARY 
     The present disclosure is directed to methods and systems for non-contact monitoring of a patient to determine respiratory parameters such as respiration rate, tidal volume, minute volume, oxygen saturation, and other parameters such as motion and activity. The systems and methods utilize a first, video signal received from the patient and from that extract a distance or depth signal from the relevant area to calculate the parameter(s) from the depth signal. The systems and methods also receive a second, light intensity signal, such as from an IR feature projected onto the patient, and from that calculate the parameter(s) from the light intensity signal. The parameter(s) from the two signals can be combined or compared to provide a qualified output parameter. 
     One particular embodiment described herein is a method qualifying a respiratory parameter of a patient by combining two measurements or calculations of that parameter. The method includes determining the respiratory parameter of the patient using depth information determined by a non-contact patient monitoring system in a region of interest (ROI), over time, between the patient and the monitoring system. The method also includes determining the respiratory parameter of the patient using light intensity information in the ROI, over time, from the patient, which is done by: projecting a feature onto the patient in the ROI; measuring a first reflected light intensity from the feature at a first time; measuring a second reflected light intensity from the feature at a second time subsequent to the first time; and comparing the first reflected light intensity and the second reflected light intensity to determine a change in position or location of the feature over time. The two parameters, the respiratory parameter of the patient using depth information and the respiratory parameter of the patient using light intensity information, are combined to provide or qualify a combined respiratory parameter. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     Other embodiments are also described and recited herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
         FIG. 1  is a schematic diagram of an example non-contact patient monitoring system according to various embodiments described herein. 
         FIG. 2  is a schematic diagram of another example non-contact patient monitoring system according to various embodiments described herein. 
         FIG. 3A  and  FIG. 3B  are schematic diagrams showing two embodiments using the example non-contact patient monitoring system of  FIG. 2 . 
         FIG. 4  is a block diagram of a computing device, a server, and an image capture device according to various embodiments described herein. 
         FIG. 5A  is a photograph of a patient being monitored by a non-contact patient monitoring system according to various embodiments described herein, with a region of interest delineated;  FIG. 5B  is an enlarged portion of the region of interest of  FIG. 5A ;  FIG. 5C  is a graphical representation of data from a non-contact patient monitoring system according to various embodiments described herein; and  FIG. 5D  is a graphical representation of additional data from a non-contact patient monitoring system according to various embodiments described herein. 
         FIG. 6  is a stepwise method of an example method of using a non-contact patient monitoring system according to various embodiments described herein. 
         FIG. 7  is a stepwise method of another example method of using a non-contact patient monitoring system according to various embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     As described above, the present disclosure is directed to medical monitoring, and in particular, non-contact, video-based monitoring of respiratory parameters, including respiration rate, tidal volume, minute volume, oxygen saturation, and other parameters such as motion or activity. Systems and methods are described for receiving a video signal view of a patient, identifying a physiologically relevant area within the video image (such as a patient&#39;s forehead or chest), extracting a distance or depth signal from the relevant area and also a light intensity signal from the relevant area, filtering those signals to focus on a physiologic component, calculating a vital sign from the signals, measuring the vital sign from the signals, and comparing the calculated vital sign to the measured vital sign. 
     The signals are detected by a camera or camera system that views but does not contact the patient. With appropriate selection and filtering of the signals detected by the camera, the physiologic contribution by the detected depth signal can be isolated and measured. Additionally, the light intensity signal is detected by at least one camera that views but does not contact the patient. With appropriate selection and filtering of the signal detected, the physiologic contribution can be estimated or calculated. 
     This approach has the potential to improve patient mobility and comfort, along with many other potential advantages discussed below. 
     Remote sensing of a patient with video-based monitoring systems presents several challenges. One challenge is due to motion or movement of the patient. The problem can be illustrated with the example of conventional, contact, pulse oximetry, which utilizes a sensor including two light emitters and a photodetector. The sensor is placed in contact with the patient, such as by clipping or adhering the sensor around a finger, toe, or ear of the patient. The sensor&#39;s emitters emit light of two particular wavelengths into the patient&#39;s tissue, and the photodetector detects the light after it is reflected or transmitted through the tissue. The detected light signal, called a photoplethysmogram (PPG), modulates with the patient&#39;s heartbeat, as each arterial pulse passes through the monitored tissue and affects the amount of light absorbed or scattered. Movement of the patient can interfere with this contact-based oximetry, introducing noise into the PPG signal due to compression of the monitored tissue, disrupted coupling of the sensor to the finger, pooling or movement of blood, exposure to ambient light, and other factors. Modern pulse oximeters use filtering algorithms to remove noise introduced by motion and to continue to monitor the pulsatile arterial signal. 
     However, movement in non-contact pulse oximetry creates different complications, due to the extent of movement possible between the patient and the camera. Because the camera is remote from the patient, the patient may move toward or away from the camera, creating a moving frame of reference, or may rotate with respect to the camera, effectively morphing the region that is being monitored. Thus, the monitored tissue can change morphology within the image frame over time. This freedom of motion of the monitored tissue with respect to the detector introduces new types of motion noise into the video-based signals. 
     Another challenge is ambient light. In this context, “ambient light” means surrounding light not emitted by components of the camera or the monitoring system. In contact-based pulse oximetry, the desired light signal is the reflected and/or transmitted light from the light emitters on the sensor, and ambient light is entirely noise. The ambient light can be filtered, removed, or avoided in order to focus on the desired signal. In contact-based pulse oximetry, contact-based sensors can be mechanically shielded from ambient light, and direct contact between the sensor and the patient also blocks much of the ambient light from reaching the detector. By contrast, in non-contact pulse oximetry, the desired physiologic signal is generated or carried by the ambient light source; thus, the ambient light cannot be entirely filtered, removed, or avoided as noise. Changes in lighting within the room, including overhead lighting, sunlight, television screens, variations in reflected light, and passing shadows from moving objects all contribute to the light signal that reaches the camera. Even subtle motions outside the field of view of the camera can reflect light onto the patient being monitored. 
     Non-contact monitoring such as video-based monitoring can deliver significant benefits over contact monitoring if the above-discussed challenges can be addressed. Some video-based monitoring can reduce cost and waste by reducing use of disposable contact sensors, replacing them with reusable camera systems. Video monitoring may also reduce the spread of infection, by reducing physical contact between caregivers and patients. Video cameras can improve patient mobility and comfort, by freeing patients from wired tethers or bulky wearable sensors. In some cases, these systems can also save time for caregivers, who no longer need to reposition, clean, inspect, or replace contact sensors. 
     The present disclosure describes methods and systems for non-contact monitoring of a patient to determine respiratory parameters such as respiration rate, tidal volume, minute volume, oxygen saturation, and other parameters such as motion and activity. The systems and methods receive a first, video signal from the patient and from that extract a distance or depth signal from the relevant area to calculate the parameter(s) from the depth signal. The systems and methods also receive a second signal, a light intensity signal reflected from the patient, and from that calculate the parameter(s) from the light intensity signal. The parameter(s) from the two signals can be combined or compared to provide a qualified output parameter. In some embodiments, the light intensity signal is a reflection of an IR feature projected onto the patient, such as by a projector. 
     The depth sensing feature of the system provides a measurement of the distance or depth between the detection system and the patient. One or two video cameras may be used to determine the depth, and change in depth, from the system to the patient. When two cameras, set at a fixed distance apart, are used, they offer stereo vision due to the slightly different perspectives of the scene from which distance information is extracted. When distinct features are present in the scene, the stereo image algorithm can find the locations of the same features in the two image streams. However, if an object is featureless (e.g., a smooth surface with a monochromatic color), then the depth camera system has difficulty resolving the perspective differences. By including an image projector to project features (e.g., in the form of dots, pixels, etc.) onto the scene, this projected feature can be monitored over time to produce an estimate of changing distance or depth. 
     In the following description, reference is made to the accompanying drawing that forms a part hereof and in which is shown by way of illustration at least one specific embodiment. The following description provides additional specific embodiments. It is to be understood that other embodiments are contemplated and may be made without departing from the scope or spirit of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense. While the present disclosure is not so limited, an appreciation of various aspects of the disclosure will be gained through a discussion of the examples, including the figures, provided below. In some instances, a reference numeral may have an associated sub-label consisting of a lower-case letter to denote one of multiple similar components. When reference is made to a reference numeral without specification of a sub-label, the reference is intended to refer to all such multiple similar components. 
       FIG. 1  shows a non-contact patient monitoring system  100  and a patient P according to an embodiment of the invention. The system  100  includes a non-contact detector system  110  placed remote from the patient P. In this embodiment, the detector system  110  includes a camera system  114 , particularly, a camera that includes an infrared (IR) detection feature. The camera  114  may be a depth sensing camera, such as a Kinect camera from Microsoft Corp. (Redmond, Wash.) or a RealSense™ D415, D435 or D455 camera from Intel Corp. (Santa Clara, Calif.). The camera system  114  is remote from the patient P, in that it is spaced apart from and does not physically contact the patient P. The camera system  114  includes a detector exposed to a field of view F that encompasses at least a portion of the patient P. 
     The camera system  114  includes a depth sensing camera that can detect a distance between the camera system  114  and objects in its field of view F. Such information can be used, as disclosed herein, to determine that a patient is within the field of view of the camera system  114  and determine a region of interest (ROI) to monitor on the patient. Once an ROI is identified, that ROI can be monitored over time, and the change in depth of points within the ROI can represent movements of the patient associated with, e.g., breathing. Accordingly, those movements, or changes of depth points within the ROI, can be used to determine, e.g., respiration rate, tidal volume, minute volume, effort to breathe, etc. 
     In some embodiments, the field of view F encompasses exposed skin of the patient. In other embodiments, the field of view F encompasses a portion of the patient&#39;s torso, covered by a blanket, sheet, or gown. 
     The camera system  114  operates at a frame rate, which is the number of image frames taken per second (or other time period). Example frame rates include 20, 30, 40, 50, or 60 frames per second, greater than 60 frames per second, or other values between those. Frame rates of 20-30 frames per second produce useful signals, though frame rates above 100 or 120 frames per second are helpful in avoiding aliasing with light flicker (for artificial lights having frequencies around 50 or 60 Hz). 
     The distance from the ROI on the patient P to the camera system  114  is measured by the system  100 . Generally, the camera system  114  detects a distance between the camera system  114  and the surface within the ROI; the change in depth or distance of the ROI can represent movements of the patient, e.g., associated with breathing. 
     In some embodiments, the system  100  determines a skeleton outline of the patient P to identify a point or points from which to extrapolate the ROI. For example, a skeleton may be used to find a center point of a chest, shoulder points, waist points, and/or any other points on a body. These points can be used to determine the ROI. For example, the ROI may be defined by filling in the area around a center point of the chest. Certain determined points may define an outer edge of an ROI, such as shoulder points. In other embodiments, instead of using a skeleton, other points are used to establish an ROI. For example, a face may be recognized, and a chest area inferred in proportion and spatial relation to the face. In other embodiments, the system  100  may establish the ROI around a point based on which parts are within a certain depth range of the point. In other words, once a point is determined that an ROI should be developed from, the system can utilize the depth information from the depth sensing camera system  114  to fill out the ROI as disclosed herein. For example, if a point on the chest is selected, depth information is utilized to determine the ROI area around the determined point that is a similar distance from the depth sensing camera  114  as the determined point. This area is likely to be a chest. 
     In another example, the patient P may wear a specially configured piece of clothing that identifies points on the body such as shoulders or the center of the chest. The system  100  may identify those points by identifying the indicating feature of the clothing. Such identifying features could be a visually encoded message (e.g., bar code, QR code, etc.), or a brightly colored shape that contrasts with the rest of the patient&#39;s clothing, etc. In some embodiments, a piece of clothing worn by the patient may have a grid or other identifiable pattern on it to aid in recognition of the patient and/or their movement. In some embodiments, the identifying feature may be stuck on the clothing using a fastening mechanism such as adhesive, a pin, etc. For example, a small sticker or other indicator may be placed on a patient&#39;s shoulders and/or center of the chest that can be easily identified from an image captured by a camera. In some embodiments, the indicator may be a sensor that can transmit a light or other information to the camera system  114  that enables its location to be identified in an image so as to help define the ROI. Therefore, different methods can be used to identify the patient and define an ROI. 
     The ROI size may differ according to the distance of the patient from the camera system. The ROI dimensions may vary linearly with the distance of the patient from the camera system. This ensures that the ROI scales according with the patient and covers the same part of the patient regardless of the patient&#39;s distance from the camera. This is accomplished by applying a scaling factor that is dependent on the distance of the patient (and the ROI) from the camera. In order to properly measure the depth changes, the actual size (area) of the ROI is determined and movements of that ROI are measured. The measured movements of the ROI and the actual size of the ROI are then used to calculate the respiratory parameter, e.g., a tidal volume. Because a patient&#39;s distance from a camera can change, e.g., due to rolling or position readjustment, the ROI associated with that patient can appear to change in size in an image from a camera. However, using the depth sensing information captured by a depth sensing camera or other type of depth sensor, the system can determine how far away from the camera the patient (and their ROI) actually is. With this information, the actual size of the ROI can be determined, allowing for accurate measurements of depth change regardless of the distance of the camera to the patient. 
     In some embodiments, the system  100  may receive a user input to identify a starting point for defining an ROI. For example, an image may be reproduced on an interface, allowing a user of the interface to select a patient for monitoring (which may be helpful where multiple humans are in view of a camera) and/or allowing the user to select a point on the patient from which the ROI can be determined (such as a point on the chest). Other methods for identifying a patient, points on the patient, and defining an ROI may also be used. 
     However, if the ROI is essentially featureless (e.g., a smooth surface with a monochromatic color, such as a blanket or sheet covering the patient P), then the camera system  114  may have difficulty resolving the perspective differences. To address this, the system  100  includes a projector  116  to project individual features (e.g., dots, crosses or Xs, lines, individual pixels, etc.) onto the ROI; the features may be visible light, UV light, infrared (IR) light, etc. The projector may be part of the detector system  110  or the overall system  100 . 
     The projector  116  generates a sequence of features over time on the ROI from which is monitored and measured the reflected light intensity. A measure of the amount, color, or brightness of light within all or a portion of the reflected feature over time is referred to as a light intensity signal. The camera system  114  detects the features from which this light intensity signal is determined. In an embodiment, each visible image projected by the projector  116  includes a two-dimensional array or grid of pixels, and each pixel may include three color components—for example, red, green, and blue. A measure of one or more color components of one or more pixels over time is referred to as a “pixel signal,” which is a type of light intensity signal. In another embodiment, when the projector  116  projects an IR feature, which is not visible to a human eye, the camera system  114  includes an infrared (IR) sensing feature. In another embodiment, the projector  116  projects a UV feature. In yet other embodiments, other modalities including millimeter-wave, hyper-spectral, etc., may be used. 
     The projector  116  may alternately or additionally project a featureless intensity pattern (e.g., a homogeneous, a gradient or any other pattern that does not necessarily have distinct features). In some embodiments, the projector  116 , or more than one projector, can project a combination of a feature-rich pattern and featureless patterns on to the ROI. 
     For one projector  116  or multiple projectors, the emission power may be dynamically controlled to modulate the light emissions, in a manner as commonly done for pulse-oximeters with LED light. 
     The detected images and diffusion measurements are sent to a computing device  120  through a wired or wireless connection  121 . The computing device  120  includes a display  122 , a processor  124 , and hardware memory  126  for storing software and computer instructions. Sequential image frames of the patient P are recorded by the video camera system  114  and sent to the processor  124  for analysis. The display  122  may be remote from the camera system  114 , such as a video screen positioned separately from the processor and memory. Other embodiments of the computing device  120  may have different, fewer, or additional components than shown in  FIG. 1 . In some embodiments, the computing device may be a server. In other embodiments, the computing device of  FIG. 1  may be additionally connected to a server. The captured images (e.g., still images, or video) can be processed or analyzed at the computing device and/or at the server to determine the parameters of the patient P as disclosed herein. 
       FIG. 2  shows another non-contact patient monitoring system  200  and a patient P. The system  200  includes a non-contact detector  210  placed remote from the patient P. In this embodiment, the detector  210  includes a first camera  214  and a second camera  215 , at least one of which includes an infrared (IR) camera feature. The cameras  214 ,  215  are positioned so that their ROI at least intersect, in some embodiments overlap. The detector  210  also includes an IR projector  216 , which projects individual features (e.g., dots, crosses or Xs, lines, or a featureless pattern, or a combination thereof etc.) onto the ROI. The projector  216  can be separate from the detector  210  or integral with the detector  210 , as shown in  FIG. 2 . In some embodiments, more than one projector  216  can be used. Both cameras  214 ,  215  are aimed to have the features projected by the projector  216  to be in the ROI. The cameras  214 ,  215  and projector  216  are remote from the patient P, in that they are spaced apart from and do not contact the patient P. In this implementation, the projector  216  is physically positioned between the cameras  214 ,  215 , whereas in other embodiments it may not be so. 
     The distance from the ROI to the cameras  214 ,  215  is measured by the system  200 . Generally, the cameras  214 ,  215  detect a distance between the cameras  214 ,  215  and the projected features on a surface within the ROI. The light from the projector  216  hitting the surface is scattered/diffused in all directions; the diffusion pattern depends on the reflective and scattering properties of the surface. The cameras  214 ,  215  also detect the light intensity of the projected individual features in their ROIs. From the distance and the light intensity, at least one physiological parameter of the patient P is monitored. 
       FIG. 3A  and  FIG. 3B  both show a non-contact detector  310  having a first camera including an IR detection feature  314 , a second IR camera including an IR detection feature  315 , and an IR projector  316 . A dot D is projected by the projector  316  onto a surface S, e.g., of a patient, via a beam  320 . Light from the dot D is reflected by the surface S and is detected by the camera  314  as beam  324  and by the camera  315  as beam  325 . 
     The light intensity returned to and observed by the cameras  314 ,  315  depends on the diffusion pattern caused by the surface S (e.g., the surface of a patient), the distance between the cameras  314 ,  315  and surface S, the surface gradient, and the orientation of the cameras  314 ,  315  relative to the surface S. In  FIG. 3A , the surface S has a first profile S 1  and in  FIG. 3B , the surface S has a second profile S 2  different than S 1 ; as an example, the first profile S 1  is during an exhale breath of a patient and the second profile S 2  is during an inhale breath of the patient. Because the surface profiles S 1  and S 2  differ, the deflection pattern from the dot D on each of the surfaces differs for the two figures. 
     During breathing (respiration), the light intensity reflection off the dot D observed by the cameras  314 ,  315  changes because the surface profile S 1  and S 2  (specifically, the gradient) changes as well as the distance between the surface S and the cameras  314 ,  315 .  FIG. 3A  shows the surface S having the surface profile S 1  at time instant t=t n  and  FIG. 3B  shows the surface S having the surface profile S 2  at a later time, specifically t=t n+1 , with S 2  being slightly changed due to motion caused by respiration. Consequently, the intensity of the projected dot D observed by the cameras  314 ,  315  will changed due to the changes of the surface S. In  FIG. 3A , a significantly greater intensity is measured by the camera  315  than the camera  314 , seen by the x and y on the beams  324 ,  325 , respectively. In  FIG. 3B , y is less than y in  FIG. 3A , whereas x in  FIG. 3B  is greater than x in  FIG. 3A . The manner in how these intensities change depends on the diffusion pattern and its change over time. As seen in  FIGS. 3A and 3B , the light intensities as measured by the cameras  314  and  315  have changed between  FIGS. 3A and 3B , and hence, the surface S has moved. Each camera will generate a signal because of the change of the intensity of dot D when the surface profile changes from time instant t=t n  to t=t n+1  due to movement. 
     In some other embodiments, a single camera and light projector can be used. For example, in  FIGS. 3A and 3B , the camera  315  is not present or is ignored. It is clear that the camera  314  will still produce a change in light intensity from time instant t=t n  to t=t n+1  due to movement. This embodiment will therefore produce only a single signal as opposed to the two signals generated by the embodiment discussed in the previous paragraph. 
       FIG. 4  is a block diagram illustrating a system including a computing device  400 , a server  425 , and an image capture device  485  (e.g., a camera, e.g., the camera system  114  or cameras  214 ,  215 ). In various embodiments, fewer, additional and/or different components may be used in the system. 
     The computing device  400  includes a processor  415  that is coupled to a memory  405 . The processor  415  can store and recall data and applications in the memory  405 , including applications that process information and send commands/signals according to any of the methods disclosed herein. The processor  415  may also display objects, applications, data, etc. on an interface/display  410 . The processor  415  may also or alternately receive inputs through the interface/display  410 . The processor  415  is also coupled to a transceiver  420 . With this configuration, the processor  415 , and subsequently the computing device  400 , can communicate with other devices, such as the server  425  through a connection  470  and the image capture device  485  through a connection  480 . For example, the computing device  400  may send to the server  425  information determined about a patient from images captured by the image capture device  485 , such as depth information of a patient in an image. 
     The server  425  also includes a processor  435  that is coupled to a memory  430  and to a transceiver  440 . The processor  435  can store and recall data and applications in the memory  430 . With this configuration, the processor  435 , and subsequently the server  425 , can communicate with other devices, such as the computing device  400  through the connection  470 . 
     The computing device  400  may be, e.g., the computing device  120  of  FIG. 1  or the computing device  220  of  FIG. 2 . Accordingly, the computing device  400  may be located remotely from the image capture device  485 , or it may be local and close to the image capture device  485  (e.g., in the same room). The processor  415  of the computing device  400  may perform any or all of the various steps disclosed herein. In other embodiments, the steps may be performed on a processor  435  of the server  425 . In some embodiments, the various steps and methods disclosed herein may be performed by both of the processors  415  and  435 . In some embodiments, certain steps may be performed by the processor  415  while others are performed by the processor  435 . In some embodiments, information determined by the processor  415  may be sent to the server  425  for storage and/or further processing. 
     The devices shown in the illustrative embodiment may be utilized in various ways. For example, either or both of the connections  470 ,  480  may be varied. For example, either or both the connections  470 ,  480  may be a hard-wired connection. A hard-wired connection may involve connecting the devices through a USB (universal serial bus) port, serial port, parallel port, or other type of wired connection to facilitate the transfer of data and information between a processor of a device and a second processor of a second device. In another example, one or both of the connections  470 ,  480  may be a dock where one device may plug into another device. As another example, one or both of the connections  470 ,  480  may be a wireless connection. These connections may be any sort of wireless connection, including, but not limited to, Bluetooth connectivity, Wi-Fi connectivity, infrared, visible light, radio frequency (RF) signals, or other wireless protocols/methods. For example, other possible modes of wireless communication may include near-field communications, such as passive radio-frequency identification (RFID) and active RFID technologies. RFID and similar near-field communications may allow the various devices to communicate in short range when they are placed proximate to one another. In yet another example, the various devices may connect through an internet (or other network) connection. That is, one or both of the connections  470 ,  480  may represent several different computing devices and network components that allow the various devices to communicate through the internet, either through a hard-wired or wireless connection. One or both of the connections  470 ,  480  may also be a combination of several modes of connection. 
     The configuration of the devices in  FIG. 4  is merely one physical system on which the disclosed embodiments may be executed. Other configurations of the devices shown may exist to practice the disclosed embodiments. Further, configurations of additional or fewer devices than the ones shown in  FIG. 4  may exist to practice the disclosed embodiments. Additionally, the devices shown in  FIG. 4  may be combined to allow for fewer devices than shown or separated such that more than the three devices exist in a system. It will be appreciated that many various combinations of computing devices may execute the methods and systems disclosed herein. Examples of such computing devices may include other types of medical devices and sensors, infrared cameras/detectors, night vision cameras/detectors, other types of cameras, radio frequency transmitters/receivers, smart phones, personal computers, servers, laptop computers, tablets, RFID enabled devices, or any combinations of such devices. 
     The method of this disclosure utilizes depth (distance) information between the camera(s) and the patient to determine a respiratory parameter such as respiratory rate. A depth image or depth map, which includes information about the distance from the camera to each point in the image, can be measured or otherwise captured by a depth sensing camera, such as a Kinect camera from Microsoft Corp. (Redmond, Wash.) or a RealSense™ D415, D435 or D455 camera from Intel Corp. (Santa Clara, Calif.) or other sensor devices based upon, for example, millimeter wave and acoustic principles to measure distance. 
     The depth image or map can be obtained by a stereo camera, a camera cluster, camera array, or a motion sensor focused on a ROI, such as a patient&#39;s chest. In some embodiments, the camera(s) are focused on visible or IR features in the ROI. Each projected feature may be monitored, less than all the features in the ROI may be monitored or all the pixels in the ROI can be monitored. 
     When multiple depth images are taken over time in a video stream, the video information includes the movement of the points within the image, as they move toward and away from the camera over time. 
     Because the image or map includes depth data from the depth sensing camera, information on the spatial location of the patient (e.g., the patient&#39;s chest) in the ROI can be determined. This information can be contained, e.g., within a matrix. As the patient breathes, the patient&#39;s chest moves toward and away from the camera, changing the depth information associated with the images over time. As a result, the location information associated with the ROI changes over time. The position of individual points within the ROI (i.e., the change in distance) may be integrated across the area of the ROI to provide a change in volume over time. 
     For example, movement of a patient&#39;s chest toward a camera as the patient&#39;s chest expands forward represents inhalation. Similarly, movement backward, away from the camera, occurs when the patient&#39;s chest contrasts with exhalation. This movement forward and backward can be tracked to determine a respiration rate. 
     Additionally, the changes in the parameter can be monitored over time for anomalies, e.g., signals of sleep apnea or other respiratory patterns. 
     In some embodiments, the depth signal from the non-contact system may need to be calibrated, e.g., to provide an absolute measure of volume. For example, the volume signal obtained from integrating points in a ROI over time may accurately track a patient&#39;s tidal volume and may be adjusted by a calibration factor or factors. The calibration or correction factor could be a linear relationship such as a linear slope and intercept, a coefficient, or other relationships. As an example, the volume signal obtained from a video camera may under-estimate the total tidal volume of a patient, due to underestimating the volume of breath that expands a patient&#39;s chest backward, away from the camera, which is not measured by the depth cameras, or upward orthogonal to the line of sight of the camera. Thus, the non-contact volume signal may be adjusted by simply adding or applying a correction or calibration factor. This correction factor can be determined in a few different ways, including measuring the actual parameter to obtain a reference value to use as a baseline. 
     In some embodiments, demographic data about a patient may be used to calibrate the depth or volume signal. From a knowledge of the patient&#39;s demographic data, which may include height, weight, chest circumference, BMI, age, sex, etc., a mapping from the measured volume signal to an actual volume signal may be determined. For example, patients of smaller height and/or weight may have less of a weighting coefficient for adjusting measured volume for a given ROI box size than patients of greater height and/or weight. Different corrections or mappings may also be used for other factors, such as whether the patient is under bedding, type/style of clothing worn by a patient (e.g., t-shirt, sweatshirt, hospital gown, dress, v-neck shirt/dress, etc.), thickness/material of clothing/bedding, a posture of the patient, and/or an activity of the patient (e.g., eating, talking, sleeping, awake, moving, walking, running, etc.). 
     As indicated above, in addition to the methodology of this disclosure utilizing depth (distance) information between the camera(s) and the patient to determine a respiratory parameter, the method also uses reflected light intensity from projected IR features (e.g., dots, grid, stripes, crosses, squares, etc., or a featureless pattern, or a combination thereof) in the scene to estimate the depth (distance). 
       FIG. 5A  shows an IR image from a subject patient. A region of interest (ROI)  500  is indicated on the image by the boxed (rectangular) region, although the ROI could have other, e.g., non-rectangular, shapes. The ROI  500  is shown enlarged in  FIG. 5B  with a pattern of projected IR features  510  readily visible in the figure. It can be readily seen that the features  510  have a varying intensity across the ROI  500 . In addition, the intensity of the features  510  varies over time as the ROI  500  moves, e.g., during a respiratory cycle. 
     This change of intensity over time of each of the projected features is used to produce a respiratory waveform plot. The waveform is formed by aggregating all the pixel values, at an instant in time, over time, from across the ROI  500  to generate a pattern signal shown in  FIG. 5C . In some embodiments, less than all the projected features in the ROI  500  are monitored; for example, only a random sampling of the projected features is monitored, or for example, every third feature is monitored. In some embodiments, each feature reflection over time is monitored only for a predetermined duration, to determine which projected features provide an accurate or otherwise desired light intensity signal, and then those selected features are monitored to obtain the signal. In some embodiments, each pixel in the ROI is monitored and the light intensity signal obtained. 
     The respiratory modulations over time, extracted from the varying intensity of the projected features, closely match those obtained from the respiration depth (distance) measured by the depth camera(s) (shown in  FIG. 5D ). 
     It should be noted that the phase of the intensity pattern signal ( FIG. 5D ) may be 180 degrees out of phase with that of the respiration modulation signal ( FIG. 5C ). This would be due the direction of the movement of the surface (e.g., exhale versus inhale) and gradient of the surface as well as orientation of the camera(s) relative to the surface, all which play a role in modulating the reflected light. 
     Across the whole ROI  500 , some features (e.g., dots or each pixel) may produce “in phase” modulations and some may be “out of phase.” These may be combined separately to produce two signals. Light returning from each of these features may be combined to produce a single respiratory signal, for example by inverting or phase-shifting by 180 degrees so where necessary to produce all in phase and then combining to get a combined pattern signal. 
     This method for producing a respiratory signal, i.e., from the intensity of the light diffusion, is independent from the depth data used to produce a signal representative of the respiratory parameter. This secondary pattern signal, from the light intensity, can be used to enhance or confirm the measurement of the respiratory parameters from the depth data. 
     For example, the calculation of respiratory rate (determined from, e.g., a plot such as  FIG. 5C ) can be combined with a similar plot of the respiratory rate obtained from the depth camera (RR depth ). This may be done, e.g., by computing respiratory rate from each signal and then averaging the two numbers, or, with a more advanced method, such as Kalman filtering.  FIG. 6  shows a method  600  for combining the data from the depth measurements with the data from the light intensity measurements to provide a combined parameter. In  FIG. 6 , the method  600  is particularly directed to respiratory rate, whereas in other embodiments a similar method is used to provide a different respiratory parameter. 
     The method  600  includes a first branch  610  that derives a respiratory parameter (specifically for this example, the respiratory rate (RR irp )) from the light intensity measurements and a second branch  620  that calculates the respiratory parameter (specifically for this example, the respiratory rate (RR depth )) from the depth measurements. The method  600  combines the derived respiratory rate (RR irp ) from the first branch  610  with the calculated respiratory rate (RR depth ) from the second branch  620 . 
     For the respiratory rate (RR irp ) derived from the light intensity measurements, the method  600  includes a step  612  where the IR images are acquired of the surface being monitored. The features within the desired ROI are inspected in step  614  for their light intensity and change in light intensity over time. From the intensity information obtained in step  614 , a respiratory pattern signal is calculated in step  616 . From this patterned signal, the respiratory rate (RR irp ) is derived. 
     For the respiratory rate (RR depth ) derived from the depth measurements, the method  600  includes step  622  where the depth image stream of the surface is acquired from a depth camera. A respiratory signal (e.g., volume) is derived from the depth stream in step  624 , from which a respiratory rate (RR depth ) is calculated in step  626 . 
     In step  630 , the derived respiratory rate (RR irp ) from step  618  is combined with the respiratory rate (RR depth ) calculated in step  626 . The two rates may be averaged (e.g., simple average or mean, median, etc.), added, or combined in any other manner. Either individual patterns or the combined pattern can be inspected for anomalies, e.g., signals of sleep apnea or other respiratory patterns. 
       FIG. 7  shows another method  700  for combining the data from the depth measurements with the data from the light intensity measurements to provide a combined parameter; the method  700  is also directed to respiratory rate. 
     The method  700  includes a first branch  710  that derives a respiratory pattern from the light intensity measurements and a second branch  720  that calculates the respiratory parameter from the depth measurements. The method  700  combines the calculated respiratory pattern from the first branch  710  with the calculated respiratory signal from the second branch  720 . 
     The method  700  includes a step  712  where the IR images are acquired of the surface being monitored. The features (e.g., dots) within the desired ROI are inspected in step  714  for their light intensity and change in light intensity over time. From the intensity information obtained in step  714 , a respiratory pattern signal is calculated in step  716 . 
     For the respiratory signal derived from the depth measurements, the method  700  includes step  722  where the depth image stream of the surface is acquired from a depth camera. A respiratory signal (e.g., respiratory volume) is derived from the depth stream in step  724 . 
     The calculated respiratory pattern signal (from step  716 ) and the derived respiratory signal (from step  724 ) are combined in step  730 , prior to calculating the respiration rate. The signals can be added, average, or otherwise combined in step  730  and then used to calculate a respiration rate in step  732  from the combined signal of step  730 . 
     In both the method  600  of  FIG. 6  and the method  700  of  FIG. 7 , the respiratory signal from the depth stream is combined with one respiratory signal obtained from the light intensity. In other embodiments, multiple light intensity signals may be obtained, e.g., one from IR features, one from visible features, one from UV features, etc., so that the respiratory signal from the depth stream is combined with multiple respiratory signals from multiple light intensity measurements. 
     Returning to and with respect to  FIG. 2  and  FIGS. 3A and 3B  above, it is described that a system  200  with two cameras  214 ,  215  or a system  300  with two cameras  314 ,  315  can be used, the two cameras  214 ,  215  and  314 ,  315  providing a stereo property for one or both of the depth signal and the light intensity signal. When two cameras are used, although both cameras will produce very similar results, they each have their own noise characteristics. The noise, which is added to the respiratory signal, is generally uncorrelated and the overall noise component is therefore reduced by combining the results of two cameras. Thus, each camera produces a respiratory pattern and the results may then be, for example, averaged. Note that more than two cameras may be used to further improve the performance. Additionally, e.g., other, more advanced, methods for combining/fusing the different respiratory signals may be used including Kalman and particle filtering. 
     Thus, described herein are methods and systems for non-contact monitoring of a patient to determine respiratory parameters by utilizing a distance or depth signal from the patient to the system to calculate the parameter(s) from the depth signal and by utilizing a reflected light intensity signal from projected IR features to derive the same parameter(s). The parameter(s) from the two signals are combined or compared to provide an output parameter value or signal. 
     The above specification and examples provide a complete description of the structure and use of exemplary embodiments of the invention. The above description provides specific embodiments. It is to be understood that other embodiments are contemplated and may be made without departing from the scope or spirit of the present disclosure. The above detailed description, therefore, is not to be taken in a limiting sense. For example, elements or features of one example, embodiment or implementation may be applied to any other example, embodiment or implementation described herein to the extent such contents do not conflict. While the present disclosure is not so limited, an appreciation of various aspects of the disclosure will be gained through a discussion of the examples provided. 
     Unless otherwise indicated, all numbers expressing feature sizes, amounts, and physical properties are to be understood as being modified by the term “about,” whether or not the term “about” is immediately present. Accordingly, unless indicated to the contrary, the numerical parameters set forth are approximations that can vary depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings disclosed herein. 
     As used herein, the singular forms “a”, “an”, and “the” encompass implementations having plural referents, unless the content clearly dictates otherwise. As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise.