Patent Publication Number: US-2022211333-A1

Title: Video-based patient monitoring systems and associated methods for detecting and monitoring breathing

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of U.S. application Ser. No. 16/535,228, filed Aug. 8, 2019, which claims the benefit of U.S. Provisional Application No. 62/716,724, filed Aug. 9, 2018, which is specifically incorporated by reference herein for all that it discloses or teaches. 
    
    
     FIELD 
     The present technology is generally related to video-based patient monitoring and to detection and monitoring of breathing of patients. 
     BACKGROUND 
     Many conventional medical monitors require attachment of a sensor to a patient in order to detect physiologic signals from the patient and to 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 can 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 field of patient monitoring that uses one or more remote video cameras to detect physical attributes of the patient. This type of monitoring can also be called “non-contact” monitoring in reference to the remote video sensor(s), which does/do not contact the patient. The remainder of this disclosure offers solutions and improvements in this field. 
     SUMMARY 
     The techniques of this disclosure generally relate to the field of medical monitoring, and, in particular, to non-contact detecting and monitoring of patient breathing. 
     In one aspect, the present disclosure provides systems, methods, and computer readable media for calculating a change in depth of regions in one or more regions of interest (ROI&#39;s) on a patient and assigning one or more visual indicators to the regions based on the calculated changes in depth of the regions over time. 
     In one aspect, a video-based patient monitoring system includes at least one processor configured to define one or more regions of interest (ROI&#39;s) on a patient and a non-contact detector having at least one image capture device. The at least one image capture device is configured to capture two or more images of the one or more ROI&#39;s. The at least one processor is further configured to: calculate a change in depth of a region of at least one of the one or more ROI&#39;s within the two or more images and assign one or more visual indicators from a predetermined visual scheme to the region of the at least one ROI based at least in part on the calculated change in depth of the region within the two or more images. 
     In one aspect, a method includes capturing two or more images of a patient, calculating a change in depth of regions on the patient within the two or more images; and assigning one or more visual indicators from a predetermined visual scheme to the regions based at least in part on the calculated changes in depth of the regions. 
     In another aspect, the disclosure provides one or more breathing parameter signals corresponding to the regions of interest that can be generated and/or analyzed. In further aspects, the one or more visual indicators can be displayed overlaid onto the regions in real-time. In additional aspects, the systems, methods, and/or computer readable media (i) can display one or more generated breathing parameter signals in real-time and/or (ii) can trigger an alert and/or an alarm when a breathing abnormality is detected. 
     The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed on illustrating clearly the principles of the present disclosure. The drawings should not be taken to limit the disclosure to the specific embodiments depicted, but are for explanation and understanding only. 
         FIG. 1  is a schematic view that illustrates a video-based patient monitoring system configured in accordance with various embodiments of the present technology; 
         FIG. 2  is a block diagram that illustrates a video-based patient monitoring system having a computing device, a server, and one or more image capture devices, and configured in accordance with various embodiments of the present technology; 
         FIG. 3  is a schematic view of a patient that illustrates various regions of interest that can be defined by video-based patient monitoring systems configured in accordance with various embodiments of the present technology; 
         FIGS. 4A and 4B  are schematic views that illustrate images of regions of interest generated from images captured using an image capture device of a video-based patient monitoring system configured in accordance with various embodiments of the present technology; 
         FIGS. 5A-5D  are schematic views that illustrate images of regions of interest generated from images captured using an image capture device of a video-based patient monitoring system configured in accordance with various embodiments of the present technology; 
         FIGS. 6A-6C  are schematic views that illustrate images of a region of interest on a patient generated from images captured using an image capture device of a video-based patient monitoring system configured in accordance with various embodiments of the present technology; 
         FIGS. 7A-7C  are schematic views that illustrate images of a region of interest on a patient generated from images captured using an image capture device of a video-based patient monitoring system configured in accordance with various embodiments of the present technology; 
         FIGS. 8A-8C  are schematic views that illustrate images of a region of interest on a patient generated from images captured using an image capture device of a video-based patient monitoring system configured in accordance with various embodiments of the present technology; 
         FIG. 9  is a schematic view that illustrates a display of four patients in four images captured and/or generated using video-based patient monitoring systems configured in accordance with various embodiments of the present technology; 
         FIG. 10A  is a line plot that illustrates a volume gain signal and a volume loss signal in a region of interest over time and generated using a video-based patient monitoring system configured in accordance with various embodiments of the present technology; 
         FIG. 10B  is a line plot that illustrates a tidal volume signal in a region of interest over time and generated using a video-based patient monitoring system configured in accordance with various embodiments of the present technology; and 
         FIG. 11  is a flow diagram that illustrates a video-based patient monitoring routine of a method for detecting and monitoring breathing in a patient in accordance with various embodiments of the present technology. 
     
    
    
     DETAILED DESCRIPTION 
     The following disclosure describes video-based patient monitoring systems and associated methods for detecting and/or monitoring patient breathing. As described in greater detail below, systems and/or methods configured in accordance with embodiments of the present technology are configured to recognize and/or identify a patient and to define one or more regions of interest (ROI&#39;s) on the patient. Additionally or alternatively, the system and/or methods are configured to capture one or more images (e.g., a video sequence) of the ROI&#39;s and/or to measure changes in depth of regions (e.g., one or more pixels or groups of pixels) in the ROI&#39;s over time. Based, at least in part, on these measurements, the systems and/or methods can assign one or more visual indicators to regions of one or more of the ROI&#39;s. In these and other embodiments, the systems and/or methods generate various breathing parameter signals of all or a subset of the ROI&#39;s. The breathing parameter signals can include tidal volume, minute volume, and/or respiratory rate, among others. In these and other embodiments, the systems and/or methods can analyze the generated signals and can trigger alerts and/or alarms when the systems and/or methods detect one or more breathing abnormalities. In these and still other embodiments, the systems and/or methods can display (e.g., in real-time) all or a subset of the assigned visual indicator(s) and/or of the generated signals on a display, e.g., to provide a user (e.g., a caregiver, a clinician, a patient, etc.) a visual indication of the patient&#39;s breathing. For example, the systems and/or methods can overlay the assigned visual indicator(s) onto the captured images of the patient to indicate (i) whether the patient is breathing and/or, (ii) whether a patient&#39;s breathing is abnormal. 
     Specific details of several embodiments of the present technology are described herein with reference to  FIGS. 1-11 . Although many of the embodiments are described with respect to devices, systems, and methods for video-based detection and/or monitoring of breathing in a human patient, other applications and other embodiments in addition to those described herein are within the scope of the present technology. For example, at least some embodiments of the present technology can be useful for video-based detection and/or monitoring of breathing in other animals and/or in non-patients (e.g., elderly or neonatal individuals within their homes). It should be noted that other embodiments in addition to those disclosed herein are within the scope of the present technology. Further, embodiments of the present technology can have different configurations, components, and/or procedures than those shown or described herein. Moreover, a person of ordinary skill in the art will understand that embodiments of the present technology can have configurations, components, and/or procedures in addition to those shown or described herein and that these and other embodiments can be without several of the configurations, components, and/or procedures shown or described herein without deviating from the present technology. 
       FIG. 1  is a schematic view of a patient  112  and a video-based patient monitoring system  100  configured in accordance with various embodiments of the present technology. The system  100  includes a non-contact detector  110  and a computing device  115 . In some embodiments, the detector  110  can include one or more image capture devices, such as one or more video cameras. In the illustrated embodiment, the non-contact detector  110  includes a video camera  114 . The non-contact detector  110  of the system  100  is placed remote from the patient  112 . More specifically, the video camera  114  of the non-contact detector  110  is positioned remote from the patient  112  in that it is spaced apart from and does not contact the patient  112 . The camera  114  includes a detector exposed to a field of view (FOV)  116  that encompasses at least a portion of the patient  112 . 
     The camera  114  can capture a sequence of images over time. The camera  114  can be a depth sensing camera, such as a Kinect camera from Microsoft Corp. (Redmond, Wash.). A depth sensing camera can detect a distance between the camera and objects within its field of view. Such information can be used, as disclosed herein, to determine that a patient  112  is within the FOV  116  of the camera  114  and/or to determine one or more regions of interest (ROI&#39;s) to monitor on the patient  112 . Once a ROI is identified, the ROI can be monitored over time, and the changes in depth of regions (e.g., pixels) within the ROI  102  can represent movements of the patient  112  associated with breathing. As described in greater detail in U.S. Provisional Patent Application Ser. No. 62/614,763, those movements, or changes of regions within the ROI  102 , can be used to determine various breathing parameters, such as tidal volume, minute volume, respiratory rate, etc. U.S. Provisional Patent Application Ser. No. 62/614,763 is incorporated herein by reference in its entirety. 
     In some embodiments, the system  100  determines a skeleton outline of the patient  112  to identify a point or points from which to extrapolate a ROI. For example, a skeleton can be used to find a center point of a chest, shoulder points, waist points, and/or any other points on a body of the patient  112 . These points can be used to determine one or more ROI&#39;s. For example, a ROI  102  can be defined by filling in area around a center point  103  of the chest, as shown in  FIG. 1 . Certain determined points can define an outer edge of the ROI  102 , such as shoulder points. In other embodiments, instead of using a skeleton, other points are used to establish a ROI. For example, a face can be recognized, and a chest area inferred in proportion and spatial relation to the face. In other embodiments, the system  100  can define a ROI around a point using parts of the patient  112  that are within a range of depths from the camera  114 . In other words, once the system  100  determines a point from which to extrapolate a ROI, the system  100  can utilize depth information from the depth sensing camera  114  to fill out the ROI. For example, if the point  103  on the chest is selected, parts of the patient  112  around the point  103  that are a similar depth from the camera  114  as the point  103  are used to determine the ROI  102 . 
     In another example, the patient  112  can wear specially configured clothing (not shown) that includes one or more features to indicate points on the body of the patient  112 , such as the patient&#39;s shoulders and/or the center of the patient&#39;s chest. The one or more features can include visually encoded message (e.g., bar code, QR code, etc.), and/or brightly colored shapes that contrast with the rest of the patient&#39;s clothing. In these and other embodiments, the one or more features can include one or more sensors that are configured to indicate their positions by transmitting light or other information to the camera  114 . In these and still other embodiments, the one or more features can include a grid or another identifiable pattern to aid the system  100  in recognizing the patient  112  and/or the patient&#39;s movement. In some embodiments, the one or more features can be stuck on the clothing using a fastening mechanism such as adhesive, a pin, etc. For example, a small sticker can be placed on a patient&#39;s shoulders and/or on the center of the patient&#39;s chest that can be easily identified within an image captured by the camera  114 . The system  100  can recognize the one or more features on the patient&#39;s clothing to identify specific points on the body of the patient  112 . In turn, the system  100  can use these points to recognize the patient  112  and/or to define a ROI. 
     In some embodiments, the system  100  can receive user input to identify a starting point for defining a ROI. For example, an image can be reproduced on a display  122  of the system  100 , allowing a user of the system  100  to select a patient  112  for monitoring (which can be helpful where multiple objects are within the FOV  116  of the camera  114 ) and/or allowing the user to select a point on the patient  112  from which a ROI can be determined (such as the point  103  on the chest of the patient  112 ). In other embodiments, other methods for identifying a patient  112 , identifying points on the patient  112 , and/or defining one or more ROI&#39;s can be used. 
     The images detected by the camera  114  can be sent to the computing device  115  through a wired or wireless connection  120 . The computing device  115  can include a processor  118  (e.g., a microprocessor), the display  122 , and/or hardware memory  126  for storing software and computer instructions. Sequential image frames of the patient  112  are recorded by the video camera  114  and sent to the processor  118  for analysis. The display  122  can be remote from the camera  114 , such as a video screen positioned separately from the processor  118  and the memory  126 . Other embodiments of the computing device  115  can have different, fewer, or additional components than shown in  FIG. 1 . In some embodiments, the computing device  115  can be a server. In other embodiments, the computing device  115  of  FIG. 1  can be additionally connected to a server (e.g., as shown in  FIG. 2  and discussed in greater detail below). The captured images/video can be processed or analyzed at the computing device  115  and/or a server to determine a variety of parameters (e.g., tidal volume, minute volume, respiratory rate, etc.) of a patient&#39;s breathing. 
       FIG. 2  is a block diagram illustrating a video-based patient monitoring system  200  (e.g., the video-based patient monitoring system  100  shown in  FIG. 1 ) having a computing device  210 , a server  225 , and one or more image capture devices  285 , and configured in accordance with various embodiments of the present technology. In various embodiments, fewer, additional, and/or different components can be used in the system  200 . The computing device  210  includes a processor  215  that is coupled to a memory  205 . The processor  215  can store and recall data and applications in the memory  205 , including applications that process information and send commands/signals according to any of the methods disclosed herein. The processor  215  can also (i) display objects, applications, data, etc. on an interface/display  207  and/or (ii) receive inputs through the interface/display  207 . As shown, the processor  215  is also coupled to a transceiver  220 . 
     The computing device  210  can communicate with other devices, such as the server  225  and/or the image capture device(s)  285  via (e.g., wired or wireless) connections  270  and/or  280 , respectively. For example, the computing device  210  can send to the server  225  information determined about a patient from images captured by the image capture device(s)  285 . The computing device  210  can be the computing device  115  of  FIG. 1 . Accordingly, the computing device  210  can be located remotely from the image capture device(s)  285 , or it can be local and close to the image capture device(s)  285  (e.g., in the same room). In various embodiments disclosed herein, the processor  215  of the computing device  210  can perform the steps disclosed herein. In other embodiments, the steps can be performed on a processor  235  of the server  225 . In some embodiments, the various steps and methods disclosed herein can be performed by both of the processors  215  and  235 . In some embodiments, certain steps can be performed by the processor  215  while others are performed by the processor  235 . In some embodiments, information determined by the processor  215  can be sent to the server  225  for storage and/or further processing. 
     In some embodiments, the image capture device(s)  285  are remote sensing device(s), such as depth sensing video camera(s), as described above with respect to  FIG. 1 . In some embodiments, the image capture device(s)  285  can be or include some other type(s) of device(s), such as proximity sensors or proximity sensor arrays, heat or infrared sensors/cameras, sound/acoustic or radiowave emitters/detectors, or other devices that include a field of view and can be used to monitor the location and/or characteristics of a patient or a region of interest (ROI) on the patient. Body imaging technology can also be utilized according to the methods disclosed herein. For example, backscatter x-ray or millimeter wave scanning technology can be utilized to scan a patient, which can be used to define and/or monitor a ROI. Advantageously, such technologies can be able to “see” through clothing, bedding, or other materials while giving an accurate representation of the patient&#39;s skin. This can allow for more accurate measurements, particularly if the patient is wearing baggy clothing or is under bedding. The image capture device(s)  285  can be described as local because they are relatively close in proximity to a patient such that at least a part of a patient is within the field of view of the image capture device(s)  285 . In some embodiments, the image capture device(s)  285  can be adjustable to ensure that the patient is captured in the field of view. For example, the image capture device(s)  285  can be physically movable, can have a changeable orientation (such as by rotating or panning), and/or can be capable of changing a focus, zoom, or other characteristic to allow the image capture device(s)  285  to adequately capture images of a patient and/or a ROI of the patient. In various embodiments, for example, the image capture device(s)  285  can focus on a ROI, zoom in on the ROI, center the ROI within a field of view by moving the image capture device(s)  285 , or otherwise adjust the field of view to allow for better and/or more accurate tracking/measurement of the ROI. 
     The server  225  includes a processor  235  that is coupled to a memory  230 . The processor  235  can store and recall data and applications in the memory  230 . The processor  235  is also coupled to a transceiver  240 . In some embodiments, the processor  235 , and subsequently the server  225 , can communicate with other devices, such as the computing device  210  through the connection  270 . 
     The devices shown in the illustrative embodiment can be utilized in various ways. For example, either the connections  270  and  280  can be varied. Either of the connections  270  and  280  can be a hard-wired connection. A hard-wired connection can involve connecting the devices through a USB (universal serial bus) port, serial port, parallel port, or other type of wired connection that can facilitate the transfer of data and information between a processor of a device and a second processor of a second device. In another embodiment, either of the connections  270  and  280  can be a dock where one device can plug into another device. In other embodiments, either of the connections  270  and  280  can be a wireless connection. These connections can take the form of 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 can include near-field communications, such as passive radio-frequency identification (RFID) and active RFID technologies. RFID and similar near-field communications can allow the various devices to communicate in short range when they are placed proximate to one another. In yet another embodiment, the various devices can connect through an internet (or other network) connection. That is, either of the connections  270  and  280  can 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. Either of the connections  270  and  280  can also be a combination of several modes of connection. 
     The configuration of the devices in  FIG. 2  is merely one physical system  200  on which the disclosed embodiments can be executed. Other configurations of the devices shown can exist to practice the disclosed embodiments. Further, configurations of additional or fewer devices than the devices shown in  FIG. 2  can exist to practice the disclosed embodiments. Additionally, the devices shown in  FIG. 2  can be combined to allow for fewer devices than shown or can be separated such that more than the three devices exist in a system. It will be appreciated that many various combinations of computing devices can execute the methods and systems disclosed herein. Examples of such computing devices can include other types of medical devices and sensors, infrared cameras/detectors, night vision cameras/detectors, other types of cameras, augmented reality goggles, virtual reality goggles, mixed reality goggle, radio frequency transmitters/receivers, smart phones, personal computers, servers, laptop computers, tablets, blackberries, RFID enabled devices, smart watch or wearables, or any combinations of such devices. 
       FIG. 3  is a schematic view of a patient  112  showing various regions of interest (ROI&#39;s) that can be defined by video-based patient monitoring systems configured in accordance with various embodiments of the present technology. As discussed above, a video-based patient monitoring system can define a ROI using a variety of methods (e.g., using extrapolation from a point on the patient  112 , using inferred positioning from proportional and/or spatial relationships with the patient&#39;s face, using parts of the patient  112  having similar depths from the camera  114  as a point, using one or more features on the patient&#39;s clothing, using user input, etc.). In some embodiments, the video-based patient monitoring system can define an aggregate ROI  102  that includes both sides of the patient&#39;s chest as well as both side of the patient&#39;s abdomen. As discussed in greater detail below, the aggregate ROI  102  can be useful in determining a patient&#39;s aggregate tidal volume, minute volume, and/or respiratory rate, among other aggregate breathing parameters. In these and other embodiments, the system  100  can define one or more smaller regions of interest within the patient&#39;s torso. For example, the system  100  can define ROI&#39;s  351 - 354 . As shown, ROI  351  corresponds to the left half of the patient&#39;s chest, ROI  352  corresponds to the left half of the patient&#39;s abdomen, ROI  353  corresponds to the right half of the patient&#39;s abdomen, and ROI  354  corresponds to the right half of the patient&#39;s chest. 
     In these and other embodiments, the system  100  can define other regions of interest in addition to or in lieu of the ROI&#39;s  102 ,  351 ,  352 ,  353 , and/or  354 . For example, the system  100  can define a ROI  356  corresponding to the patient&#39;s chest (e.g., the ROI  351  plus the ROI  354 ) and/or a ROI  357  corresponding to the patient&#39;s abdomen (e.g., the ROI  352  plus the ROI  353 ). As discussed in greater detail below, the system  100  can use ROI&#39;s  351 ,  352 ,  353 ,  354 ,  356  and/or  357  to detect paradoxical breathing of the patient  112 . In these and other embodiments, the system  100  can define a ROI  358  corresponding to the right side of the patient&#39;s chest or torso (e.g., the ROI  353  and/or the ROI  354 ) and/or a ROI  359  corresponding to the left side of the patient&#39;s chest or torso (e.g., the ROI  351  and/or the ROI  352 ). As described in greater detail below, the system  100  can use the ROI&#39;s  351 ,  352 ,  353 ,  354 ,  358 , and/or  359  to detect asymmetric breathing across the patient&#39;s chest (e.g., due to a collapsed lung). In these and still other embodiments, the system  100  can define one or more other regions of interest than shown in  FIG. 3 . For example, the system  100  can define a region of interest that includes other parts of the patient&#39;s body, such as at least a portion of the patient&#39;s neck (e.g., to detect when the patient  112  is straining to breathe). 
       FIGS. 4A and 4B  are schematic views of images  461  and  462 , respectively, of an aggregate ROI  102 . The images  461  and  462  can be generated from images of the ROI  102  captured using an image capture device of a video-based patient monitoring system configured in accordance with various embodiments of the present technology. In some embodiments, the video-based patient monitoring system can capture images of the ROI  102  by directing the image capture device toward the ROI  102  and capturing a sequence of two or more images (e.g., a video sequence) of the ROI  102 . As described in greater detail below, the generated image  461  illustrates outward movement (e.g., in real-time) of a patient&#39;s torso within the ROI  102 , whereas the generated image  462  illustrates inward movement (e.g., in real-time) of the patient&#39;s torso within the ROI  102 . 
     Using two images of the two or more captured images, the system can calculate change(s) in depth over time between the image capture device and one or more regions (e.g., one or more pixels or groups of pixels) within a ROI. For example, the system can compute a difference between a first depth of a first region  467  in the ROI  102  in a first image of the two or more captured images and a second depth of the first region  467  in the ROI  102  in a second image of the two or more captured images. In some embodiments, the system can assign visual indicators (e.g., colors, patterns, shades, concentrations, intensities, etc.) from a predetermined visual scheme to regions in an ROI. The visual indicators can correspond to changes in depth of computed by the system (e.g., to the signs and/or magnitudes of computed changes in depth). As shown in  FIGS. 4A and 4B , for example, the system can assign (i) a first pattern  471  to regions (e.g., to regions  467  and  469  in the image  461 ) in the ROI  102  that the system determines have moved toward the image capture device over time (e.g., that have exhibited negative changes in depth across two captured images), (ii) a second pattern  472  to regions (e.g., to region  467  in the image  462 ) in the ROI  102  that the system determines have moved away from the image capture device over time (e.g., that have exhibited positive changes in depth across two captured images), and/or (iii) no pattern to regions (e.g., to region  468  in the image  461 ) in the ROI  102  that the system determines have not moved toward or away from the image capture device over time (e.g., that have exhibited negligible changes in depth and/or changes in depth equivalent to zero across two images). In these and other embodiments, the system can assign a new pattern or no pattern to regions that exhibit changes in depth that the system determines are not physiological and/or are not related to respiratory motion (e.g., changes in depth that are too quick, changes in depth indicative of gross body movement, etc.). 
     In these and other embodiments, the concentration (e.g., the density) of the assigned patterns can be positively correlated with the magnitude of a computed change in depth. As shown in  FIG. 4A , for example, the concentration of the first pattern  471  assigned to the region  467  in the image  461  is much greater than the concentration of the first pattern  471  assigned to the region  469 . In other words, the portion of the patient&#39;s body that corresponds to the region  467  in the image  461  exhibited a greater change in depth toward the image capture device of the system over time than the portion of the patient&#39;s body that corresponds to the region  469  in the image  461 . 
     Although the visual indicators displayed in the images  461  and  462  illustrated in  FIGS. 4A and 4B , respectively, are patterns with varying concentrations, video-based patient monitoring systems configured in accordance with other embodiments of the present technology can use other visual indicators, such as colors, shades, and/or varying degrees of intensity, to visually depict changes in depth over time. For example, a video-based patient monitoring system can assign (i) a first color (e.g., green) to regions in the ROI that the system determines have moved toward the image capture device over time (e.g., that have exhibited negative changes in depth across two captured images) and (ii) a second color (e.g., red) to regions in the ROI that the system determines have moved away from the image capture device over time (e.g., that have exhibited positive changes in depth across two captured images). In some embodiments, the system can assign a third color or shade (e.g., black) to regions in the ROI that the system determines have not changed in depth toward or away from the image capture device over time (e.g., that have exhibited negligible changes in depth and/or changes in depth equivalent to zero across two images). 
     In these and other embodiments, the shade and/or intensity (e.g., degree of brightness) of an assigned color can be relative to an amount of excursion of a region in an ROI over time. For example, the shade and/or intensity of an assigned color can be positively correlated with a magnitude of a computed change in depth. In these embodiments, the system (i) can assign a first shade and/or a first intensity of a color (e.g., green) to a first region that the system determines has exhibited a change in depth over time having a first magnitude and (ii) can assign a lighter shade and/or a greater intensity of the color (e.g., green) to a second region that the system determines has exhibited a change in depth over time having a second magnitude greater than the first magnitude. As a result, regions in the ROI with no detected change in depth (e.g., a negligible change in depth and/or a change in depth equivalent to zero) can be displayed as black (e.g., with zero intensity) and/or appear as if no visual indicator has been assigned to these regions. 
     Regardless of the visual scheme employed, the system can display (e.g., in real-time) the assigned visual indicators over corresponding regions of the ROI in a captured image to visually portray the computed changes in depths. Thus, the assigned visual indicators can exaggerate or emphasize subtle changes in depths detected by the system. In turn, a user (e.g., a caregiver, a clinician, a patient, etc.) can quickly and easily determine whether or not a patient is breathing based on whether or not visual indicators corresponding to one or more breathing cycles of the patient are displayed over the ROI on the patient. As discussed in greater detail below, this can help a user and/or a video-based patient monitoring system to detect a variety of medical conditions, such as apnea, rapid breathing (tachypnea), slow breathing, intermittent or irregular breathing, shallow breathing, and others. 
     Additionally or alternatively, a user can quickly and easily determine a phase (e.g., inhalation and/or exhalation) of a patient&#39;s breathing. For example, a large majority of the ROI  102  in the generated image  461  illustrated in  FIG. 4A  includes the first pattern  471 . As discussed above, the first pattern  471  corresponds to negative changes in depths computed by the system. In other words, the generated image  461  illustrates that the large majority of the ROI  102  is moving toward the image capture device of the system and out from the patient&#39;s body over time. Based on this display, a user can quickly and easily determine that the patient is currently inhaling. Similarly, a large majority of the ROI  102  in the generated image  462  illustrated in  FIG. 4B  includes the second pattern  472 . As discussed above, the second pattern  472  corresponds to positive changes in depths computed by the system. In other words, the generated image  462  illustrates that the large majority of the ROI  102  is moving away from the image capture device of the system and in toward the patient&#39;s body over time. Based on this display, a user can quickly and easily determine that the patient is currently exhaling. 
       FIGS. 5A-5D  are schematic views of images  581 - 584 , respectively, of various regions of interest generated from images captured using an image capture device of a video-based patient monitoring system. As shown in  FIG. 5A , the video-based patient monitoring system has defined a ROI  356  corresponding to a patient&#39;s chest and a ROI  357  corresponding to the patient&#39;s abdomen. The first pattern  471  is displayed over a region  567  of the ROI  357  in the generated image  581 . In contrast, the second pattern  472  is displayed over a region  569  of the ROI  356  in the generated image  581 . As discussed above, the system can assign the first pattern  471  to a region that exhibits a negative change in depth over time (e.g., that exhibits movement toward the image capture device and/or out from the patient&#39;s body across two captured images), whereas the system can assign the second pattern  472  to a region that exhibits a positive change in depth over time (e.g., that exhibits movement away from the image capture device and/or in toward the patient&#39;s body across two captured images). Therefore, the generated image  581  illustrates that the region  567  is moving out from the patient&#39;s body (e.g., suggesting that the patient is inhaling) while the region  569  is moving in toward the patient&#39;s body (e.g., suggesting that the patient is exhaling). In other words, the visual indicators displayed in the image  581  illustrate paradoxical breathing of the patient (e.g., where the chest and the abdomen move out of phase with one another) that can be quickly and easily diagnosed by the system and/or a user monitoring the patient. The generated image  582  illustrated in  FIG. 5B  similarly depicts paradoxical breathing of the patient but with the chest and abdomen of the patient in opposite phases from the chest and abdomen, respectively, of the patient in the generated image  581  illustrated in  FIG. 5A . 
     Referring to  FIG. 5C , the video-based patient monitoring system has defined an aggregate ROI  555  that includes a patient&#39;s torso as well as the patient&#39;s neck. The first pattern  471  is displayed over a region  567  of the ROI  555 , indicating that the corresponding portion of the patient&#39;s body is moving toward the image capture device (e.g., that the patient is inhaling). Nevertheless, the second pattern  472  is displayed over a region  566  of the ROI  555  in the generated image  583  that corresponds to the patient&#39;s neck. This suggests that the patient&#39;s neck is moving away from the image capture device. In some embodiments, this can indicate (e.g., to the system and/or to a user) that the patient is straining to breathe. In these and other embodiments, the presence of a visual indicator (e.g., above a threshold magnitude) on a patient&#39;s neck regardless of the corresponding direction of depth change and/or regardless of whether the direction of the depth change is in sync with the patient&#39;s torso can indicate muscle tension associated with the patient straining to breathe. In other words, no visual indicator on the patient&#39;s neck or a visual indicator corresponding to a depth change of less than a threshold magnitude can indicate normal breathing and/or that the patient is not straining to breathe. 
     Referring to  FIG. 5D , the video-based patient monitoring system has defined a ROI  358  corresponding to the right half of a patient&#39;s torso and a ROI  359  corresponding to the left half of the patient&#39;s torso. The first pattern  471  is displayed over regions  564  and  565  of the ROI  358  and  359 , respectively, in the generated image  584 . The concentration (e.g., the density) of the first pattern  471  displayed over the region  564  of the ROI  358  in the generated image  584 , however, is much less than the concentration (e.g., the density) of the first pattern  471  displayed over the region  565  of the ROI  359 . As discussed above, the concentration of the first pattern  471  can correspond to (e.g., be positively correlated with) the magnitude of computed depth changes. In other words, the visual indicators displayed in the generated image  584  illustrate that the left half of the patient&#39;s torso is exhibiting larger changes in depth as the patient inhales than the right half of the patient&#39;s torso. In some embodiments, this can indicate (e.g., to the system and/or to a user) asymmetric breathing across the patient&#39;s chest, possibly due to a collapsed lung. 
       FIGS. 6A-6C  are schematic views of generated images  691 - 693 , respectively, of a ROI  102  on a patient  112  facing toward an image capture device of a video-based patient monitoring system. As shown in  FIG. 6A , the first pattern  471  is displayed over a region  667  of the ROI  102 , indicating that the patient  112  is inhaling in the generated image  691 . As shown in  FIG. 6B , the second pattern  472  is displayed over the region  667  of the ROI  102 , indicating that the patient  112  is exhaling in the generated image  692 . The patterns  471  and  472  in the generated images  691  and  692 , respectively, are also substantially uniform across the ROI  102 . Thus, a user (e.g., a caregiver and/or a clinician) is quickly able to determine that (i) the patient  112  is breathing and (ii) the breathing appears normal in the generated images  691  and  692 . 
     In contrast, the region  667  and other regions of the ROI  102  are displayed in the generated image  693  illustrated in  FIG. 6C  without the pattern  471  and without the pattern  472 . This indicates that no patient breathing is detected in the generated image  693 . If a user (e.g., a caregiver and/or a clinician) notices that no patient breathing is detected over one or more (e.g., consecutively) generated images (e.g., including the generated image  693 ), the user can determine that the patient  112  is not breathing (e.g., is exhibiting apnea) and/or is in need of urgent medical attention. 
       FIGS. 7A-7C  are schematic views of generated images  711 - 713 , respectively, of a ROI  702  on a patient  112  facing away from an image capture device of a video-based patient monitoring system. As shown in  FIG. 7A , the first pattern  471  is displayed over a region  767  of the ROI  702 , indicating that the patient  112  is inhaling in the generated image  711 . As shown in  FIG. 7B , the second pattern  472  is displayed over the region  767  of the ROI  702 , indicating that the patient  112  is exhaling in the generated image  712 . Thus, a user (e.g., a caregiver and/or a clinician) is quickly able to determine that (i) the patient  112  is breathing and (ii) the breathing appears normal in the generated images  711  and  712 . 
     In contrast, the region  767  and other regions of the ROI  702  are displayed in the generated image  713  illustrated in  FIG. 7C  without the pattern  471  and without the pattern  472 . This indicates that no patient breathing is detected in the generated image  713 . If a user (e.g., a caregiver and/or a clinician) notices that no patient breathing is detected over one or more (e.g., consecutively) generated images (e.g., including the generated image  713 ), the user can determine that the patient  112  is not breathing (e.g., is exhibiting apnea) and/or is in need of urgent medical attention. 
       FIGS. 8A-8C  are schematic views of generated images  821 - 823 , respectively, of a ROI  802  on a patient  112  on his/her side relative to an image capture device of a video-based patient monitoring system. As shown in  FIG. 8A , the first pattern  471  is displayed over a region  867  of the ROI  802 , indicating that the patient  112  is inhaling in the generated image  821 . As shown in  FIG. 8B , the second pattern  472  is displayed over the region  867  of the ROI  802 , indicating that the patient  112  is exhaling in the generated image  822 . Thus, a user (e.g., a caregiver and/or a clinician) is quickly able to determine that (i) the patient  112  is breathing and (ii) the breathing appears normal in the generated images  821  and  822 . 
     In contrast, the region  867  and other regions of the ROI  802  are displayed in the generated image  823  illustrated in  FIG. 8C  without the pattern  471  and without the pattern  472 . This indicates that no patient breathing is detected in the generated image  823 . If a user (e.g., a caregiver and/or a clinician) notices that no patient breathing is detected over one or more (e.g., consecutively) generated images (e.g., including the generated image  823 ), the user can determine that the patient  112  is not breathing (e.g., is exhibiting apnea) and/or is in need of urgent medical attention. 
       FIGS. 6A-8C  illustrate that video-based patient monitoring systems configured in accordance with embodiments of the present technology can detect and/or monitor patient breathing while a patient is in a variety of orientations with respect to image capture devices of the systems (e.g., when the patient is on his/her back, stomach, and/or side). This can be helpful in determining whether a patient is breathing and/or whether the patient&#39;s breathing is abnormal while the patient rests (e.g., at home, in a hospital bed, in a recovery room, in a neonatal ICU, etc.), especially when a patient often changes their orientation and/or when a patient&#39;s pulse oximeter and/or other medical sensors become disconnected from the patient. 
     Additionally or alternatively, the video-based patient monitoring systems can be helpful in determining whether a patient is breathing and/or whether the patient&#39;s breathing is abnormal is situations where a patient has fallen. For example, a video-based patient monitoring system can alert a caregiver at a central station (e.g., at a hospital) and/or a caregiver remote from a patient that the patient has fallen. In some embodiments, the caregiver can direct the image capture device toward the fallen patient. In these and other embodiments, the caregiver can view a sequence of generated images on a display screen to determine whether there are cyclical visual indicators (e.g., of a first color and a second color, of a first pattern and a second pattern, etc.) displayed across the sequence of generated images on the patient&#39;s torso indicating that the patient is breathing. This can allow the caregiver to quickly determine the urgency of medical attention the patient requires. 
       FIG. 9  is a schematic view of a display  930  of four patients  112  in four images  931 - 934  captured and/or generated using video-based patient monitoring systems configured in accordance with various embodiments of the present technology. In some embodiments, the display  930  can be a caregiver&#39;s display (e.g., at a central station at a hospital and/or at a remote site from the patients  112 ). In these and other embodiments, the patients  112  in the generated images  931 - 934  can be at the same and/or separate locations. For example, one or more of the patients  112  can be in recovery and/or hospital room(s) or can be at their home(s). As shown in  FIG. 9 , a patient ID  945  for each of the patients  112  can be shown on the display  930  (e.g., to allow a user to quickly determine which patient is portrayed in a given generated image). In some embodiments, a patient ID  945  can be entered for each patient  112  by a user (e.g., the patient, the caregiver, a clinician, etc.) of the systems. In these and other embodiments, the video-based patient monitoring systems can include facial recognition hardware and/or software. As discussed in greater detail below, the systems can recognize the patients  112  by recognizing one or more characteristics of the patients&#39; faces. Once the systems recognize the patients  112 , the systems can automatically populate the patient ID  945  on the display  930  for each patient  112 . 
     As shown in  FIG. 9 , a user (e.g., a caregiver, a clinician, etc.) of the system viewing (e.g., monitoring) the display  930  can quickly determine whether or not any given patient  112  is breathing and/or whether that patient&#39;s breathing is abnormal. Referring to the generated image  931 , for example, the user can quickly determine that the patient  112  is inhaling (e.g., based on the display of the first pattern  471  over a region  967  and other regions of a ROI  951  in the generated image  931 ). Similarly, a line plot  992  of a tidal volume signal  999  can be displayed beneath the generated image  931  on the display  930  to provide an indication of the patient&#39;s tidal volume over time. As described in greater detail below, the tidal volume signal  999  can be used to determine one or more abnormalities in the patient&#39;s breathing. 
     Referring to the generated image  932 , the user can similarly determine that the patient  112  is inhaling (e.g., based on the display of the first pattern  471  over a region  967  and other regions of a ROI  952  in the generated image  932 ). In contrast with the patient  112  portrayed in the generated image  931 , however, the patient  112  in the generated image  932  is straining to breath, which is evidenced by the display of the second pattern  472  on the patient&#39;s neck. Additionally a tidal volume signal  999  displayed in a line plot  992  beneath the patient  112  on the display  930  includes erratic amplitudes, illustrating that the patient  112  in the generated image  932  is rapidly and/or erratically breathing, which indicates that the patient is having difficulty breathing. The line plot  992  can be used in addition to or alternatively to the generated image  932 , to show the patient&#39;s status. 
     Referring to the generated image  933 , the user can quickly determine that no patient breathing is detected in the generated image  933  (e.g., based on the lack of the first pattern  471  and the second pattern  472  shown over a region  967  and other regions of a ROI  953  in the generated image  933 ). In some embodiments, the user can confirm that the patient  112  is not breathing by monitoring one or more (e.g., consecutively) generated images (e.g., including the generated image  933 ) and seeing that no patient breathing is detected across the one or more generated images. In these and other embodiments, the user can confirm that the patient  112  is not breathing by analyzing a tidal volume signal  999  displayed in a line plot  993  beneath the patient  112  on the display  930 . As shown, the tidal volume signal  999  in the plot  993  is relatively flat for the past 22.5 seconds, suggesting that the patient  112  has not been breathing for approximately that period of time. 
     As described in greater detail below, the system can additionally or alternatively analyze the tidal volume signal  999  and/or other breathing parameter signals to determine whether a patient  112  is exhibiting breathing abnormalities. In some embodiments, if the system detects a breathing abnormality, the system can trigger an audio and/or visual alarm to alert a user (e.g., the patient, the caregiver, the clinician, etc.). In the embodiment illustrated in  FIG. 9 , for example, the system has triggered a visual alarm  970  to alert a user that the patient  112  in the generated image  933  is exhibiting signs of apnea. 
     Referring to the generated image  934 , the user can quickly determine that the patient  112  is exhaling (e.g., based on the display of the second pattern  472  over a region  967  and other regions of a ROI  954  in the generated image  934 ). Similarly, a line plot  994  of a tidal volume signal  999  can be displayed beneath the generated image  934  on the display  930  to provide an indication of the patient&#39;s tidal volume over time. The tidal volume signal  999  in the plot  994  is substantially similar to the tidal volume signal  999  in the plot  991 , and both of these tidal volume signals  999  illustrate normal, healthy breathing with respect to tidal volume. 
       FIG. 10A  is a line plot  1091  illustrating a volume gain signal  1096  and a volume loss signal  1097  in a region of interest (e.g., ROI  102  shown in  FIGS. 1, 3, 4A and 4B ) over time and generated using a video-based patient monitoring system configured in accordance with various embodiments of the present technology. In some embodiments, the system (i) can generate the volume gain signal  1096  by (e.g., continuously) integrating (e.g., summing up) all volume increases in the ROI and/or (ii) can generate the volume loss signal  1097  by (e.g., continuously) integrating (e.g., summing up) all volume decreases in the ROI. Volume increases can correspond to negative changes in depth computed by the system (e.g., to regions of the ROI moving toward the image capture device), whereas volume decreases can correspond to positive changes in depth computed by the system (e.g., to regions of the ROI moving away from the image capture device). 
     In some embodiments, the video-based patient monitoring system can use the volume gain signal  1096  and/or the volume loss signal to determine one or more parameters of a patient&#39;s breathing. As shown in  FIG. 10A , for example, the volume gain signal  1096  is approximately 180 degrees out of phase with the volume loss signal  1097 . In other words, the volume gain signal  1096  and the volume loss signal  1097  in the plot  1091  illustrate that the patient is breathing normally. As the patient inhales, the volume gain (e.g., the sum of the magnitudes of all negative changes in depths computed by the system) in the ROI increases while the volume loss (e.g., the sum of the magnitudes of all positive changes in depths computed by the system) in the ROI decreases, and vice versa. 
     In contrast, when a patient exhibits abnormal breathing behaviors, the phase difference between the volume gain signal  1096  and the volume loss signal  1097  changes markedly away from the approximate 180-degree phase difference observed under normal breathing. For example, when a patient exhibits paradoxical breathing (as shown in  FIGS. 5A and 5B ), the phase of the volume gain signal  1096  and will be much closer to the phase of the volume loss signal  1097  (e.g., the phase difference between the volume gain signal  1096  and the volume loss signal  1097  will move closer to zero degrees). As such, the system and/or a user can detect paradoxical breathing in a patient by monitoring the volume gain signal  1096  and the volume loss signal  1097  and/or can trigger an alert and/or alarm when the volume gain signal and the volume loss signal change more than a threshold value away from the 180-degree phase difference. 
       FIG. 10B  is a line plot  1092  illustrating a tidal volume signal  1099  in a region of interest (e.g., ROI  102  shown in  FIGS. 3, 4A, and 4B ) over time and generated using a video-based patient monitoring system configured in accordance with various embodiments of the present technology. The tidal volume signal  1099  corresponds to the volume gain signal  1096  and the volume loss signal  1097  illustrated in the plot  1091  shown in  FIG. 10A . In some embodiments, the system can generate the tidal volume signal  1099  by (e.g., continuously) integrating all volume changes computed across the ROI and/or by subtracting the volume loss signal  1097  from the volume gain signal  1096 . The tidal volume signal  1099  can provide an indication of the normal volume of air displaced by a patient between normal inhalation and exhalation. 
     In some embodiments, the video-based patient monitoring system can use the tidal volume signal  1099  to determine one or more parameters of a patient&#39;s breathing. For example, a patient&#39;s respiratory rate can be calculated by determining the period of the tidal volume signal  1099 . In these and other embodiments, assuming that (i) a trough represented on the tidal volume signal  1099  corresponds to a patient&#39;s maximum exhalation and (ii) a peak represented on the tidal volume signal  1099  corresponds to the patient&#39;s maximum inhalation, the patient&#39;s inhalation tidal volume can be calculated by taking a trough to peak measurement of the tidal volume signal  1099  corresponding to a single breath of the patient. Additionally, or alternatively, a patient&#39;s exhalation tidal volume can be calculated by taking a peak to trough measurement of the tidal volume signal  1099  corresponding to a single breath of the patient. In embodiments where the tidal volume signal  1099  is displayed inverted, a peak to trough measurement of a single breath of a patient can determine the patient&#39;s inhalation tidal volume, whereas a trough to peak measurement of a single breath of the patient can determine the patient&#39;s exhalation tidal volume. These measurements taken over a minute can be used to calculate the patient&#39;s inhalation and/or exhalation minute volumes (e.g., by summing the patient&#39;s corresponding tidal volume measurements over the span of a minute, by multiplying the patient&#39;s corresponding tidal volume and the patient&#39;s respiratory rate, etc.). 
     In these and other embodiments, the video-based patient monitoring system can use the tidal volume signal  1099  as an indicator of other breathing characteristics. For example, when the tidal volume signal  1099  indicates that the patient is displacing a small volume of air between inhalation and exhalation (e.g., a negligible volume of air, a volume of air equivalent to zero, a volume of air less than a predetermined threshold volume of air and/or below a predetermined tidal volume range, etc.), the system (and/or a clinician) can determine that the patient is either not breathing and/or that the patient&#39;s breathing is restricted and/or impaired. In these and other embodiments, when the tidal volume signal  1099  indicates that the patient is displacing a large volume of air between inhalation and exhalation (e.g., a volume of air greater than a predetermined threshold volume of air and/or above a predetermined tidal volume range), the system (and/or a clinician) can determine that the patient (i) is at risk of lung injury or trauma and/or (ii) is in respiratory distress, trauma, or pain. The can be useful, for example, when a mechanical ventilator is connected to the patient. In these and still other embodiments, the system can calculate a degree of consistency in the volume of air in each breath from the tidal volume signal and/or can display the computed consistency/consistencies (e.g., to a clinician) to illustrate the variability in tidal volume over a period of time. In these and still other embodiments, the system can compute an inhalation to exhalation ratio (I/E ratio) from the tidal volume signal and/or can display the I/E ratio to a user. As described in greater detail below, the system can trigger an alert and/or an alarm when the tidal volume, the I/E ratio, and/or the degree of consistency are/move outside of one or more predetermined ranges and/or above or below one or more threshold values. 
     The generated I/E ratio and/or the generated tidal volume signal  1099  can also be useful in other applications. For example, an amount of carbon dioxide a patient exhales and/or a patient&#39;s pulse oximetry signal are often used to monitor the patient&#39;s respiration. As specific examples, a decline in the amount of carbon dioxide a patient exhales (e.g., indicated by a capnography waveform) and/or a decline in the patient&#39;s peripheral pulse oxygen saturation (e.g., the patient&#39;s SpO2 or pulse oximetry signal) can often be used as early indications of respiratory compromise. In high flow oxygen therapy, however, a high flow rate of oxygen is provided to the patient that washes out carbon dioxide the patient exhales, making it difficult to accurately determine the amount of carbon dioxide exhaled by the patient. Additionally, or alternatively, when the patient is experiencing respiratory compromise, the flow of oxygen in high flow oxygen therapy can delay and/or impair a noticeable decline in the pulse oximetry signal (e.g., the flow of oxygen can keep the oxygen saturation artificially high). As such, monitoring (i) the amount of carbon dioxide the patient exhales and/or (ii) the patient&#39;s oxygen saturation for early indications of respiratory compromise can be ineffective during high flow oxygen therapy and similar settings. The patient&#39;s tidal volume signal  1099  generated in accordance with embodiments of the present technology, however, remains useful in these settings. Thus, a decline in the patient&#39;s generated tidal volume signal  1099  can be used as an early indication of respiratory compromise (and ultimately respiratory arrest) in the high flow therapy context. 
     As additional examples, the generated I/E ratio and/or the generated tidal volume signal  1099  can be used to detect talking and/or coughing. Talking involves a large amount of exhalation followed by a quick inhalation, which can be visualized and/or detected in the generated tidal volume signal  1099 . Similarly, coughing appears and/or can be detected as localized impulses or peaks over short time scales on the generated tidal volume signal  1099 . In these and other embodiments, the system can use the generated tidal volume signal  1099  and/or other generated signals (e.g., a trending minute volume signal, a respiratory rate signal, an absolute minute volume signal, an absolute tidal volume signal, etc.) derived from the change in depth information to determine other parameters of patient breathing. For example, the generated tidal volume signal  1099  and/or a generated respiratory rate signal can be used to determine when a patient is hyperventilating, is not breathing, and/or is exhibiting apnea. 
     In some embodiments, a video-based patient monitoring system can generate more than one volume gain signal, volume loss signal, and/or tidal volume signal. For example, the system can define two ROI&#39;s (e.g. the ROI  356  and the ROI  357  shown in  FIG. 3 ) where one ROI corresponds to a patient&#39;s chest and the other ROI corresponds to patient&#39;s abdomen. In these embodiments, the system can calculate a volume gain signal and a volume loss signal for each ROI. When the volume gain signal of one ROI has the same phase as the volume gain signal of the other ROI, the system can determine that the patient&#39;s chest and abdomen are in phase (e.g., that the patient is breathing normally and/or is not exhibiting paradoxical breathing). On the other hand, when the volume gain signal of one ROI is substantially out of phase (e.g., 45 degrees out of phase, 90 degrees out of phase, 180 degrees out of phase, etc.) with the volume gain signal of the other ROI, the system can determine that the patient is exhibiting paradoxical breathing as illustrated in  FIGS. 5A and 5B . The system can perform a similar analysis using the volume loss signal and/or a tidal volume signal generated for one ROI in comparison with the volume loss signal and/or a tidal volume signal, respectively, generated for the other ROI. In some embodiments, the system can trigger an alert and/or an alarm when the system detects paradoxical breathing. 
     In these and other embodiments, the system can define two ROI&#39;s (e.g. the ROI  358  and the ROI  359  shown in  FIG. 3 ) where one ROI corresponds to the right half of a patient&#39;s chest or torso and the other ROI corresponds to left half of the patient&#39;s chest or torso. In these embodiments, the system can calculate a volume gain signal and a volume loss signal for each ROI. When the volume gain signal of one ROI is substantially out of phase (e.g., 90 or 180 degrees out of phase) with the volume gain signal of the other ROI and/or when the volume gain signal of the one ROI is exhibiting an amplitude significantly less than the amplitude of the volume gain signal of the other ROI, the system can determine that the patient is exhibiting abnormal breathing across the patient&#39;s chest (e.g., due to a collapsed lung), as illustrated in  FIG. 5D . The system can perform a similar analysis using the volume loss signal and/or a tidal volume signal generated for one ROI in comparison with the volume loss signal and/or a tidal volume signal, respectively, generated for the other ROI. In some embodiments the system can trigger an alert and/or alarm when the system detects abnormal breathing across ROI&#39;s. 
       FIG. 11  is a flow diagram illustrating a video-based patient monitoring routine  1100  of a method for detecting and monitoring breathing in a patient in accordance with various embodiments of the present technology. All or a subset of the steps of the routine  1100  can be executed by various components of a video-based patient monitoring system and/or a user of the system (e.g., a caregiver, a clinician, a patient, etc.). For example, all or a subset of the steps of the routine  1100  can be executed by (i) components of the video-based patient monitoring system  100  shown in  FIG. 1  and/or (ii) components of the video-based patient monitoring system  200  shown in  FIG. 2 . 
     The routine  1100  can begin at block  1101  by determining whether a patient is within a field of view FOV of an image capture device of the video-based patient monitoring system. In some embodiments, the routine  1100  can direct the image capture device toward a patient bed (e.g., in a hospital room, at home, etc.), and the routine  1100  can determine whether a patient is within the bed by determining whether the patient is within the FOV of the image capture device. In these and other embodiments, the routine  1100  can direct the image capture device toward the patient (e.g., to monitor a patient that has moved and/or fallen out of the FOV of the image capture device). If the routine  1100  determines that a patient is not within the FOV of the image capture device, the routine  1100  can proceed to block  1102  to trigger an alert or alarm. On the other hand, if the routine  1100  determines that a patient is within the FOV of the image capture device, the routine  1100  can proceed to block  1103  to recognize the patient and/or to define one or more regions of interest (ROI&#39;s) on the patient. 
     At block  1102 , the routine  1100  triggers an alert and/or an alarm. In some embodiments, the alert or alarm can be an audio alert or alarm to, for example, alert a clinician and/or the patient that the patient has moved and/or fallen outside of the FOV of the image capture device. In these and other embodiments, the routine  1100  can trigger a visual alert or alarm on a display. For example, the routine  1100  can display a visual alert or alarm (e.g., notification) on a display to notify a user (e.g., during set up) that the routine  1100  does not recognize a patient in the FOV of the image capture device and/or that user input is required. As another example, the routine  1100  can display a visual alert or alarm (e.g., a notification) on a display of a caregiver at a central station in a hospital and/or at a remote site. The visual alert or alarm can notify the caregiver that a patient has moved and/or fallen out of the FOV of the image capture device. This can enable the caregiver (i) to redirect the image capture device toward the patient and/or (ii) to determine whether or not the patient is breathing and/or the state of the patient&#39;s breathing (e.g., to assess the urgency of medical attention required). Additionally or alternatively, the routine  1100  can trigger an alert or alarm on a display visible to the patient to notify the patient that the patient has moved outside of the FOV of the image capture device (e.g., during a medical exam and/or other monitoring). In these and still other embodiments, the routine  1100  can trigger an alert and/or alarm unique to the routine  1100  determining a patient is not within the FOV of the image capture device (e.g., an alert and/or alarm different from other alerts and/or alarms the routine  1100  can trigger at block  1111 , discussed in greater detail below). In other embodiments, the routine  1100  can trigger a same alert and/or alarm as an alert and/or alarm triggered at block  1111 , discussed in greater detail below. 
     At block  1103 , the routine  1100  recognizes a patient within the FOV of the image capture device and/or defines one or more regions of interest (ROI&#39;s) on the patient. In some embodiments, the routine  1100  can recognize the patient by identifying the patient using facial recognition hardware and/or software of the image capture device. In these embodiments, the routine  1100  can display the name of the patient on a display screen once the routine  1100  has identified the patient. In these and other embodiments, the routine  1100  can recognize a patient within the FOV of the image capture device by determining a skeleton outline of the patient and/or by recognizing one or more characteristic features (e.g., a torso of a patient). In these and still other embodiments, the routine  1100  can define one or more ROI&#39;s on the patient in accordance with the discussion above with respect to  FIGS. 1 and/or 3 . For example, the routine  1100  can define one or more ROI&#39;s on the patient using extrapolation from a point on the patient, using inferred positioning from proportional and/or spatial relationships with the patient&#39;s face, using parts of the patient having similar depths from the camera  114  as a point, using one or more features on the patient&#39;s clothing, using user input, etc. 
     At block  1104 , the routine  1100  captures two or more images of one or more ROI&#39;s. In some embodiments, the routine  1100  can capture the two or more images of the one or more ROI&#39;s by capturing a video sequence of the one or more ROI&#39;s. In these and other embodiments, the routine  1100  can capture the two or more images of the one or more ROI&#39;s by capturing separate still images of the one or more ROI&#39;s. The routine  1100  can capture the two or more still images at a rate faster than a period of the patient&#39;s respiration cycle to ensure that the two or more still images occur within one period of the patient&#39;s respiration cycle. 
     At block  1105 , the routine  1100  can measure changes in depth of one or more regions in one or more ROI&#39;s over time. In some embodiments, the routine  1100  can measure changes in depth of regions in the one or more ROI&#39;s in accordance with the discussion above with respect to  FIGS. 4A-9 . For example, the routine  1100  can measure a change in depth by computing a difference between a depth of a region of a ROI in a first captured image of the ROI and a depth of the same region in a second captured image of the ROI. 
     At block  1106 , the routine  1100  can assign one or more visual indicators to one or more regions in the ROI. In some embodiments, the one or more visual indicators can be colors, patterns, shades, concentrations, intensities, etc. In these and other embodiments, the routine  1100  can assign the one or more visual indicators in accordance with a predetermined visual scheme. In these and still other embodiments, the routine  1100  can assign one or more visual indicators to one or more regions in accordance with the discussion above with respect to  FIGS. 4A-9 . For example, the routine  1100  can assign one or more visual indicators to the one or more regions based at least in part on the (e.g., sign and/or magnitude of a) measured/computed change in depth exhibited by a region over time (e.g., across two captured images of the ROI). 
     At block  1107 , the routine  1100  generates one or more breathing parameter signals. In some embodiments, the routine  1100  generates a volume gain signal and/or a volume loss signal for one or more ROI&#39;s in accordance with the discussion above with respect to  FIGS. 10A and/or 10B . In these and other embodiments, the routine  1100  generates a tidal volume signal for one or more ROI&#39;s in accordance with the discussion above with respect to  FIGS. 10A and/or 10B . In these and still other embodiments, the routine  1100  generates one or more other breathing parameter signals for one or more ROI&#39;s. For example, the routine  1100  can generate an inhalation-to-exhalation ratio for one or more ROI&#39;s, a degree of consistency value indicating consistency in the volume of each breath for one or more ROI&#39;s, a trending and/or an absolute minute volume signal for one or more ROI&#39;s, a respiratory rate signal for one or more ROI&#39;s, a SpO2 signal for one or more ROI&#39;s, and/or an absolute tidal volume signal for one or more ROI&#39;s, among others. 
     At block  1108 , the routine  1100  displays one or more visual indicators assigned at block  1106  over corresponding regions of one or more ROI&#39;s and/or displays one or more of the breathing parameter signals generated at block  1107 . In some embodiments, the routine  1100  can display the one or more visual indicators in accordance with the discussion above with respect to  FIGS. 4A-9 . For example, the routine  1100  can display the one or more visual indicators over a corresponding region in a corresponding ROI (e.g., over a corresponding portion of the patient). In these and other embodiments, the routine  1100  can display a generated volume gain signal, a generated volume loss signal, a generated trending tidal volume signal, a generated absolute tidal volume signal, a generated trending minute volume signal, a generated absolute minute volume signal, a generated respiratory rate signal, a generated inhalation-to-exhalation ratio, a generated degree of consistency in the volume of each breath, and/or a generated SpO2 signal for one or more ROI&#39;s. In these and still other embodiments, the one or more visual indicators and/or one or more of the generated breathing parameter signals can be displayed in real-time. In these and other embodiments, the one or more visual indicators and/or one or more of the generated breathing parameter signals can be recorded such that they can be displayed at a later time (e.g., for a clinician to review). In these and still other embodiments, the one or more visual indicators and/or one or more of the breathing parameter signals can be displayed on a clinician&#39;s display, on a caregiver&#39;s display, and/or on a patient&#39;s display. For example, the one or more visual indicators and/or one or more of the breathing parameter signals can be displayed on a caregiver&#39;s display where the display is at a central station (e.g., in a hospital) and/or at a remote site from the patient. 
     At block  1109 , the routine  1100  analyzes one or more of the breathing parameter signals generated at block  1107  to determine whether a patient is exhibiting one or more breathing abnormalities. In some embodiments, the routine  1100  can analyze one or more of the breathing parameter signals generated at block  1107  in accordance with the discussion above with respect to  FIGS. 10A and/or 10B . For example, the routine  1100  can analyze a generated volume gain signal and a generated volume loss signal corresponding to a ROI. If the volume gain signal is not approximately 180 degrees out of phase with the volume loss signal, the routine  1100  can determine that the patient is exhibiting a breathing abnormality. In the event the routine  1100  determines that the volume gain signal is substantially in phase with the volume loss signal, the routine  1100  can determine that the patient is exhibiting paradoxical breathing. 
     In these and other embodiments, the routine  1100  can analyze a generated volume gain signal for a first ROI corresponding to a patient&#39;s chest and a generated volume gain signal for a second ROI corresponding to the patient&#39;s abdomen. If the volume gain signals are substantially out of phase (e.g., 45 degrees out of phase, 90 degrees out of phase, 180 degrees out of phase, etc.) with one another, the routine  1100  can determine that the patient is exhibiting paradoxical breathing. In some embodiments, the routine  1100  can perform a similar analysis with (i) a generated volume loss signal and/or a generated tidal volume signal of the first ROI and (ii) a generated volume loss signal and/or a generated tidal volume signal, respectively, of the second ROI. 
     In these and still other embodiments, the routine  1100  can analyze a generated volume gain signal for a first ROI corresponding to the right side of a patient&#39;s chest and/or torso and a generated volume gain signal for a second ROI corresponding to the left side of the patient&#39;s chest and/or torso. If (i) the volume gain signal of the first ROI is substantially out of phase (e.g., 90 or 180 degrees out of phase) with the volume gain signal of the second ROI and/or (ii) the volume gain signal of the first ROI is exhibiting an amplitude significantly less than the amplitude of the volume gain signal of the second ROI, the routine  1100  can determine that the patient is exhibiting abnormal breathing across the patient&#39;s chest (e.g., due to a collapsed lung), as illustrated in  FIG. 5D . In some embodiments, the routine  1100  can perform a similar analysis with a volume loss signal generated for the first ROI and a volume loss signal generated for the second ROI. 
     In these and other embodiments, the routine  1100  can analyze a tidal volume signal generated for a ROI. In some embodiments, the routine  1100  can predetermine a tidal volume range (e.g., using a low threshold tidal volume value and a high threshold tidal volume value). The predetermined tidal volume range can be dependent upon a patient&#39;s characteristics (e.g., height, weight, gender, etc.). If a tidal volume for the patient falls outside of (e.g., above and/or below) the predetermined tidal volume range, the routine  1100  can determine that the patient is exhibiting a breathing abnormality. For example, if the tidal volume for the patient is and/or drops below a low tidal volume threshold value of the predetermined tidal volume range, the routine  1100  can determine that the patient is not breathing and/or that the patient&#39;s breathing is restricted and/or impaired. In these and other embodiments, if the tidal volume for the patient is and/or rises above the high tidal volume threshold value of the predetermined tidal volume range, the routine  1100  can determine that the patient (i) is at risk of lung injury or trauma (e.g., if connected to a mechanical ventilator) and/or (ii) is in respiratory distress, trauma, or pain. 
     In some embodiments, the routine  1100  can perform a similar analysis with (i) a generated inhalation-to-exhalation ratio and a predetermined inhalation-to-exhalation ratio range and/or threshold values, (ii) a generated degree of consistency in the volume of each breath and a predetermined degree of consistency range and/or threshold values, (iii) a generated volume gain signal and a predetermined volume gain range and/or threshold values, (iv) a generated volume loss signal and a predetermined volume loss range and/or threshold values, (v) a generated trending and/or absolute minute volume signal and a predetermined minute volume range and/or threshold values, (vi) a general absolute tidal volume signal and a predetermined absolute volume range and/or threshold values, (vii) a generated respiratory rate signal and a predetermined respiratory rate range and/or threshold values, and/or (viii) a generated SpO2 signal and a predetermined SpO2 range and/or threshold values, among others. For example, if a patient&#39;s respiratory rate is and/or drops below a predetermined respiratory rate threshold value and/or range, the routine  1100  can determine that the patient is not breathing, that the patient is exhibiting apnea, and/or that the patient&#39;s breathing is restricted and/or impaired. In these and other embodiments, if a patient&#39;s respiratory rate is and/or rises above a predetermined respiratory rate threshold value and/or range, the routine  1100  can determine that the patient is hyperventilating and/or is in respiratory distress, trauma, or pain. 
     In these and still other embodiments, the routine  1100  can analyze other information and/or signals generated and/or displayed by the routine  1100  at blocks  1105 ,  1106 ,  1107 , and/or  1108 . For example, the routine can analyze the I/E ratio and/or the tidal volume signal corresponding to a ROI to detect talking and/or coughing. In these and other embodiments, the routine  1100  can analyze one or more changes in depth computed by the routine  1100  at block  1105 . For example, the routine  1100  can analyze changes in depth of regions corresponding to a patient&#39;s neck to determine whether a patient is straining to breathe, as discussed above with respect to  FIG. 5C . 
     At block  1110 , the routine  1100  determines whether one or more breathing abnormalities were detected at block  1109 . If the routine  1100  determines that one or more breathing abnormalities were detected at block  1109 , the routine  1100  can proceed to block  1111  to trigger one or more alerts and/or alarms. On the other hand, if the routine  1100  determines that one or more breathing abnormalities were not detected at block  1109 , the routine  1100  can return to block  1104  to capture two or more images of one or more ROI&#39;s. In some embodiments, the routine  1100  can automatically return to block  1104  after determining whether one or more breathing abnormalities were detected at block  1109 . In other embodiments, the routine  1100  can wait to return to block  1104  until instructed to do so (e.g., by a user of the system). 
     At block  1111 , the routine  1100  triggers one or more alerts and/or alarms. In some embodiments, the routine  1100  triggers the one or more alerts and/or alarms in a manner similar to the routine  1100  at block  1102 . In these and other embodiments, the routine  1100  can trigger an alert and/or alarm to indicate a concerning condition. For example, the routine  1100  can trigger an alert and/or alarm (e.g., on a user&#39;s display) to indicate a patient is exhibiting apnea. In these and other embodiments, the routine  1100  can highlight a problematic site in the ROI on a display. In these and still other embodiments, the routine  1100  can trigger different alerts and/or alarms for different breathing abnormalities. For example, the routine can trigger an alert and/or alarm for apnea and/or a different alert and/or alarm for paradoxical breathing. In other embodiments, the routine  1100  can trigger the same alert and/or alarm for all detected breathing abnormalities. 
     Although the steps of the routine  1100  are discussed and illustrated in a particular order, the routine  1100  in  FIG. 11  is not so limited. In other embodiments, the routine  1100  can be performed in a different order. In these and other embodiments, any of the steps of the routine  1100  can be performed before, during, and/or after any of the other steps of the routine  1100 . Moreover, a person of ordinary skill in the relevant art will readily recognize that the illustrated method can be altered and still remain within these and other embodiments of the present technology. For example, before, during, and/or after executing blocks  1109  and/or  1110 , the routine  1100  can return to blocks  1101 ,  1103 ,  1105 , and/or  1107  in addition to or in lieu of returning to block  1104 . In these and other embodiments, one or more steps of the routine  1100  illustrated in  FIG. 11  can be omitted and/or repeated in some embodiments. 
     In one aspect, a video-based patient monitoring system includes at least one processor configured to define one or more regions of interest (ROI&#39;s) on a patient and a non-contact detector having at least one image capture device. The at least one image capture device is configured to capture two or more images of the one or more ROI&#39;s. The at least one processor is further configured to: calculate a change in depth of a region of at least one of the one or more ROI&#39;s within the two or more images and assign one or more visual indicators from a predetermined visual scheme to the region of the at least one ROI based at least in part on the calculated change in depth of the region within the two or more images. 
     In another aspect, a method includes capturing two or more images of a patient, calculating a change in depth of regions on the patient within the two or more images; and assigning one or more visual indicators from a predetermined visual scheme to the regions based at least in part on the calculated changes in depth of the regions. 
     In exemplary aspects, the at least one image capture device is a depth sensing camera. In additional exemplary aspects, the at least one processor is configured to assign the one or more visual indicators to the region of the at least one ROI based at least in part on a sign and/or a magnitude of the calculated change in depth of the region within the two or more images. 
     In additional exemplary aspects, the one or more visual indicators include a color, a shade, a pattern, a concentration, and/or an intensity. 
     In additional exemplary aspects, the at least one processor is further configured to display the one or more assigned visual indicators overlaid onto to the region of the at least one ROI. 
     In additional exemplary aspects, the at least one processor is further configured to generate one or more breathing parameter signals for the at least one ROI, and wherein the one or more breathing parameter signals include a volume gain signal, a volume loss signal, a tidal volume signal, a minute volume signal, a respiratory rate signal, an inhalation-to-exhalation ratio, a degree of consistency signal, and/or a SpO2 signal. 
     In additional exemplary aspects, the at least one processor is further configured monitor one or more breathing parameter signals for the at least one ROI and to trigger an alert and/or an alarm when a volume gain signal and a volume loss signal are not approximately 180 degrees out of phase, a tidal volume signal is below a first threshold tidal volume level and/or is above a second threshold tidal volume level, and/or the tidal volume signal indicates the patient is talking and/or coughing. 
     In additional exemplary aspects, the at least one processor is further configured to monitor one or more breathing parameter signals for the at least one ROI and to trigger an alert and/or an alarm when a minute volume signal is below a first threshold minute volume level and/or is above a second threshold minute volume level, a respiratory rate signal is below a first threshold respiratory rate level and/or is above a second threshold respiratory rate level, an inhalation-to-exhalation ratio is below a first threshold inhalation-to-exhalation ratio value and/or is above a second threshold inhalation-to-exhalation ratio value, a degree of consistency signal is below a first threshold degree of consistency level and/or is above a second degree of consistency level, and/or a SpO2 signal is below a first threshold SpO2 level and/or is above a second threshold SpO2 level. 
     In additional exemplary aspects, the at least one ROI includes at least two ROI&#39;s, wherein the at least one processor is further configured to generate one or more breathing parameter signals for each ROI of the at least two ROI&#39;s, and wherein the one or more breathing parameter signals include a volume gain signal, a volume loss signal, a tidal volume signal, a minute volume signal, a respiratory rate signal, an inhalation-to-exhalation ratio, a degree of consistency signal, and/or a SpO2 signal. 
     In additional exemplary aspects, the at least one ROI includes at least two ROI&#39;s, and wherein the at least one processor is further configured monitor one or more breathing parameter signals generated for each ROI of the at least two ROI&#39;s and to trigger an alert and/or an alarm when a volume gain signal of a first ROI and/or a volume loss signal of the first ROI is substantially in phase with a volume loss signal of a second ROI and/or a volume gain signal of the second ROI, respectively, the volume gain signal of the first ROI, the volume loss signal of the first ROI, and/or a tidal volume signal of the first ROI is substantially out of phase with the volume gain signal of the second ROI, the volume loss signal of the second ROI, and/or a tidal volume signal of the second ROI, respectively, and/or an amplitude of the volume gain signal of the first ROI, of the volume loss signal of the first ROI, and/or of the tidal volume signal of the first ROI varies from an amplitude of the volume gain signal of the second ROI, of the volume loss signal of the second ROI, and/or of the tidal volume signal of the second ROI, respectively, by more than a predetermined threshold value. 
     In additional exemplary aspects, the at least one processor is further configured (i) to monitor calculated changes in depth of a region of the at least one ROI corresponding to the patient&#39;s neck and (ii) trigger an alert and/or an alarm when the at least one processor determines that the calculated changes in depth of the region corresponding the patient&#39;s neck indicate that the patient is straining to breathe. 
     In additional exemplary aspects, the at least one processor is further configured to identify the patient within a field of view of the at least one image capture device by performing facial recognition on the patient. 
     In additional exemplary aspects, the at least one processor is further configured to recognize when the patient is within a field of view of the at least one image capture device and/or to trigger an alert and/or an alarm when the at least one processor determines that the patient has fallen and/or has moved outside of the field of view. 
     In additional exemplary aspects, the at least one processor is further configured to display the one or more visual indicators overlaid onto the regions of the at least one ROI in real-time, display one or more generated breathing parameter signals in real-time, and/or display plots of one or more generated breathing parameter signals in real-time and over time. 
     The above detailed descriptions of embodiments of the technology are not intended to be exhaustive or to limit the technology to the precise form disclosed above. Although specific embodiments of, and examples for, the technology are described above for illustrative purposes, various equivalent modifications are possible within the scope of the technology, as those skilled in the relevant art will recognize. For example, while steps are presented in a given order, alternative embodiments can perform steps in a different order. Furthermore, the various embodiments described herein can also be combined to provide further embodiments. 
     The systems and methods described herein can be provided in the form of tangible and non-transitory machine-readable medium or media (such as a hard disk drive, hardware memory, etc.) having instructions recorded thereon for execution by a processor or computer. The set of instructions can include various commands that instruct the computer or processor to perform specific operations such as the methods and processes of the various embodiments described here. The set of instructions can be in the form of a software program or application. The computer storage media can include volatile and non-volatile media, and removable and non-removable media, for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media can include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, CD-ROM, DVD, or other optical storage, magnetic disk storage, or any other hardware medium which can be used to store desired information and that can be accessed by components of the system. Components of the system can communicate with each other via wired or wireless communication. The components can be separate from each other, or various combinations of components can be integrated together into a monitor or processor, or contained within a workstation with standard computer hardware (for example, processors, circuitry, logic circuits, memory, and the like). The system can include processing devices such as microprocessors, microcontrollers, integrated circuits, control units, storage media, and other hardware. 
     From the foregoing, it will be appreciated that specific embodiments of the technology have been described herein for purposes of illustration, but well-known structures and functions have not been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments of the technology. To the extent any materials incorporated herein by reference conflict with the present disclosure, the present disclosure controls. Where the context permits, singular or plural terms can also include the plural or singular term, respectively. Moreover, unless the word “or” is expressly limited to mean only a single item exclusive from the other items in reference to a list of two or more items, then the use of “or” in such a list is to be interpreted as including (a) any single item in the list, (b) all of the items in the list, or (c) any combination of the items in the list. Where the context permits, singular or plural terms can also include the plural or singular term, respectively. Additionally, the terms “comprising,” “including,” “having” and “with” are used throughout to mean including at least the recited feature(s) such that any greater number of the same feature and/or additional types of other features are not precluded. Furthermore, as used herein, the term “substantially” refers to the complete or nearly complete extent or degree of an action, characteristic, property, state, structure, item, or result. For example, an object that is “substantially” enclosed would mean that the object is either completely enclosed or nearly completely enclosed. The exact allowable degree of deviation from absolute completeness may in some cases depend on the specific context. However, generally speaking the nearness of completion will be so as to have the same overall result as if absolute and total completion were obtained. The use of “substantially” is equally applicable when used in a negative connotation to refer to the complete or near complete lack of an action, characteristic, property, state, structure, item, or result. 
     It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the techniques). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device. 
     In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer). 
     Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.