Patent Publication Number: US-2022225893-A1

Title: Methods for automatic patient tidal volume determination using non-contact patient monitoring systems

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/137,886, entitled “Methods for Automatic Patient Tidal Volume Determination Using Non-Contact Patient Monitoring Systems”, filed Jan. 15, 2021, the entirety of which is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to the use of non-contact patient monitoring systems to automatically determine a patient tidal volume for breathing. In some embodiments, the systems and methods described herein can employ non-contact patient monitoring technology to determine various characteristics of a patient that can then be used in calculating an appropriate patient tidal volume. Non-limiting examples of patient characteristics that can be obtained using non-contact patient monitoring technology include patient height, patient gender, and the length of one or more segments of the patient&#39;s body. In some embodiments, the measured patient characteristic or characteristics obtained using non-contact patient monitoring technology are used to calculate predicted (also sometimes referred to as ideal) patient height and/or body weight, which are then used to calculate an appropriate patient tidal volume. 
     BACKGROUND 
     The outcome of mechanical ventilation may be influenced by the size of breath given to a patient in relation to the size of that patient&#39;s lungs. The size of the lungs is influenced by, e.g., the height and gender of the patient, which in turn determines ideal/predicted body weight. Lung protection ventilation strategies are based on keeping delivered volume within a target range of mL of volume delivered for each kg of ideal/predicted body weight (mL/kg). 
     When a ventilator is used on a patient, various initial settings are input to ensure that the amount of air supplied to the ventilated patient with each breath is appropriate for the size of that patient&#39;s lungs. Initial tidal volume-related settings can be selected using patient demographics, such as gender, height, and/or predicted (ideal) weight of the patient. In one example, a predicted body weight (PBW) is calculated based on the gender and height of the patient using one of various preestablished formula (see, e.g., Moreault, O., Lacasse, Y., &amp; Bussières, J. S. (2017). Calculating ideal body weight: Keep it simple. Anesthesiology: The Journal of the American Society of Anesthesiologists, 127(1), 203-204). The PBW measurement is then used to set an initial tidal volume setting on the ventilator, again using one of various preestablished formula or correlation charts. 
     Selecting an appropriate tidal volume setting for a ventilator can therefore depend heavily on obtaining accurate measurements of the various patient demographics. In an ideal setting, a patient&#39;s height is manually measured by a clinician so that subsequent calculations used to determine an appropriate tidal volume setting and which rely on the patient&#39;s height are as accurate as possible. However, it has been recently observed that many clinicians continue to only estimate a patient&#39;s height based on visual observation. Furthermore, in emergency situations, the clinician may not have the time or ability to take a manual measurement of the patient. As a result, erroneous tidal volume settings occur more frequently than desired based on these inaccurate patient demographic measurements. 
     Accordingly, a need exists for methods and systems capable of automating an accurate measurement of various patient demographics used in establishing appropriate patient tidal volume so that more appropriate tidal volume settings can be used, regardless of the clinician&#39;s ability to manually measure such patient demographics. 
     SUMMARY 
     Described herein are various embodiments of methods and systems for automatic determination of a patient&#39;s tidal volume using non-contact video-based patient monitoring technology. In one embodiment, a video-based patient monitoring method includes: obtaining a depth sensing image of a patient using a depth sensing camera, the depth sensing image encompassing at least the length of the patient&#39;s body; from the depth sensing image, determining the patient&#39;s height; calculating a predictive body weight of the patient based on the determined patient height; and calculating a tidal volume for the patient based on the calculated predictive body weight. 
     In another embodiment, a video-based patient monitoring method includes: obtaining a depth sensing image of a patient using a depth sensing camera; from the depth sensing image, determining the length of a segment of the patient&#39;s body; calculating a patient height from the length of the segment of the patient&#39;s body; calculating a predicted body weight of the patient based on the calculated patient height; and calculating a tidal volume for the patient based on the calculated predicted body weight. 
     In another embodiment, a video-based patient monitoring method includes: obtaining a depth sensing image of a patient using a depth sensing camera, the depth sensing image encompassing at least the patient&#39;s body; from the depth sensing image, determining the patient&#39;s body volume; and calculating a tidal volume of the patient based on the patient&#39;s body volume. 
    
    
     
       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 drawing 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 of a video-based patient monitoring system configured in accordance with various embodiments of the present technology. 
         FIG. 2  is a block diagram illustrating a video-based patient monitoring system having a computing device, a server, and one or more image capturing devices, and configured in accordance with various embodiments of the present technology. 
         FIG. 3  is flow chart illustrating a patient monitoring method configured in accordance with various embodiments of the present technology 
         FIG. 4  is an illustration of a non-contact video-based patient monitoring system suitable for use in various embodiments of the present technology. 
         FIG. 4A  is an illustration of a non-contact video-based patient monitoring system suitable for use in various embodiments of the present technology. 
         FIG. 5  is an illustration of a method for determining a ratio of patient shoulder to patient waist suitable for use in embodiments of the present technology. 
         FIG. 6  is an illustration of a method for determining patient height suitable for use in various embodiments of the present technology. 
         FIG. 7  is an illustration of a method for determining patient height suitable for use in various embodiments of the present technology. 
         FIG. 8  is an illustration of a method for determining patient tidal volume suitable for use in various embodiments of the present technology. 
     
    
    
     DETAILED DESCRIPTION 
     Specific details of several embodiment of the present technology are described herein with reference to  FIGS. 1-8 . Although many of the embodiments are described with respect to devices, systems, and methods for automatic determination of patient tidal volume using video-based non-contact patient monitoring technology, 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 monitoring of 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.) or Intel camera such as the D415, D435, and SR305 cameras from Intel Corp, (Santa Clara, Calif.). A depth sensing camera can detect a distance between the camera and objects within its field of view. Such information can be used 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) 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 . 
     In some embodiments, the system  100  determines a skeleton-like outline of the patient  112  to identify a point or points from which to extrapolate a ROI. For example, a skeleton-like outline 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 ROIs. 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, a reference point of a patient&#39;s chest can be obtained (e.g., through a previous 3-D scan of the patient), and the reference point can be registered with a current 3-D scan of the patient. In these and 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, e.g., a patient&#39;s position while lying in bed or a patient&#39;s change from a first position to second position while lying in bed. In some embodiments, some or all of the processing may be performed by the camera, such as by a processor integrated into the camera or when some or all of the computing device  115  is incorporated into the camera. 
       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 radio wave 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  or  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. 
     With reference to  FIG. 3 , a method  300  for use in automatically determining the tidal volume of a patient includes a step  310  of obtaining a depth sensing image of a patient using a depth sensing camera, a step  320  of determining the patient&#39;s height from the depth sensing image, a step  330  of calculating the patient&#39;s predictive body weight (PBW) using the height measurement obtained in step  320 , and a step  340  of calculating the patient&#39;s tidal volume using the PBW calculated in step  330 . By employing method  300 , a clinician can obtain more accurate measurements of the patient tidal volume without having to either manually measure the height of the patient or make an estimate of the patient height based on visual inspection. Instead, the depth sensing camera and associated components of a non-contact patient monitoring system obtain an accurate measurement of the patient&#39;s height and automatically perform all subsequent calculations required to provide a tidal volume calculation for the patient. The patient monitoring system may also be communicatively coupled with a ventilator and associated control componentry such that the calculated tidal volume is automatically transmitted to the ventilator and set as the tidal volume for the ventilated patient. 
       FIGS. 4 and 4A  provide further detail regarding steps  310  and  320  of method  300  wherein a depth sensing camera  114  is used to obtain a depth sensing image of the patient  400  and a patient height calculation is extracted from the depth sensing image. Starting with reference to  FIG. 4 , the camera  114  may be similar or identical to the camera  114  described previously with respect to  FIG. 2 , and may be positioned over patient  400  so as to have a clear view of the patient  400 . While camera  114  is shown in  FIG. 4  as being positioned directly over the patient  400 , it should be appreciated that the camera  114  can be located at other positions relative to the patient  400 , provided that the camera  114  has a clear view of the patient  400 . In some embodiments, the camera  114  is positioned so that the depth sensing image of the patient  400  captured by the camera  114  encompasses the length of the patient  400  (i.e., from the patient&#39;s head to the patient&#39;s feet). In this manner, an accurate height measurement of the patient  400  can be obtained from the depth sensing image. While depth sensing images of the patient  400  including less than the entire length of the patient&#39;s body can also be used in the method  300 , such instances will generally require that extrapolations and/or assumptions regarding the total height of the patient  400  be made from the measured portion of the patient  400  included in the image, which may then reduce the accuracy of the overall height measurement and subsequent calculations made thereon. 
     Any manner of determining the patient&#39;s height from the depth sensing image captured by the camera  114  can be used. The computing device  115  associated with the camera  114  as shown in  FIG. 2  (including processor  118  and memory  124 ) may include computer-executable instructions specifically designed to analyze and process data within the captured depth sensing image to determine patient height. In some embodiments, the computing device  115  may be programmed with instructions that are capable of using the depth sensing image and associated data to identify the approximate boundary of the patient&#39;s body, from which the patient height can then be measured. For example, when the outline of the patient body is determined from the depth sensing image, the software can further identify the head and feet of the patient  400  based on the outline, and measure the distance between the feet and the head to thereby obtain the patient height. 
     In other embodiments, the computing device  115  runs executable instructions that identify the opposite ends of the patient  400  based on the depth sensing data within the depth sensing image. For example, the computing device  115  may analyze the depth sensing data within the depth sensing image to identify a first end of the patient  400  and a second end of the patient  400  opposite the first end. Identifying these ends may be based on, e.g., identifying locations within the depth sensing image where relatively large changes in measured distance occur, such large changes denoting a transition from the patient&#39;s body to the bed upon which the patient  400  is positioned. 
     The computing device  115  may also employ, in conjunction with the camera  114  and the captured patient depth sensing image, computer-executable instructions capable of identifying a predicted patient foot region and/or a predicted patient head region within the depth sensing image. Such predicted regions can then be used to measure the height of the patient  400 . 
     With respect to identifying a predicted foot region of the patient  400 , a predicted foot region can be determined using any suitable methods, including using methods similar to those described previously. For example, in a method where the depth sensing image is used to determine an outline of the patient, the shape of the obtained outline can be analyzed to identify the predicted foot region. The predicted foot region can be identified based on the predicted foot region having a shape similar to an expected foot region for standard body outlines, or may be identified based on its spatial relation to other portions of the outline that are identified as other parts of the patient&#39;s body. For example, identification of a hand, waist, hip, etc., from the obtained boundary shape of the patient may then be used to identify other portions of the outline based on proximity/spatial relation to the identified body part. 
     With reference to  FIG. 6 , identifying a first end of the patient  400  (i.e., the feet of the patient  400 ) for purposes of measuring the height of the patient  400  may be complicated by the presence of a blanket covering a portion of the patient&#39;s body. As shown in  FIG. 6 , patient  400  is covered from the neck down by a blanket  401 . The presence of the blanket  401  may result in some amount of draping at different portions of the patient&#39;s body and/or obscure the outline of the patient&#39;s body, which may impact depth readings in the associated depth image captured by a depth sensing camera positioned over the patient  400 . In order to accommodate for these problems, a method of identifying a first end of a patient  400  for purposes of ultimately determining a patient&#39;s height may include identifying a predicted foot region, such as by any of the method described previously, and then identifying within the predicted foot region a peak height  610 . The peak height  610  within the predicted foot region is then assumed to be the toes of the patient  400 , which then allows for identifying the first end of the of the patient  400  at or proximate the peak height  610 . 
     With respect to identifying a predicted head region of the patient  400 , a predicted head region can be determined using any suitable method, including using methods similar to those described previously with respect to identifying a predicted foot region. In some embodiments, facial recognition software can be integrated into the computing device  115  so that the facial recognition software can be used to assist with identifying a predicted head region. For example, facial recognition software can be used to identify the location of a face in the image captured by the camera  114 . Location of a face by facial recognition software can then allow for assigning a predicted head region in the location of and/or encompassing the recognized face. A more precise predicted head region determination can be achieved by further using the identification of specific facial features within a facial recognition. For example, identification of a nose or eyes within a facial recognition analysis can then be used to more accurately identify a predicted head region, the predicted head region being established at least based on its proximity and/or special relation to the identified facial features. 
     With reference to  FIG. 4A , the methods and systems described herein may also take into account whether a portion of the patient  400  is positioned at an angle to thereby provide a more accurate height measurement. As shown in  FIG. 4A , the patient&#39;s torso is at an angle based on the upper portion of the bed being positioned at an angle. If the angle at which the patient is propped is not taken into account, the height of the patient  400  may be calculated as the sum of segments A and B shown in  FIG. 4A . However, such calculation would underestimate the height of the patient  400 . Accordingly, the method  300  outlined in  FIG. 3  may include an additional step in which prior to the calculation of the patient height, it is first determined if the patient or portion of the patient is positioned at an angle. Any suitable method for determining whether the patient or a portion of the patient is positioned at an angle may be used. In some embodiments, the bed on which the patient  400  is positioned may be communicatively coupled with the patient monitoring system such that any degree to which the bed is angled is communicated to the computing device  115  for consideration in subsequent calculations regarding patient height. In other embodiments, the depth sensing image is used to determine whether the patient is positioned at an angle. For example, if a torso of the patient  400  is positioned at an angle, the depth measurements to the patient&#39;s head will be much smaller than the depth measurements to the patient&#39;s legs. Such a discrepancy can serve as an initial indicator that the patient is not lying flat on his or her back. 
     When the system identifies the patient or a portion of the patient as being positioned at an angle, the calculation of the patient&#39;s height can be adjusted to take into account such an angle. Any suitable manner of calculating the patient&#39;s height as being the sum of segments A and B′ shown in  FIG. 4A  rather than the sum of segments A and B can be used. In some embodiments, such as embodiments where the angle Θ is known or can be calculated, basic trigonometry can be used to determine the length of B′. For example, B′ may be calculated as B/cos Θ when the value of both B and Θ can be obtained from the non-contact patent monitoring system. Pythagorean&#39;s theorem can also be used to determine B′ when B and the height the patient&#39;s head is away from the bed are known (both of which information may be obtained from the depth sensing camera and the depth sensing image). 
     Once the patient&#39;s height has been determined in step  320 , such as by using any of the methods described previously, the method  300  proceeds to step  330 , wherein a predictive body weight (PBW) is calculated based on the height determined in step  320 . Any known formula or correlation chart used to calculate PBW from height can be used to carry out step  330 . One exemplary formula is described in Moreault, O., Lacasse, Y., &amp; Bussières, J. S. (2017). Calculating ideal body weight: Keep it simple. Anesthesiology: The Journal of the American Society of Anesthesiologists, 127(1), 203-204. For example, a man&#39;s predicted body weight can be calculated based on measured height using the following formula: 
       Weight (kg)=50 kg+(0.91×[Patient Height in Centimeters−152.5])
 
     As noted previously, this calculation can be carried out automatically using the computing device  115 . In some embodiments, the computing device  115  is used to determine the patient height in step  320  as described previously, and therefore the computing device  115  immediately has possession of the height calculation for implementation into the PBW calculation in step  330 . 
     As alluded to previously, calculation of PBW may depend on the gender of the patient. For example, the formula provided previously provides a means for determining a PBW based on patient height for when the patient is a man. The formula for a woman is different, and therefore it may be beneficial for the methods described herein to further take into account the gender of the patient when calculating PBW. In some embodiments, the gender of the patient can be manually input into the system so that the appropriate formula is used when carrying out step  330 . However, some embodiments of the method may include an additional step wherein the gender of the patient is determined using the non-contact patient monitoring system. 
     Any suitable method for determining patient gender using non-contact patient monitoring systems can be used. In some embodiments, patient gender is determined by calculating a ratio of shoulder length (S) to waist length (W). With reference to  FIG. 5 , both the measurement S and W can be obtained using the depth sensing technology previously described herein. For example, the depth sensing camera can obtain a depth sensing image of a patient, within which the patient&#39;s shoulders and waist can be identified. For example, methods described previously for identifying various parts of the patient&#39;s body can be used to identify the patient&#39;s shoulders and waist. Once identified, the length of these body parts can be measured to calculate an S:W ratio. Once determined, the S:W ratio can be compared against previously tabulated data regarding typical S:W ratio values for men and women to thus determine a predicted gender of the patient. Once determined, subsequent calculation of the patient&#39;s PBW in step  330  is adjusted to use the appropriate formula for a man or a woman. Any other suitable body ratios or body segment lengths known to be useful in predicting patient gender can also be used. 
     In step  340 , the predictive body weight obtained in step  330  is used to calculate the patient&#39;s tidal volume. Any known formula or correlation chart used to calculate tidal volume from PBW can be used. In some embodiments, the tidal volume (in mL) is calculated as being 4 to 10 times the PBW of the patient in kilograms. In other words, the tidal volume used for the ventilator setting is set as being 4 to 10 mL per kilogram of the patient. As with step  330  discussed above, this calculation can be carried out automatically using the computing device  115 . In embodiments where the computing device  115  is used to automatically calculate the PBW from the patient height and gender determined automatically by the non-contact patient monitoring technology associated with the computing device  115 , the computing device  115  is also immediately automatically apply this value to the tidal volume calculation to immediately obtain the desired tidal volume setting for the ventilator. 
     In embodiments where the tidal volume is calculated as being 4 to 10 times the PBW of the patient in kilograms, it may be necessary to select the specific value between 4 and 10 that is used to carry out the tidal volume calculation. The specific value between 4 and 10 is often selected based on facility and/or clinician preference. Thus, in some embodiments, it may be possible for the preferred value to be entered into and stored in the computing device  115  such that the preferred value between 4 and 10 is automatically used when calculating tidal volume. In a scenario where different clinicians within the same facility and using the same equipment have a different preference for the value between 4 and 10 to be used when calculating tidal volume, it is possible for the unique preference of each clinician to be entered into and stored in the computing device  115  and for the computing device  115  to recognize and/or be told which clinician is treating the monitored patient such that each clinician&#39;s preferred value is automatically applied when calculating tidal volume. 
     Once the tidal volume calculation is determined in step  340 , a ventilator&#39;s tidal volume setting can be programmed for a specific patient based on the tidal volume calculated in step  340 . When the ventilator is communicatively associated with, for example, the computing device  115 , the calculated tidal volume value can be automatically and immediately sent to the ventilator for use in initial ventilator settings for the patient. 
     Method  300  described previously generally relates to measuring a patient&#39;s height and determining patient tidal volume from the measured patient height (via a PBW calculation that depends on the measured patient height). However, it should be appreciated that modifications to method  300  can be implemented such that the patient&#39;s height is calculated from measuring a segment of the patient&#39;s body, rather than directly measuring the patient&#39;s overall height. With reference to  FIG. 7 , once such modification involves measuring the length of a patient&#39;s ulna  701  and using the measured length of the patient&#39;s ulna  701  to calculate a predicted height of the patient  700 . Any known correlation between ulna length and overall patient length can be used. One example of a correlation between ulna length and patient height that can be used in the methods described herein is set forth in Barbosa, V. M., Stratton, R. J., Lafuente, E. &amp; Elia M. (2012). Ulna length to predict height in English and Portugese patient populations. European journal of clinical nutrition, 66 (2), 209. 
     It should be appreciated that a modification to method  300  as described previously is not limited to measuring ulna length and calculating patient height from ulna length. Any other body segment that has been correlated to overall body height can be used in this embodiment. Regardless of the body segment selected, the manner of identifying and measuring the selected body segment can be similar or identical to methods described previously with respect to identifying various parts of the patient in a depth sensing image and using non-contact patient monitoring systems. 
     Once the patient&#39;s predicted height is calculated from a patient&#39;s body segment, the method  300  can generally progress in the same manner as described previously (i.e., where PBW is calculated from the calculated patient height in step  330  and tidal volume is then calculated from the calculated PBW in step  340 ). 
     With reference to  FIG. 8 , other embodiments of the technology described herein may use non-contact patient monitoring technology to determine the total body volume  801  (V b ) of a patient  800  and then calculate a patient&#39;s tidal volume  802  from the determined total body volume  801  using previously known and established ratios (referred to as R v ) of total body volume to tidal volume (V t ) (where R v =V t /V b ). In such embodiments, a depth sensing camera is used to obtain a depth sensing image of the patient&#39;s entire body, and the total volume  801  of the patient&#39;s body is calculated using the depth sensing data from the depth sensing image. For example, in some embodiments, the total volume  801  of the patient&#39;s body can be obtained from the depth sensing image by integrating the depth measurements across the patient&#39;s body. In other embodiments, a patient&#39;s body is scanned prior to getting into bed to determine a patient total body volume  801 . Any other suitable method of obtaining patient total volume  801  can also be used. 
     Once a patient&#39;s total body volume  801  is determined using the depth sensing image or other techniques, the computing device  115  (as shown in  FIG. 2 ). can be used to automatically calculate patient tidal volume  802  by accessing standard and pre-established R v  values. For example, in some embodiments, R v  values are obtained from existing population studies. In some embodiments, the R v  value used for the patient  800  is selected based on other measured characteristics of the patient  800 . For example, the R v  value may depend on one or more patient demographics, such as the gender of the patient, the age of the patient, etc. As such, the determination of the patient tidal volume  802  using a measured total patient body volume  801  and an appropriate R v  value may further employ methods of determining certain patient characteristics and or demographics prior to selecting the appropriate R v  value to use in the calculation of patient tidal volume. 
     In some embodiments, the method further includes a step of determining the gender of the patient  800  prior to selecting the appropriate R v  value. Determination of patient gender may employ similar techniques as described herein previously, such as measuring a patient S:W ratio and making a determination of gender by comparing the calculated S:W ratio to preestablished S:W values associated with men or women. Once the gender is determined, an appropriate Revalue can be selected such that a more accurate tidal volume calculation can be carried out. 
     Determination of patient age can also be carried out using non-contact patient monitoring technology. In some embodiments, a rough approximation of age (e.g., infant, adolescent, adult) is all that is required to select an appropriate R v  value. In such embodiments, the non-contact patient monitoring technology is used to make a determination of patient age. For example, a depth sensing image captured from a depth sensing camera and which includes an overall outline of the patient body can be used to make determinations of patient age based on, e.g., the overall size of the patient outline and other ratios of body segments that denote whether a patient is an infant, adolescent or adult. 
     Information on patient gender, age, etc. can also be obtained by other means, such as manually inputting such data into the computing device  115  by a clinician. This data can then be accessed by the computing device  115  at the appropriate time for calculation of the patient tidal volume  802  in conjunction with the measured patient total body volume  801 . 
     From the foregoing, it will be appreciated that specific embodiments of the invention have been described herein for purposes of illustration, but that various modifications may be made without deviating from the scope of the invention. Accordingly, the invention is not limited except as by the appended claims.