Patent Publication Number: US-2023141862-A1

Title: Predictive system for elopement detection

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to and the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 63/277,241 filed on Nov. 9, 2021, entitled PREDICTIVE SYSTEM FOR FALL PREVENTION, the entire disclosure of which is hereby incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention generally relates to surveillance and monitoring systems, and more specifically, a patient monitoring system that can be used for predicting adverse events within a patient care setting. 
     BACKGROUND OF THE INVENTION 
     Within many hospital settings, video surveillance is used to monitor the position and status of a patient within the patient&#39;s bed. This is frequently used in conjunction with an offsite human observer that monitors the video feed. This type of patient monitoring typically includes the offsite observer either communicating with the patient or communicating with hospital staff about when an adverse event has occurred or is about to occur. This type of monitoring requires continuous surveillance by the offsite observer over several hours during the course of a particular observation shift. 
     SUMMARY OF THE INVENTION 
     According to an aspect of the present disclosure, a patient monitoring system includes a camera that selectively delivers a video feed to a monitoring station. A processor evaluates the video feed, and converts the video feed into a plurality of data points for an elopement detection system. The plurality of data points for the elopement detection system correspond at least to a combination of facial features and components of clothing for each person within the video feed. The processor is further configured to associate the combination of facial features and the components of clothing for each person to define a confirmed association, to identify, for each confirmed association, whether the person is a patient or a non-patient, to verify the confirmed association of the patient by comparing updated data points from the video feed with the confirmed association, and to activate an alert when the confirmed association of the patient is unverified based upon a comparison with the updated data points. 
     According to another aspect of the present disclosure, a method for operating an elopement detection system for a patient monitoring system includes the steps of activating the elopement detection system when a patient is outside of a predetermined boundary, analyzing buffered sections of a video feed to identify a combination of facial features and components of clothing for the patient outside of the predetermined boundary, associating the combination of facial features with the components of clothing for each person to define a confirmed association, verifying the confirmed association of the patient by comparing the combination of facial features with the components of clothing of the patient, and activating an alert if the confirmed association of the patient is unverified based upon changes in the combination of facial features or the components of clothing of the confirmed association. 
     According to yet another aspect of the present disclosure, a method for detecting an elopement condition includes the steps of receiving user input to enable image processing, where the user input is related to a monitored patient exiting a predetermined boundary, generating a new video stream handling a request related to the user input, presenting the new video stream to an observer client for heightened monitoring of the monitored patient, processing the new video stream using a real-time streaming protocol to compare the new video stream to historical data related to past elopement events, adjusting a monitoring device to track the monitored patient within a patient care setting, and activating an elopement alert when an elopement indicator is determined. The elopement indicator includes confirmation that the monitored patient is outside of a video feed and is determined to be outside of the patient care setting. 
     These and other features, advantages, and objects of the present invention will be further understood and appreciated by those skilled in the art by reference to the following specification, claims, and appended drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the drawings: 
         FIG.  1    is a schematic diagram illustrating a patient care setting with a mobile monitoring unit positioned to allow for observation of a patient; 
         FIG.  2    is a schematic diagram illustrating the patient monitoring system for use in observing events that take place within the patient care setting; 
         FIG.  3    is a schematic diagram illustrating use of the mobile monitoring unit via a display screen to allow for teleconferencing and other forms of telecommunication; 
         FIG.  4    is a schematic diagram illustrating a method for utilizing a pose estimation network and a relative depth network for monitoring a patient; 
         FIG.  5    is a schematic diagram illustrating a method for monitoring a patient utilizing another aspect of the pose estimation network and the relative depth network; 
         FIG.  6    is a schematic diagram illustrating a method for monitoring the position of a patient within a care setting utilizing a plurality of estimation networks in combination to determine whether an adverse event has occurred; 
         FIG.  7    is a schematic diagram illustrating a method for monitoring the position of a patient utilizing an image classifier for determining whether an adverse event has occurred; 
         FIG.  8    is a schematic diagram illustrating a multi-tiered process for monitoring a patient and also activating a video analysis network for determining whether an adverse event has occurred; 
         FIG.  9    is a schematic diagram illustrating a method for utilizing multiple estimation networks in combination with previously determined data points for predicting when an adverse event is likely to occur; 
         FIG.  10    is a schematic diagram illustrating a process for monitoring a patient and predicting when an adverse event is likely; 
         FIG.  11    shows a block diagram of the patient monitoring system according to one aspect of the present disclosure; 
         FIG.  12    is a schematic diagram of as aspect of the patient monitoring system; 
         FIG.  13    is a schematic diagram of an aspect of the patient monitoring system that uses tracking features of occupants of a patient care space to track movements of the occupants; 
         FIG.  14    is a schematic diagram of an aspect of the patient monitoring system that incorporates an elopement detection feature; 
         FIG.  15    is a schematic flow diagram illustrating a method for operating an elopement detection feature of the patient monitoring system; 
         FIG.  16    is a schematic diagram of an aspect of the elopement detection system; and 
         FIG.  17    is a linear flow diagram illustrating a method for operating an elopement detection system. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     Reference will now be made in detail to the present preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numerals will be used throughout the drawings to refer to the same or like parts. In the drawings, the depicted structural elements are not to scale and certain components are enlarged relative to the other components for purposes of emphasis and understanding. 
     As required, detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. The figures are not necessarily to a detailed design; some schematics may be exaggerated or minimized to show function overview. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention. 
     For purposes of description herein, the terms “upper,” “lower,” “right,” “left,” “rear,” “front,” “vertical,” “horizontal,” and derivatives thereof shall relate to the concepts as oriented in  FIG.  1   . However, it is to be understood that the concepts may assume various alternative orientations, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise. 
     The present illustrated embodiments reside primarily in combinations of method steps and apparatus components related to a patient monitoring system that converts buffered video to a plurality of data points and utilizes these data points to assess the current position of a patient in a care space as well as the likelihood of potential future adverse events involving the patient. Accordingly, the apparatus components and method steps have been represented, where appropriate, by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Further, like numerals in the description and drawings represent like elements. 
     As used herein, the term “and/or,” when used in a list of two or more items, means that any one of the listed items can be employed by itself, or any combination of two or more of the listed items, can be employed. For example, if a composition is described as containing components A, B, and/or C, the composition can contain A alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination. 
     In this document, relational terms, such as first and second, top and bottom, and the like, are used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “comprises . . . a” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element. 
     As used herein, the term “about” means that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and/or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art. When the term “about” is used in describing a value or an end-point of a range, the disclosure should be understood to include the specific value or end-point referred to. Whether or not a numerical value or end-point of a range in the specification recites “about,” the numerical value or end-point of a range is intended to include two embodiments: one modified by “about,” and one not modified by “about.” It will be further understood that the end-points of each of the ranges are significant both in relation to the other end-point, and independently of the other end-point. 
     The terms “substantial,” “substantially,” and variations thereof as used herein are intended to note that a described feature is equal or approximately equal to a value or description. For example, a “substantially planar” surface is intended to denote a surface that is planar or approximately planar. Moreover, “substantially” is intended to denote that two values are equal or approximately equal. In some embodiments, “substantially” may denote values within about 10% of each other, such as within about 5% of each other, or within about 2% of each other. 
     As used herein the terms “the,” “a,” or “an,” mean “at least one,” and should not be limited to “only one” unless explicitly indicated to the contrary. Thus, for example, reference to “a component” includes embodiments having two or more such components unless the context clearly indicates otherwise. 
     Referring now to  FIGS.  1 - 16   , reference numeral  10  generally refers to a patient monitoring system that is positioned within a care setting  12  for monitoring the position, location, and status of a patient  14  within the care setting  12 . The patient monitoring system  10  is usually placed within a patient&#39;s room or other similar care setting  12  for conducting audio and video surveillance of a patient  14 . This type of surveillance is typically conducted within a care setting  12  where the patient  14  is a fall risk if the patient  14  gets out of bed  78 . This surveillance can also be conducted where the patient  14  is a risk for removing or improperly adjusting medical equipment, at risk for elopement, as well as to monitor interactions between the patient  14  and other individuals within a care setting  12 , and for other patient-care purposes. The patient monitoring system  10  can include a monitoring unit  16  that can be in the form of a stationary monitoring unit  16  that is attached to a wall  18  or ceiling  20  of a care setting  12  of the patient  14 . The monitoring unit  16  can also be a mobile monitoring unit  16  that can be moved from one care setting  12  to another, as needed for monitoring patients  14  that may be at a higher risk for adverse events  22 . As part of the stationary and mobile monitoring units  16 , the video components, such as camera  40 , typically include pan, tilt, and zoom (PTZ) tracking operability to more completely monitor the patient care setting  12 . 
     Referring again to  FIGS.  1 - 16   , the patient monitoring system  10  is utilized for monitoring the occurrence of adverse events  22  and also for predicting adverse events  22 . The patient monitoring system  10  includes the camera  40  that selectively delivers a video feed  42  to a monitoring station  44 , where the video feed  42  is delivered as buffered sections  46  of video. The patient monitoring system  10  also includes a processor  48  that evaluates the buffered sections  46  of video. The processor  48  converts each buffered section  46  of video into a plurality of data points  50 . The plurality of data points  50  typically correspond at least to positions of various features contained within the buffered section  46  of video. The processor  48  is further configured to store the plurality of data points  50  within a memory  52 , analyze the plurality of data points  50  within a plurality of estimation networks  54  and deliver the video feed  42  to the monitoring station  44  at least when the plurality of estimation networks  54  determine that an adverse event  22  is likely. 
     The use of buffered sections  46  of video for delivering a particular video feed  42  is utilized to minimize interruptions in the observed video feed  42 . These buffered sections  46  of video can be represented by a particular number of frames of the video feed  42  that are sent as a batch to a particular monitoring station  44 . These buffered sections  46  of video are delivered sequentially and arranged at the monitoring station  44  so that they appear seamless to the offsite observer  24 . When the buffered sections  46  of video are generated, these buffered sections  46  of video, before reaching the monitoring station  44 , are evaluated by the processor  48 . This processor  48 , as described herein, converts each buffered section  46  of video into a plurality of data points  50 . This conversion of the buffered section  46  of video into data points  50  can occur as a separate step after the video feed  42  is divided into the buffered sections  46  of video. Alternatively, the evaluation of the buffered sections  46  of video can occur as the video feed  42  is segmented into buffered sections  46  for delivery to the monitoring station  44 . During this evaluation phase of the video feed  42 , the buffered sections  46  of video are converted into the plurality of data points  50 . In addition, as will be described more fully below, a single buffered section  46  of video can be analyzed using more than one estimation network  54  to generate multiple layers  180  of data points  50  that can be used cooperatively for determining the occurrence of or likelihood of an adverse event  22 . 
     The various estimation networks  54  can also be used independently on one another, then compared for verification purposes. In addition, in the event that the system finds that an adverse event has occurred or is likely to occur, the estimation networks  54  or a separate estimation network  54  can be used to verify or confirm that the adverse event  22  actually occurred, was prevented, was imminent, did not occur or was determined to be a false positive alert. This verification system can be used to improve and train the various estimation networks  54  and improve the accuracy with respect to future events. 
     The data points  50  that are generated through the estimation networks  54  are typically in the form of universal data points  50  that are common to most patients  14  and patient care settings  12 . As will be described in greater detail herein, the use of these plurality of data points  50  can be utilized when comparing a particular set of data points  50  to previously captured sets of data points  50 . This is to prevent the data points  50  from being used to derive any identifying personal characteristics or other personal information regarding the patient  14 . Also, the use of generic and universally applicable data points  50  allows for comparison of multiple sets of data points  50  for predictive comparison, as will be described more fully herein. 
     The captured set of data points  50  can correspond to various features and positions of features within the view of the video feed  42 . By way of example, and not limitation, the plurality of data points  50  can correspond to a patient&#39;s shoulders  70 , hands  72 , feet  74 , head  76 , eyes, and other similar data points  50  that are shared by most individuals. The plurality of data points  50  can also include features contained within the care setting  12 . Such features can include, but are not limited to, the boundaries of the bed  78  of the patient  14 , the floor  80  of the care setting  12 , the walls  18  of the care setting  12  and other similar stationary or generally non-moving features positioned within the care setting  12 . In generating these data points  50 , identifying characteristics found within the buffered sections  46  of video are eliminated such that no identifying characteristics of the patient  14  can be ascertained through the plurality of data points  50 . Additionally, the locations of certain features are converted into the plurality of data points  50 . These data points  50  are recorded and placed within a three-dimensional space and positioned relative to one another. Accordingly, while a particular section of the data points  50  may include a patient&#39;s head  76 , information such as hair color, eye color, the shape of most facial features  310 , and other identifying characteristics are not ascertainable from the data points  50 . In certain aspects of the device, the resulting plurality of data points  50  can be represented as one or more wire frames that are positioned relative to one another. One such wire frame can be in the general form of a human figure, while other wire frames can provide information relating to the position of the bed  78  of the patient  14 , and other features within the care setting  12 . 
     In addition to converting the video feed  42  to the plurality of data points  50 , certain data points  50  are extrapolated from the captured data points  50 . By way of example and not limitation, the processor  48  can extrapolate the approximate location of the spine  90  of the patient  14  based upon the locations of the patient&#39;s shoulders  70  and head  76 . As will be described more fully herein, one or more of the estimation networks  54  can utilize the position of the patient&#39;s spine  90  for determining the relative position of a patient  14  with respect to the bed  78 , the floor  80 , and/or other portions of the care setting  12 . 
     As described herein, the buffered sections  46  of video are not recorded or saved at any stage of the process for the patient monitoring system  10 . Rather, the plurality of data points  50  are stored within the memory  52  for further analysis and later storage. These plurality of data points  50  that relate to a particular section of buffered video are stored within an onsite memory, an off-site memory, a cloud-based memory and other similar memory configurations. The buffered sections  46  of video are selectively delivered to the monitoring station  44  for view by the offsite observer  24 . To protect the privacy of the patient  14 , these buffered sections  46  of video are not saved. 
     Because each buffered section  46  of video includes a plurality of video frames, each buffered section  46  of video includes a certain amount of motion with respect to the various data points  50 . Accordingly, as the processor  48  converts the various buffered section  46  of video into the plurality of data points  50 , certain data points  50  may be converted into a range of motion or a vector with respect to a particular feature contained within the buffered section  46  of video. By way of example, and not limitation, if a buffered section  46  of video includes an individual moving from a prone position flat on the patient&#39;s back to a position on the patient&#39;s side, the buffered section  46  of video may be converted to a set of data points  50  that includes a vector with respect to one shoulder  70  moving from a beginning position to an ending position through the course of the various frames of the buffered section  46  of video. This motion of the particular data point  50  through the buffered section  46  of video can be converted into a vector having a particular motion value, such as acceleration or velocity. These data points  50  and vectors, over the course of multiple buffered sections  46  of video can be used to further define these vectors of motion with respect to the data points  50 . 
     After the buffered sections  46  of video are converted into the plurality of data points  50 , the processor  48  analyzes these plurality of data points  50  within a plurality of estimation networks  54 . These estimation networks  54  can include various methods of evaluating the locations of various data points  50  with respect to other features within the care setting  12  and also evaluating the motion of various data points  50  over time. The various estimation networks  54  which will be described more fully herein can include, but are not limited to, a pose estimation network  100 , an object detection network  102 , a relative depth network  104 , a field segmentation network  106  and a movement segmentation network  108 . These various estimation networks  54  can operate independently or in combination with one another to derive and evaluate the various sets of data points  50 . 
     Utilizing the various estimation networks  54 , as described in  FIGS.  4 - 8   , the processor  48  can analyze the data points  50  to determine a particular location of a patient  14 , or a portion of the body of the patient  14  within a care setting  12 . This analysis of the data points  50  can alert hospital staff to an adverse event  22 , such as a fall, without recording or saving any video of the patient  14  or the care setting  12 . The process of evaluating, converting and analyzing the buffered sections  46  of video and the plurality of data points  50  is accomplished as the buffered sections  46  of video are delivered to the monitoring station  44  where an offsite observer  24  can review the video feed  42 . In this configuration, the estimation networks  54  can be used to alert hospital staff with respect to the current position of the patient  14  as well as the occurrence of any adverse events  22 . As will be described more fully herein, these estimation networks  54 , in combination with stored sets  120  of data points  50 , can also be used as a predictive tool for analyzing when an adverse event  22  is likely to occur in the future. 
     According to various aspects of the device, as exemplified in  FIGS.  4 - 10   , the patient monitoring system  10  can also operate the processor  48  to compare the plurality of data points  50  that are converted from the buffered sections  46  of video against previously stored sets  120  of data points  50 . These previously stored sets  120  of data points  50  can be from the same patient  14  or from any one of various patients  14  that may have been monitored using the patient monitoring system  10  previously. Accordingly, the data points  50  obtained from any previous use of the patient monitoring system  10 , from any patient  14 , and from anywhere in the world, are generic data points  50  that do not contain any identifying information. The information related to the data points  50  also cannot be used to extrapolate the identity of the patient  14 . While certain information related to the locations of features are analyzed, this information is used to derive a data point  50  in relation to a portion of the body of the patient  14 . By way of example, and not limitation, the pose estimation network  100  may include some facial recognition features that can be used to determine the location of the eyes, nose, and mouth of a patient  14 . However, this information may only be used to determine the location and orientation of the head  76  of the patient  14 . The output of the pose estimation network  100  using these facial features  310  is typically limited to the location and orientation of the head  76  of the patient  14 . 
     The object detection network  102  can also be used to determine what types of clothing the patient  14  is wearing and also the types of medical devices that are attached to the patient  14 . Using this data, the processor  48  can identify certain additional reference points that may be useful in deriving the data points  50 . As a non-limiting example, the pose estimation network  100  may determine that the patient  14  is wearing a gown. In this instance, the video feed  42  will have very limited information, if any, regarding the body of the patient  14  between the shoulders  70  and feet  74  of the patient  14 . With this recognition, the processor  48  may provide more emphasis or devote more resources toward one or more of the other estimation networks  54 . 
     As discussed herein, no distinguishable or ascertainable characteristics with respect to any patient  14  are recorded with the buffered section  46  of video into the data points  50 . Accordingly, the stored sets  120  of data points  50  are recorded as generic wire frame movements with respect to a generic human form. Accordingly, when the processor  48  converts the buffered section  46  of video into the plurality of data points  50 , these data points  50  are compared against numerous sets of previously stored data points  50  to determine whether the data points  50  captured from the buffered section  46  of video correspond to a particular chain of events that has occurred previously and which led to the occurrence of an adverse event  22 . 
     It is also contemplated that the processor  48  can compare the plurality of data points  50  from the buffered section  46  of video against the plurality of data points  50  converted from an immediately previous buffered section  46  of video. Accordingly, the processor  48  utilizes these two sets of data points  50  to determine a plurality of corresponding vectors that reflect a difference between sequential sets of data points  50 . These corresponding vectors can be used to determine if a particular data point  50  has moved as well as a velocity and acceleration of that data point  50  over time. By way of example, and not limitation, certain movements may be indicative of a fall, such as a quick downward movement toward the floor  80 . Also, a quick upward movement of a particular data point  50  may be indicative of the patient  14  getting out of bed  78  or a patient  14  moving toward the edge of the bed  78 . As discussed herein, these various corresponding vectors and data points  50  can be compared with one another and also compared against previously stored sets  120  of data points  50  to determine the status of the individual as well as conducting a comparative analysis of previously stored events that led to an adverse event  22 . 
     The processor  48  can also analyze the plurality of data points  50  and the plurality of corresponding vectors within the plurality of estimation networks  54 . In conducting this analysis, the processor  48  can be configured to determine whether the plurality of data points  50  are indicative of the patient  14  moving toward the edge of the bed  78  or attempting to get out of the bed  78 . In this instance, the processor  48  can determine that the patient  14  should be monitored and can provide instruction that the video feed  42  should be delivered to the monitoring station  44  for surveillance and potential intervention. Using this configuration, only those video feeds  42  that contain data points  50  that are indicative of the patient  14  getting out of their bed  78  are delivered to a monitoring station  44  for review. Accordingly, the time and effort of the offsite observer  24  in monitoring various video feeds  42  can be focused on those situations that require our attention, rather than video feeds  42  that show no imminent danger of an adverse event  22 . 
     Referring now to  FIGS.  4 - 8   , which reference various implementations and embodiments of the estimation networks  54 , it is contemplated that an initial step of the usage of the estimation networks  54  occurs in determining whether a patient  14  has left the bed  78  within their care facility. In a certain aspect of the device, exemplified in particular within a portion of  FIG.  8   , the patient monitoring system  10  utilizes the pose estimation network  100  and the object detection network  102  for determining the position of the patient  14  within their bed  78 . The pose estimation network  100  can include an analysis of various data points  50  of the patient  14 . These data points  50  can include the shoulders  70 , and various portions of the head  76 , such as the top of the head  76  and eyes. As discussed herein, the pose estimation network  100  can utilize these data points  50  for estimating the location of the spine  90  for the individual. Typically, the spine  90  will be located between the shoulders  70  and below the top of the head  76 , and generally between the eyes. Other data points  50  relating to the patient&#39;s body can also be recorded, such as the patient&#39;s feet  74 , hands  72 , and other visible portions of the patient&#39;s body. Utilizing the pose estimation network  100 , the orientation of the patient&#39;s body within the bed  78  can be ascertained. 
     In addition, the patient monitoring system  10  utilizes the object detection network  102  for defining a boundary line  130  that typically extends around the body of the patient  14  and a boundary box  132  that is positioned relative to a bed  78  of the patient  14 . Utilizing the pose estimation network  100  and the object detection network  102 , the processor  48  for the patient monitoring system  10  can derive the orientation and position of the body of the patient  14 , using only the plurality of data points  50 , with respect to the designated boundary line  130  surrounding the patient  14 . When the boundary line  130  surrounding the patient  14  approaches or crosses the boundary box  132  of the bed  78 , this can be an indication that the patient  14  is attempting an exit of the bed  78 . This boundary box  132  can be in the form of the outer edges of the patient&#39;s bed  78 , and the boundary line  130  can be a perimeter that surrounds the patient&#39;s body. Utilizing the pose estimation network  100  and the object detection network  102 , the patient monitoring system  10  can ascertain events where the patient  14  appears to be attempting to get out of their bed  78 . At this point, the processor  48  can notify the patient monitoring system  10  to activate additional processing capabilities and additional estimation networks  54  for monitoring the patient  14  in the care space. 
     As exemplified in  FIGS.  4  and  5   , after the pose estimation network  100  and the object detection network  102  have determined that the patient  14  is attempting to get out of their bed  78  or has left their bed  78 , the pose estimation network  100  can be used to monitor the position of the spine  90  with respect to the floor  80  or other horizontal plane or vertical plane. Accordingly, the angle of the patient&#39;s spine  90  can be determined through the analysis of the various data points  50 . In addition, the patient monitoring system  10  can utilize a relative depth network  104  that monitors the position of the head  76  or other similar body part of the patient  14  in space. 
     Referring again to  FIGS.  4  and  5   , the relative depth network  104  can determine the position of the head  76  of the patient  14  in space through various analysis mechanisms. One such mechanism is through an analysis of the pixels of the buffered section  46  of video. A distance of the patient&#39;s head  76  from the camera  40  for the patient monitoring system  10  can be determined through an analysis of the individual pixels. This can be done through an analysis of the distance between various data points  50 . In one non-limiting example, these data points  50  can be compared at a particular point in time in relation to the distance between the same data points  50  when the patient  14  was in bed  78  or other similar range of time. Alternatively, or additionally, the relative depth network  104  can utilize the relative distance between the head  76  of the patient  14  and the floor  80  through an assessment of the pixels of the various data points  50  or pixels of the buffered section  46  of video. Using the relative position of the spine  90  of the patient  14  through the pose estimation network  100 , in combination with the relative position of the head  76  of the patient  14  in space from the relative depth network  104 , it can be determined whether a patient  14  is moving toward the floor  80 , has fallen or is in the process of falling. These networks can also be utilized for determining whether a patient  14  has stood up next to their bed  78 , or is attempting to walk through the care space when advised not to do so. 
     As exemplified in  FIGS.  4  and  5   , the use of the pose estimation network  100  and the relative depth network  104 , as described herein, can be used to ascertain a relative position of the head  76  with respect to the floor  80 , as well as a velocity or acceleration of the head  76  of the patient  14  towards the floor  80 . Where this velocity is an accelerating pattern towards the floor  80 , this can be indicative of a where the patient  14  is falling. Conversely, where the velocity is a consistent velocity or a slower velocity, this may be indicative of the patient  14  bending down to accomplish some task, such as tying a shoe. Using these networks, the processor  48  can evaluate the orientation of the individual&#39;s head  76  and position, in space, of the patient&#39;s head  76 . When the patient&#39;s head  76  falls below approximately three feet, or other threshold, this can be indicative of an adverse event  22 , such as a fall. 
     In certain aspects of the device, the object detection network  102  can create a bounding line that is in the form of an outline of the patient&#39;s body or general outline of the data points  50  that relate to the patient&#39;s body. In addition, as described herein, this bounding line can be defined as a particular space between the patient&#39;s body and an outer edge of the bed  78  that can be utilized for determining whether the patient  14  is attempting to get up or otherwise exit their bed  78 . Accordingly, the object detection network  102  can be utilized for ascertaining whether the patient&#39;s body is in a horizontal position a vertical position, or somewhere in between. 
     Referring now to  FIG.  6   , after the pose estimation network  100  and the object detection network  102  has determined that the patient  14  is attempting to leave the bed  78  or has left the bed  78 , the pose estimation network  100  and the object detection network  102  can be utilized for determining the size and relative position of the bounding line that surrounds the patient  14 . Accordingly, these networks can be used to determine whether the bounding line surrounding the patient  14  is getting smaller, or is getting more vertically oriented within the space. In addition, the processor  48  can utilize these data points  50  to determine whether the bounding line is transitioning from a more vertical position or a small size toward a more horizontal position or a larger size. Changes such as these can be indicative of the patient  14  falling. The size of this bounding line can be utilized for determining an approximate velocity that the patient  14  is moving toward the floor  80 , in the form of a relative velocity. In addition, the pose estimation network  100  can contemporaneously monitor the position of the data points  50  relative to the spine  90  of the patient  14 , as a confirmation with respect to the possible occurrence of an adverse event  22 . 
     Accordingly, the various estimation networks  54  included within the patient monitoring system  10  are utilized to not only verify the position and location of the patient  14  within the care space, but also confirm or reconfirm the findings of any one or more of the other estimation networks  54  with respect to the occurrence of an adverse event  22  or the absence thereof. Utilizing these systems as a predictive tool, the various estimation networks  54  serve to gather the data points  50  and numerous evaluations of the data points  50  for building a historical catalog  140  or library of previously stored sets  120  of data points  50 . This historical catalog  140  or library can be used as a comparative tool for analyzing data points  50  gathered from a particular buffered section  46  of video. This comparison can include comparing the various data points  50  with historical data, in the form of the previously stored sets  120  of data points  50 , to determine whether current data matches trends or patterns within historical data for determining whether an adverse event  22  is likely to occur in the future. 
     By way of example and not limitation, historical data related to previously stored sets  120  of data points  50  can be categorized based upon the occurrence of confirmed adverse events  22 , false positive adverse events  22  and false negative findings that no adverse event  22  has occurred. Using the plurality of data points  50  from the buffered sections  46  of video, an analysis of current data points  50  can be compared with the catalogs  140  of previously stored sets  120  of data points  50  for comparing against sequences, patterns, routines and other similarities that may be contained within the historical sets of data points  50 . 
     Where a particular set of data points  50  captured from a buffered section  46  of video matches one or more previously stored sets  120  of data points  50 , the video stream can be forwarded onto a monitoring station  44  so that an offsite observer  24  can begin surveillance of an individual that may, or is likely to, be moving toward an adverse event  22 . In situations where the adverse event  22  is likely to occur or does occur, this data can be added to the historical catalog  140  for adjusting the parameters of each pattern, sequence or routine. Additionally, where the data points  50  from the buffered section  46  of video are contrary to the historical data, this can also be added to the historical catalog  140  to be used as a possible exception to a particular pattern or to refine the pattern to provide better predictive analysis in the future. 
     In certain aspects of the device, the various data points  50 , including the derived vectors and other derived data can be recorded as a particular code or symbol. This code can be assigned to reflect at least a particular position, orientation, posture and demeanor of a patient  14 . In such an aspect of the device, rather than recording the various data points  50 , only the resulting code is recorded. This code can be derived from a single estimation network  54  or can be derived through a conglomeration of multiple estimation networks  54 . 
     According to various aspects of the device, as exemplified in  FIGS.  1 - 11   , the buffered sections  46  of video can include a video stream as well as an audio stream  150 . It is contemplated that each of the video stream and the audio stream  150  of the buffered section  46  of video can be separately converted into a plurality of visual data points  152  as well as a plurality of auditory data points  154  that make up the entire plurality of data points  50  for the particular buffered section  46  of video. Utilizing the auditory data points  154 , the process can utilize verbal communication  156  that may take place within a particular care setting  12  for ascertaining the occurrence of an adverse event  22 . In various aspects of the device, from an auditory device, a verbalization of distress or imminent danger, irritation or aggravation, and other similar emotions that can be expressed through auditory means. These auditory data points  154  can be gathered for purposes of determining an adverse event  22  that can include, but may not be limited to, verbal abuse by or toward the patient  14  or related someone else in the care space. The auditory data points  154  can be representations of information that can correspond to particular words, changes in volume of speech, changes in tenor of speech, screams, extreme coughing or trouble breathing, and other similar auditory signals. With respect to specific words or phrases, the auditory data points  154  can be in the form of symbols or code, as well as a textual or coded transcription of the observed word or phrase. As with the data points  50  that relate to more visual cues, these auditory data points  154  can be used for activating the delivery of the video feed  42  of the patient monitoring system  10  to the monitoring station  44  so that the patient  14  can be more closely observed by the offsite observer  24 . The components of the buffered sections  46  of video that include the audio streams  150  can be analyzed through one or more estimation networks  54 . These estimation networks  54  can operate to analyze both video and audio. In certain instances, one or more estimation networks  54  can be dedicated to the analysis of the audio streams  150 . These estimation networks  54  can include a natural language processing network that can be used to evaluate vocabulary, syntax, parts of speech and other similar components of the audio stream  150 . The estimation networks  54  can also include a voice activity detection network that can be used to monitor the frequency, amplitude, volume, tone and other components of the audio streams  150 . As described, herein, the various estimation networks  54  can operate independently and can also be used to confirm or verify the observations of the other estimation networks  54 . 
     In certain instances, the visual portion of the video feed  42  may not be available or may be required to be turned off. In such instances, such as personal care time for a patient  14 , bathing, and other similar highly sensitive events. During these events, the video feed  42  may be switched off or at least “fuzzed out” in the area surrounding the patient  14 . In these instances, the auditory data points  154  can be more closely analyzed and scrutinized for various events that may be occurring within the care space. In particular, it is during these moments of sensitive care that dialog between the patient  14  and a caregiver is likely to occur. This dialog can turn abusive in certain conditions and situations. The use of the auditory data points  154  can be useful in monitoring the interactions between the patient  14  and the caregiver in determining whether an event is likely to occur. 
     The visual data points  152  can be used to monitor the emotions and condition of the patient  14  and others in the care space. Facial recognition techniques can be used to derive a data point  50  or other code that can be indicative of a particular emotion or reaction. Using this information, the occurrence or absence of an adverse event  22  can be assessed, confirmed or verified in combination with one or more of the other estimation networks  54 . 
     According to various aspects of the device, the data points  50  that are converted from the buffered sections  46  of video contain no personal information relating to the particular patient  14 . Accordingly, use of the patient monitoring system  10  does not involve entry, recording or other capturing of any identifying patient data. All of the data utilized by the patient monitoring system  10  is through conversion of the buffered sections  46  of video into the data points  50 . It is then these data points  50  that are utilized for monitoring the condition, position, and relative status of the patient  14  within the care space. While it is typical for the hospital to record and maintain records related to the patient identity and health-related information, this information is maintained in an entirely separate file or database apart from the patient monitoring system  10 . 
     Referring now to  FIG.  7   , after the patient monitoring system  10  recognizes that the patient  14  has left the bed  78  or is attempting to leave the bed  78 , using the pose estimation network  100  and the object detection network  102 , the processor  48  activates a secondary layer of recognition system for monitoring the status and position of the patient  14  within the care space. In certain aspects of the device, the patient monitoring system  10  can utilize a field segmentation network  106  and a movement segmentation network  108 , that operate in combination to assess the position and status of the patient  14  within the care space. The field segmentation network  106  and the movement segmentation network  108  operate as an image classifier to separate those portions of the buffered sections  46  of video into stationary objects  170  of the care space (floor  80 , the walls  18 , the bed  78 , and other static objects) from those objects that are moving within the care space (the patient  14 , their clothing, medical devices coupled with the patient  14 , and others). 
     By way of example, and not limitation, where the patient monitoring system  10  utilizes a mobile monitoring unit  16 , the camera  40  for the mobile monitoring unit  16  can capture images of the care space. These images, as discussed herein, are separated into the buffered sections  46  of video. Within each of the buffered sections  46  of video, the field segmentation network  106  identifies those portions of the care space that can be referred to as the field or background of the care space indicative of stationary objects  170 . These static portions of the care space are not generally movable or are infrequently moved. Contemporaneously, the movement segmentation network  108  identifies those portions within the care space that are moving. These portions of the space can include, but are not limited to, the patient  14  and their clothing and visible body parts, portions of the bed  78  (covers, pillows, rails), medical devices such as tubes, sensors and the associated wires that may be attached to the bed  78  and/or the patient  14 . The field segmentation network  106  and the movement segmentation network  108  cooperate to identify various data points  50  within the buffered sections  46  of video that can be used to determine the relative location of the patient  14  within the care space as well as the relative distances between the patient  14  and portions of the care space. The proximity of the various data points  50  determined by the field segmentation network  106  and the movement segmentation network  108  can determine whether a patient  14  is vertical, horizontal, moving through the care space, or other similar information. Where the movable objects analyzed by the movement segmentation network  108  overlap of cover the stationary objects  170  analyzed by the field segmentation network  106 , the processor  48  monitors the movement of the various objects in the care space, including the patient  14 . This information is also compared with the data points  50  derived through the other estimation networks  54 . As discussed herein, the field segmentation network  106  and the movement segmentation network  108  are typically operated in combination with the other estimation networks  54  to provide confirmation of the indications provided by the various data points  50  of the other estimation networks  54 . 
     Referring now to  FIGS.  8  and  9   , the patient monitoring system  10  can be utilized as a tool for monitoring the current status of the patient  14  within the care space. Accordingly, the patient monitoring system  10  and the various estimation networks  54  can be utilized in a cooperative manner to observe events within the care setting  12  to determine whether an adverse event  22  has occurred. The estimation networks  54  cooperate to form a rapid response mechanism that can be used to detect the occurrence of an adverse event  22  at its most initial stages. The multiple estimation networks  54  operate to verify the observations of the other estimation networks  54  to minimize the occurrence of an adverse event  22 , the occurrence of a false reading or the incorrect absence of a detection related to an adverse event  22 . In addition, the patient monitoring system  10  can be used as a predictive tool for monitoring the patient  14  and determining when an adverse event  22  is likely to happen in the future. As discussed herein, an initial step in the use of the patient monitoring system  10  is determining when the patient  14  is out of bed  78  or attempting to get out of bed  78 . Use of the pose estimation network  100  for monitoring the locations of various generic data points  50  of the patient&#39;s body help to determine the orientation and position of the patient&#39;s body within the care space. This pose estimation network  100 , in combination with the object detection network  102 , can be used for providing multiple sets of data points  50  that can be used in combination to determine the position of the patient&#39;s body with respect to the bed  78  and other objects within the care space. Using these estimation networks  54  in combination, it can be determined when the patient  14  is out of bed  78  or attempting to get out of bed  78 . Once it is determined that the patient  14  is at least attempting to get out of bed  78 , the patient monitoring system  10  can activate additional estimation networks  54 . These additional estimation networks  54  provide additional layers  180  of data points  50  that can be used in conjunction with one another for determining whether a patient  14  is undertaking some innocuous action, such as going to the restroom or picking up an object from the floor  80 , or whether the patient  14  is in some form of distress or is taking any action that may be unsafe for the patient  14 . In certain instances, any actions by the patient  14  in getting out of bed  78  may be unauthorized, such as where the patient  14  is determined to be a fall risk due to lack of balance, disorientation or other conditions. 
     As the processor  48  operates the various estimation networks  54 , these layers  180  of data points  50  are compared with one another to determine the position of the patient  14  within the care space, and also determine the relative positions and distances of the patient  14  to objects within the care space. This information is used for determining whether the patient  14  is about to fall, has fallen or is in little to no danger of an adverse event  22 . 
     During operation of the patient monitoring system  10 , after it has been determined that the patient  14  is attempting to get out of bed  78 , the processor  48  utilizes the pose estimation network  100  for determining various generic data points  50  of a patient&#39;s body. As discussed herein, these generic data points  50  are utilized for extrapolating the position of the spine  90  of the patient  14 . When the relative location of the spine  90  is determined, the processor  48  can utilize the pose estimation network  100  for determining the angle of the spine  90  as well as changes in the position or angle of the spine  90  over time. Contemporaneously, the processor  48  utilizes the relative depth network  104  for determining and locating various data points  50  related to the movement and velocity of movement of the body parts of the patient  14  within the care space. In particular, the relative depth network  104  can be used to determine the velocity of the patient&#39;s head  76  through the care space as well as the relative velocity with respect to various objects within the care space, such as the floor  80 . Also, the processor  48  utilizes the objects detection network for calculating data points  50  related to a boundary line  130  that encircles the patient  14 . As discussed herein, the shape of this boundary line  130  can indicate whether the patient  14  is in a vertical position, a horizontal position or moving between a horizontal and vertical position or vice versa. Similarly, the field segmentation network  106  and the movement segmentation network  108  operate to determine data points  50  that correspond to the relative positions of the patient  14  and static features of the care space. 
     Utilizing these various estimation networks  54 , a finding of a single estimation network  54  that a patient  14  has experienced an adverse event  22  or is about to experience an adverse event  22  can be confirmed through the use of the other cooperative estimation networks  54 . The patient monitoring system  10  can be calibrated such that the finding of an adverse event can activate the processor  48  to deliver the video feed  42  to the monitoring station  44  for closer observation of the patient  14 . In certain aspects, it is contemplated that the patient monitoring system  10  such that at least two estimation networks  54  must indicate the existence or probability of an adverse event  22  as well as confirmation of that adverse event  22 . Once this finding and confirmation has occurred, the processor  48  can deliver the video feed  42  to the monitoring station  44  so that the patient  14  can be placed under closer observation. 
     Additionally, the various estimation networks  54  can also be used to determine that an adverse event  22  has occurred and provide an expedient response from hospital staff or other caregivers. In particular, the estimation networks  54  can be used to determine whether an adverse event  22 , such as a fall, has occurred such that the patient  14  is in distress and needs immediate assistance. As exemplified in  FIG.  9   , the various networks can be used for various layers  180  of data points  50  that can be used cooperatively for determining the current status of the patient  14  in a particular care space. As discussed herein, these captured data points  50  can be compared against previously stored sets  120  of data points  50  from the same patient  14  as well as a plurality of other patients  14 . 
     Over the course of time, the patient monitoring system  10  captures various data points  50  that correspond to various generic locations on the body of a patient  14 . Again, the generically derived data points  50  are merely assigned to particular locations on a body of a patient  14  for ascertaining the character and nature of certain movements without capturing enough information that might be used to ascertain the identity or any personal identifying characteristics of a particular patient  14 . These data points  50  are stored into the catalog  140  or library of previously stored sets  120  of data points  50  for comparison in the future. 
     The various estimation networks  54  operated by the processor  48  for utilizing the patient monitoring system  10  can be determined and ascertained as the buffered sections  46  of video are utilized by the processor  48 . In addition, the various data points  50  captured by the processor  48  through the analysis of the buffered sections  46  and video can also be compared with previous and similarly situated data points  50  from the catalog  140  of previously stored sets  120  of data points  50 . This comparison provides various prediction models for events that are likely or unlikely to happen in the future. Where a number of prediction models are possible based upon the captured data points  50  from the buffered section  46  of video, the processor  48  can place percentages or degrees of likelihood on certain predictive models based upon the previously stored data points  50 . As additional information is collected through the processing of subsequent buffered sections  46  of video, a larger sample size can be used from comparison with the previously stored sets  120  of data points  50 . Accordingly, the processor  48  can filter or narrow the various predictive models to arrive at one or a narrow set of events that are more likely than not to occur in the future. 
     By way of example, and not limitation, after a particular buffered section  46  of video is processed and analyzed, the processor  48  may determine that fifty (50) particular predictive models are applicable to the ascertained data points  50 , based upon a review of the previously stores sets of data points  50 . Where none of these models have a particularly high likelihood of occurrence (i.e., below 50 percent, 30 percent or 10 percent), additional data points  50  are collected through an analysis of subsequent buffered sections  46  of video. As additional sections of buffered video are analyzed by the processor  48 , these fifty (50) predictive models can be filtered over time. Evaluation of additional buffered sections  46  of video may result in ten (10) predictive models. Moreover, analysis of additional buffered sections  46  of video may narrow this result down even farther such that three predictive models, for instance may be the most likely. Additionally, these likely predictive models can also be scored based upon a percentage of likelihood or other scoring mechanism, where a particular predictive model exceeds a certain threshold (i.e., 66 percent, 80 percent, or other similar threshold). The processor  48  can determine what the most likely predictive event is. Where this predictive event is likely to occur is an adverse event  22 , the processor  48  can deliver the video feed  42  to the monitoring station  44  so that the patient  14  can be placed under closer observation through the offsite observer  24 . It should be understood that the percentages and thresholds described herein are exemplary in nature and various thresholds of various sensitivity can be utilized depending upon the needs of the user of the patient monitoring system  10 . 
     In addition, when the predictive modeling function of the patient monitoring system  10  determines that an adverse event  22  is likely or imminent, the patient monitoring system  10  can also alert hospital staff of this event such that one or more members of the hospital staff can be alerted to the patient  14  to provide closer observation. Moreover, when it is determined that an adverse event  22  is more likely, the resolution of the buffered sections  46  of video may be adjusted. By way of example, instead of the processor  48  analyzing a buffered section  46  of video with 100 frames, the processor  48  may analyze a buffered section  46  of video with 15 frames of video. In such an aspect, more rounds of analysis are set over a particular period of time so that a more refined estimation and analysis can occur, when needed most. 
     According to various aspects of the device, the patient monitoring system  10  can be utilized as an assessment tool for patients  14  that have been admitted to a hospital or other care space. By way of example, and not limitation, the patient monitoring system  10  can monitor the movements and actions of a newly admitted patient  14  by capturing the various data points  50  and layers  180  of data points  50  as described herein. These data points  50  can be evaluated and analyzed to assess whether the patient  14  presents a fall risk, presents a risk of other adverse events  22 , or presents a relatively low risk of adverse events  22 . Once this evaluation is complete, the monitoring unit  16  for the patient monitoring system  10  can either be left in the care space or can be moved to another care space where the monitoring unit  16  may be more needed. Use of the patient monitoring system  10  as an evaluation tool can provide criteria where a patient  14  may act differently where they know they are being directly observed by a doctor or another member of a care facility. The use of this objective data can be useful in monitoring the status of a particular patient  14 , as well as the likelihood of an adverse event  22  occurring. 
     Referring again to  FIG.  3   , it is contemplated that the patient monitoring system  10  can include a monitoring unit  16  that can include a microphone  190 , speaker  192 , display  194  and other sensory equipment. This sensory equipment can be used for monitoring the patient  14 , but can also be used for various telecommunication purposes. By way of example, and not limitation, the monitoring unit  16  can be used as a telecommunications device  196  to allow the patient  14  to speak with healthcare providers, staff of the facility and other members of their healthcare team. In addition, the telecommunications features of the monitoring unit  16  can also be used as a telecommunications device  196  to allow the patients  14  to communicate with friends, family members and other individuals. This connectivity and interactive resource of the patient monitoring system  10  can provide a communications outlet for patients  14  that may be quarantined or otherwise placed in isolation for any number of reasons. It is contemplated that these telecommunications may or may not be recorded. Where the telecommunication is with a member of the hospital staff or the patient&#39;s care team, these communications may be recorded for the benefit of the patient  14  and also for record keeping. As described herein, such information is stored within a memory  52  that is entirely separate from the memory  52  that retains the data points  50  and the previously stored sets  120  of data points  50 . Where the patient  14  is communicating friends, family, and people outside of the healthcare team, these communications are not typically recorded. It is contemplated that the audio and video portions of these communications can be analyzed through the processor  48  to arrive at the various data points  50  described herein. In particular, a plurality of auditory data points  154  can be utilized for gauging a condition, temperament and movement of the patient  14 . In providing this analysis, individual words and phrases may not be recorded, but may be assigned generic identifiers that correspond to the volume, tenor, pattern, and cadence of speech. These data points  50  can be utilized for determining whether the patient  14  is in distress, is irritated or is otherwise out of sorts. 
     According to various aspects of the device, as the processor  48  for the patient monitoring system  10  evaluates the various buffered sections  46  of video to determine the plurality of data points  50 , the data can be delivered from the camera  40  to a separate system for processing. This separate system can be used as a dedicated system that can be outside of the hospital&#39;s facilities so that processing speed can be specifically allocated to the review and analysis of the buffered sections  46  of video according to the patient monitoring system  10 . Once the data points  50  are converted, the buffered section  46  of video can be delivered to the monitoring station  44  and the data points  50  can be delivered to a particular storage memory  52  for analysis and later use. 
     Referring to  FIGS.  1 - 3  and  11   , the system  10  can include a server  200  configured to store and to provide data related to monitoring of the patient  14 . The server  200  can include one or more computers that may include virtual machines. The server  200  also includes a first communication interface  202  configured to communicate with the monitoring unit  16  and/or monitoring station  44  via a first network  204 . In some embodiments, the first network  204  may include wired and/or wireless network connections, including Wi-Fi, Bluetooth, ZigBee, Near-Field Communications, a cellular data network, and the like. As a non-limiting example, the first network  204  may operate locally to a medical facility (e.g., a local network of a hospital). The server  200  includes a first processor  206  and a first memory  208 . The first memory  208  includes first instructions  210  that, when executed by the first processor  206 , are operable to calculate and/or determine various data related to monitoring of the patient  14 . The server  200  is configured to store data related to monitoring of the patient  14 . For example, the first memory  208  includes a database  212  configured to hold data, such as data pertaining to monitoring of previous patients. An artificial intelligence engine  214  may be provided for interacting with the data stored in the first memory  208  when performing various techniques, such as generating various machine-learning models  216 . The models  216  may be trained to predict an adverse event  22  for a patient  14 . For example, the data can include cohort data of other or previous patients having similar experiences prior to the occurrence of an adverse event  22 . The models  216  may be trained on this data in order associate certain data points  50  with the adverse event  22 . 
     The one or more machine learning models  216  may comprise a single level of linear or non-linear operations and/or the machine learning models  216  may be trained via a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Deep networks may include neural networks  266  including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks  266 . 
     The server  200  may include a training engine  218  capable of training the models  216  based on initial data, as well as feedback data via the monitoring station  44 . The training engine  218  may include a rackmount server, a personal computer, a smartphone, an Internet of Things (IoT) device, or any other desired computing device. The models  216  may be trained to match patterns of a first set of parameters (e.g., information related to body parts and/or body movements). The one or more machine learning models  216  may be trained to receive the first set of parameters as input, map or otherwise associate or algorithmically associate the first set of parameters to the second set of parameters associated with an adverse event  22 , such as a fall of the patient, a patient pulling tubes, etc. 
     The server  200  may also be in communication with a second network  220 . The second network  220  may be configured similar to or different than the first network  204  in terms of protocol. However, according to some non-limiting examples, the second network  220  may be operable to communicate with a plurality of medical facility networks (e.g., a plurality of first networks  204 ), as demonstrated by communication node  221 . According to this aspect of the disclosure, the second network  220  may be a “global” network requiring security access information different from security access information required to access the first network  204 . Via the communication node  221 , the second network  220  may allow the server  200  to harvest data from a plurality of monitoring stations  44  and/or monitoring units  16  distributed across a plurality of first networks  204  each associated with a different medical facility or medical professional network. 
     The monitoring station  44  includes a second communication interface  222  configured to communicate with the server  200  via the first network  204 . The monitoring station  44  can include one or more computers that may include virtual machines. The monitoring station  44  includes a second processor  224  and a second memory  226 . According to some aspects, the second processor  224  may be the same processor  48  discussed throughout the disclosure. Further, the second memory  226  may be the same memory  52  described herein. The virtual data points  50  and the estimation networks  54  may therefore be stored in the second memory  226 . The second memory  226  includes second instructions  228  that, when executed by the second processor  224 , are operable to calculate and/or determine various data related to patient monitoring. The monitoring station  44  is configured to store data related to patient monitoring. For example, the second memory  226  includes a second database  230  configured to hold data, such as data pertaining to previous patient monitoring. The data stored in the second database  230  may be periodically updated with, synchronized to, or otherwise similar to the data stored in the first database  212 . In this way, the monitoring station  44  may operate as a standalone system that interacts with the monitoring unit  16 . It is generally contemplated that the second memory  226  may include models similar to the models  216  stored in the server  200 , or other similar models. 
     The monitoring station  44  may include a buffer module  232  in communication with the second processor  224  and configured to buffer image data communicated from the monitoring unit  16 . The buffer module  232  may interpose the second communication interface  222  and the second processor  224 . An interface  234  may be provided with the monitoring station  44  for displaying buffered image data. The interface  234  may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, etc. The interface  234  may incorporate various different visual, audio, or other presentation technologies. For example, the interface  234  may include a non-visual display, such as an audio signal. The interface  234  may include one or more displays  194  presenting various data or controls. 
     According to some aspects of the present disclosure, a system  10  for predicting an adverse event  22  for a patient  14  includes one of the first database  212  and the second database  230  including a first data point  50  related to a first or previously observed care setting  12  of a previously observed patient. This can correspond to the previously stored sets  120  of data points  50 . A camera  40  is configured to capture image data corresponding to a second or current care setting  12  for the patient  14 . A display  194 , such as interface  234 , is in communication with the camera  40  for presenting the image data. A processing device (e.g. first processor  206  and/or second processor  224 ) is communicatively coupled with the camera  40  and at least one of the first and second databases  212 ,  230 . The processing device is configured to determine, based on the image data, a second or current data point  50  related to the current care setting  12  within which the patient  14  is being observed. The processing device may also be configured to compare the first data point, or the previously stored sets  120  of data points  50  to the second or currently derived data point  50 . The processing device is configured to determine, based on the comparison of the first data point to the second data point, adverse event data corresponding to the adverse event  22 . The adverse event data includes probability data corresponding to a likelihood of the adverse event  22 . The processing device is configured to communicate an instruction to present the probability data on the display  194 . According to some aspects of the present disclosure, the probability data is operable between a warning state and a safe state. The warning state corresponds to a higher likelihood of the adverse event  22  and the safe state corresponds to a lower likelihood hood of the adverse event  22 . In the warning state, the video feed  42  can be delivered to the monitoring station  44  and the offsite observer  24 . In the safe state, the video feed  42  may or may not be delivered to the monitoring station  44  and the offsite observer  24 . 
     Referring now to  FIGS.  12 - 16   , the patient monitoring system  10  may include an elopement detection system  240 . The system  240  may utilize the previously-described hardware/software features shown and described with respect to the preceding figures or may be a separate implementation. For example, in addition to the features described above with respect to the adverse event detection features, the monitoring unit  16  may include one or more audio or video capturing devices  244 ,  246  configured to capture image data of objects, such as patients  14 , visitors, beds, or other items in a medical setting, similar to or the same as the aforementioned camera  40  and microphone  190 . The data may then be communicated to at least one observer client  247 . 
     In the present example shown in  FIG.  12   , the elopement detection system  240  typically includes at least one server  248 ,  250  that is configured to process the captured data to identify objects in a medical environment and/or determine a state of the objects in order to alert the observer client  247  to an elopement condition. For example, the at least one server  248 ,  250  may include a first server  248  that is configured to communicatively interpose the observer client  247  and/or the monitoring unit  16  and a second server  250 . As described herein, the observer client  247  can be the interface through which the offsite observer  24  observes the relevant information from the patient monitoring system  10  and the elopement detection system  240 . 
     Referring again to  FIG.  12   , the second server  250  may operate similar to or the same as the server  200  previously described with respect to  FIG.  11   . For example, the second server  250  may include one or more artificial intelligence engines  252  that are configured to train one or more machine learning models  254  to detect the elopement condition based on historical data stored in a database  256  that is in communication with the AI engine  252  and the second server  250 . The first and second servers  248 ,  250  may include first and second processors  258 ,  260 , respectively, that may execute instructions stored in memories  262 ,  264  (e.g., a first memory  262  and a second memory  264 ) in each of the first and second servers  248 ,  250 . The instructions stored in the memories  262 ,  264  may relate to generating new stream handling requests, for example, from the first server  248  to the second server  250 , closing certain data streams related to individual medical environments (e.g., patient care settings  12 ), modifying data streams between the first and second servers  248 ,  250  and/or the observer client  247 , and the like. 
     In the example in which the second server  250  includes AI functionality, the AI engine  252  may process the image/video/audio data captured by the monitoring unit  16  through the machine learning models  254  to generate a message or data points  50 , such as a string, binary values, or other data classifier to communicate back to the first server  248  and/or to communicate to the monitoring unit  16 . For example, as will be further described in reference to the proceeding figures, the message may include an indication of elopement, a command to control the monitoring unit  16 , or another response. 
     As illustrated in  FIG.  12   , the monitoring unit  16  may include one or more electromechanical actuation devices  268 , such as motors, to perform the PTZ operability. These motors or other actuators are configured to adjust a viewing angle of the audio and/or video capturing devices  244 ,  246  with respect to the medical environment in response to the messages received from the at least one server  248 ,  250 . For example, the at least one electromechanical actuator  268  may include three motors (e.g., a first actuator, a second actuator, a third actuator) that are configured to control a pan function, a tilt function, and a zoom function for at least the image capturing devices  244  of the monitoring unit  16 . Although not illustrated in detail, it is contemplated that various gearing mechanisms may be incorporated between the electromechanical actuation devices  268  and the image capturing devices  244 . Thus, instructions communicated by the observer client  247  to the at least one server  248 ,  250  may result in one or more instructions being communicated to the monitoring unit  16  to adjust the monitoring unit  16  to track the patient or another object in the medical environment based on determination of the elopement condition. 
     It is contemplated that the observer client  247  may incorporate similar computational structures as described in relation to the first and second servers  248 ,  250  and/or the servers previously described in relation to the preceding figures, such as the processors  258 ,  260 , memories  262 ,  264 , AI engine  252 , and machine learning models  254 . For example, virtual machines, local databases, and the like can be included or can be in communication with the observer client  247 . As will be described further below, the at least one observer client  247  can include a screen, such as a monitor  270 , an RGB display, or the like, for displaying the video/image data. Although not shown, it is contemplated that the observer client  247  can further incorporate one or more audio output devices, such as speakers, for outputting audio data captured by a microphone  190  of the monitoring unit  16 . Additionally, the observer client  247  can include a microphone  190  that can be used to provide a one-way or two-way communication interface. 
     Referring now more particularly to  FIG.  13   , an exemplary display  272  for the observer client  247  is illustrated with a plurality of tiles  274  corresponding to a plurality of care settings  12  that correspond to a plurality of individual patient  14  rooms of one or more treatment facilities and/or one or more home care settings  12 . The display  272  may be presented, for example, at the screen  270  previously described in relation to  FIG.  12    and may include one or more objects  276  that allow for user interfacing to control various monitoring functions for the elopement detection system  240 . For example, a digital toggle switch  278  may be incorporated for enabling one or both of active tracking of the patient  14  in the medical environment and/or enablement of the elopement detection system  240 . More particularly, the user, such as the offsite observer  24 , may selectively enable presentation of an alert message (e.g., a warning  282 ) of the elopement condition on the display  272  and/or selectively enable the one or more servers to control movement of the monitoring unit  16 , such as the pan function, the tilt function, and/or the zoom function (PTZ function). In operation, the offsite observer  24  may select, via a user input device such as a mouse, a touch input on a touchscreen, a user input to a keyboard, or the like by toggling the at least one object  276  after selecting (via a similar input mechanism) one of the plurality of tiles  274 . In this way, PTZ tracking and/or elopement detection enablement may be controlled on a care setting  12 -by-care setting  12  basis. In other examples, the at least one object  276  may control monitoring and/or enablement of either feature for all of the tiles  274 /medical environments. In certain aspects of the device, the PTZ tracking can be linked or assigned to a particular individual, typically the patient  14 . The PTZ tracking can then be automatically operated, via the various actuators, to track the patient  14  within and with respect to the care setting  12  for the patient  14 . 
     Still referring to  FIG.  13   , an indicator  280  may be configured to be presented for each tile  274  in response to detecting the elopement condition and/or a pre-elopement condition. For example, in the tile  274  shown at the top right of  FIG.  13   , the elopement indication  330  may be a text and/or color presentation of the warning  282  that a previously detected patient  14  is no longer in frame of the monitoring unit  16 . This condition may correspond to an elopement condition or a pre-elopement condition upon which the PTZ tracking, if enabled, would result in adjustment to the PTZ tracking functions of the image capturing devices  244 . 
     It is contemplated that although nine tiles  274  are illustrated corresponding to nine separate medical environments, it is contemplated that any number of tiles  274  may be presented at the display  272  depending on user preference, software modes, or the number of care settings  12  and patients  14  being monitored. 
     Referring now to  FIGS.  12 - 16   , the elopement detection system  240  may present, as part of the display  272 , tracking features  284 ,  286  overlaid over the image data captured by the monitoring unit  16  to indicate or flag inconsistencies based on derived or determined expectations of the elopement conditions. For example, a first tracking feature  284 , as illustrated in the middle tile  274  of the display  272 , may include a geometric overlay  288  that corresponds to one or more aspects of the clothing  312  of a person in the care setting  12 , such as the patient  14 . A second tracking feature  286  may track one facial feature  310  or a combination of facial features  310  of the patient  14 . This second tracking feature  286  can encompass or otherwise align with the face of the corresponding person, typically the patient  14 . 
     Referring again to  FIGS.  12 - 16   , according to various aspects of the device, where multiple people are present in the care setting  12  for the patient  14 , the first and second tracking features  284 ,  286  can be used to track the patient  14  as well as any additional people (non-patients  316 ) in the care setting  12 . In addition to tracking components of clothing  312  and combinations of facial features  310 , the elopement detection system  240  of the patient monitoring system  10  can also associate the tracked components of clothing  312  with the tracked facial features  310  with respect to dedicated individuals, such as the patient  14 . In this manner, using the first and second tracking features  284 ,  286 , the elopement detection system  240  can distinguish the patient  14  from non-patients  316 , such as visitors or hospital staff, that may be present in the care setting  12  for the patient  14 . Additionally, the elopement detection system  240  can maintain this confirmed association  314  to verify that the patient  14  is in the care setting  12 . Where this confirmed association  314  cannot be verified, changes, or no longer matches, such as where the patient  14  changes clothing from a hospital gown  362  to street clothes, such as shirt  356  and pants  360 , the elopement detection system  240  can provide an alert, such as the elopement indication  330 , regarding the disassociation. This disassociation or mismatch with respect to components of clothing  312  and facial features  310  can result in an alert or warning or can trigger a heightened monitoring routine for the patient monitoring system  10  and the elopement detection system  240 . 
     It is contemplated that the tracking features  284 ,  286  presented in the exemplary display  272  may be selectively hidden, muted, or omitted in some examples, in order to allow a clean presentation of the image data to the observer client  247  and, thus, an offsite observer  24 . The tracking features  284 ,  286  may be employed for detecting mismatch conditions between clothing  312  and facial features  310  of a confirmed association  314 , and other identification information, as will be described further below. 
     Referring now to  FIGS.  14  and  15   , the logic and data flow of the elopement detection system  240  is generally illustrated. For example,  FIG.  14    generally illustrates the control and data flow process S 100  amongst the various elements shown and described with respect to  FIG.  12   .  FIG.  15    presents a method of detecting an elopement condition as performed by the elopement detection system  240  shown and described in relation to  FIG.  12   . With particular reference to  FIG.  14   , the observer client  247  may be configured to receive the user input via, for example, the objects  276  shown in  FIG.  14   , at step S 102 . The input may include an enablement or disablement of one or both of the PTZ tracking control and/or the elopement warning enablement functions. In response to receiving an instruction, for example, to enable one or both of these AI features, the first server  248  may generate a new stream handling request at step S 104 . The new stream handling request may include real-time streaming protocol (RTSP) information, URI class information, and/or feature state data to allow the second server  250  to confirm the new data stream. 
     At step S 106 , the second server  250  initiates a stream handler instance to run an inference on the image data stream from the image capturing device  244  and present at the observer client  247  in response to the request from the first server  248 . As a result, at step S 108 , the monitoring unit  16 , which captures the video data, begins providing the RTSP stream to the second server  250  to allow the second server  250  to process the image data and compare the image data to historical cohort image data (e.g., stored in the database  212 ,  230 ) to detect the elopement condition. For example, the machine learning models  254  may be trained to compare the facial features  310  identified in the image data to the clothing  312  donned by the patient  14  with the given facial features  310 . Based on such comparisons, the processor  260  of the second server  250  may determine that the patient  14  is not wearing clothes  312  consistent with the previously confirmed association  314  with respect to the patient  14 . The processor may also determine that the patient  14  having the tracked facial features  310  is not wearing clothing  312  that is associated with a medical environment. The result of the non-conforming determination can be a triggering of the elopement condition in response to the comparison (Step S 210 ). This triggering can be automatic or can be at the initiation of the offsite observer  24  at the observer client  247 . In other examples, the machine learning models  254  may be trained to detect fast or quick movement by the patient  14  in changing clothes  312  and/or movements directed to removal of medical monitoring equipment and correlate such actions with the elopement condition. 
     Referring again to  FIGS.  14 - 16   , the elopement detection system  240  can initiate a prompt to adjust the monitoring unit  16 , or can automatically adjust the monitoring unit  16  where the patient  14  moves to a position that is outside of the care setting  12  monitored by the camera  40 . When the camera  40  is moved to a position to place the patient  14  within the video feed  42 , the elopement detection system  240  can verify the previously determined and confirmed association  314  of the facial features  310  and clothing  312  of the patient  14  to confirm that the patient  14  is within the care setting  12 . When the patient  14  is not able to be placed within the video feed  42 , the elopement detection system  240  can provide the observer client  247  with an elopement indication  330  and an elopement alert can be activated. 
     In addition, the patient monitoring system  10  is configured to maintain a database  256  of information that can be referenced in the future for identifying actions that are likely to precede an attempted elopement. Actions such as switching clothing  312  with a visitor, quickly changing clothing  312 , fast movements toward an egress, and other actions may be evaluated by the elopement detection system  240  of the patient monitoring system  10  and may be determined to be indicative of an attempted elopement. 
     The particular elopement conditions of the present disclosure may not be limited to the previously described elopement condition examples. For example, the machine learning models  254  may be trained to detect elopement conditions based on unforeseen variables that may have no obvious logical connection to elopement conditions but may be predicted by the AI engine  252 , based upon an evaluation of previously captured data. Other, more logical detections may be further refined and/or defined by the second processor  260  of the second server  250 , such as a patient being out of view of the camera  40  and/or hiding of the facial features  310 , clothing  312 , or body to prevent detection and tracking by the monitoring unit  16 . 
     With continued reference to  FIG.  14   , steps S 112  and S 114  may be performed by the elopement detection system  240  in response to the second server  250  determining the elopement condition and/or determining a positioning command for the image capturing devices  244  of the monitoring unit  16 . For example, with respect to step S 112 , when elopement detection is enabled via the observer client  247 , the elopement warnings  282 , in the form of instructions, may be communicated to the first server  248 , which may then be forwarded to the observer client  247  to be displayed within the display  272  at step S 116 . In this way, the first server  248  may operate as an exchange server between the observer client  247  and the second server  250 . Once the warning  282  message is communicated to the observer client  247 , the processor of the observer client  247  may receive the elopement warning  282  condition data, process the condition data, and display the indication (see, e.g.,  FIG.  13   ) with the image data captured by the monitoring unit  16 . For example, for a given medical environment/monitoring unit  16 , the elopement condition may be displayed overlaying the tile  274  associated with the particular monitoring unit  16  to which the elopement condition was detected. 
     With regard to step S 114 , if PTZ control is enabled via the observer client  247  at the object  276 , the second server  250  may communicate positioning commands to the first server  248 , which may then be forwarded to the monitoring unit  16  at step S 118 . Thus, the first server  248  may also operate as a message router for messages between the second server  250  and the monitoring unit  16 . At step S 120 , the monitoring unit  16  adjusts the positioning of the image capturing device  244  to locate the patient  14  in the care setting  12  in the event the patient  14  is outside of the viewing angle when PTZ control is enabled. Thus, at step S 120 , instructions communicated by the at least one server  248 ,  250  may result in motor control commands to adjust, for example, a lens of the image capturing module (such as camera  40 ) or an axis in 3D space of the image capturing module. 
     As indicated in  FIG.  14   , the above described data/control flow may be recursive and dependent on the user input at the display  272  and/or whether or not the elopement condition or patient out of frame condition is determined by the second processor  260 . For example, the user may disable the AI functionality, which may result in termination of the stream handling requests and stream handling instances. Further, it is contemplated that the steps described herein may be performed amongst a plurality of monitoring units  16  having individual stream instances managed/controlled by one or a plurality of observer clients  247 . It is also contemplated that the at least one server  248 ,  250  may each or either incorporate a plurality of cores, with one core assigned to each instance. These examples are nonlimiting, such that any number of processors  258 ,  260 , AI engines  252 , machine learning models  254 , virtual machines, or the like may be employed to operate it in the elopement detection system  240  as generally described above. 
     Referring now more particularly to  FIG.  15   , a method S 200  for detecting elopement condition in a care setting  12  includes receiving, at the observer client  247 , a user input to enable active patient monitoring at step S 202 . At step S 204 , the first server  248  in communication with the observer client  247  generates a new stream handling request and communicates the new stream handling request to the second server  250  that is in communication with the first server  248 . In response to receiving the new stream handling request, the second server  250  communicates an instruction to the monitoring unit  16  that is in communication with the second server  250  to communicate video data captured by the monitoring device to the second server  250  at step  206 . At step  208  of the method, the second server  250 , via an artificial intelligence engine  252 , processes the image data captured by the monitoring unit  16  in a machine learning model  254  trained to the detect an elopement condition for the patient. Based on the elopement condition, the second server  250  may communicate an instruction to the observer client  247 , via the first server  248 , to present an indication of the elopement condition at a display  194  of the observer client  247 . In some examples, the method further includes communicating an instruction to the monitoring unit  16  to operate the actuation device of the monitoring unit  16  to adjust the viewing angle of the image capturing device  244  in response to determining one or both of the elopement condition and a patient out of view condition. 
     In general, the remote database  256  shown and described with respect to  FIGS.  12 - 16    may be configured to store refined data output that includes image data sets related to patient tracking, such as tracking of motions within the care setting  12  (e.g., a quick motion to a door of the room, a window of the room, or the like), clothing  312  (e.g., hospital gowns  362 , civilian clothing, pants  360 , shirts  356 , dresses  358 , or the like), facial features  310 , or the like. Additionally, certain auditory information can be captured by the microphone  190  that may be indicative of an attempted elopement. This auditory information can be in the form of specific words or phrases (“let&#39;s go” or “be quiet” for example) as well as a particular tone of voice or non-word sound (whispering or “shushing” sounds, for example). 
     The machine learning models  254  may be trained on this data in, for example, at least one neural network  266  ( FIG.  12   ) that includes a plurality of neurons that may be weighted differently depending on outcomes (e.g., actual elopements or actual patients being out of frame) of the historical data. It is contemplated that the data stored in the refined data output database  256  may be anonymized and limited to identifiable features according to some examples. 
     It is also contemplated that the network previously described in reference to  FIG.  11    may be incorporated with the elopement detection system  240 . In this way, the elopement detection functions and the pose estimation functions may operate on the same network using the same or similar protocols for data exchange between the monitoring unit  16  and the servers  248 ,  250 , the observer client  247 , or the like. In some examples, the pose estimation and the elopement detection features may also be processed simultaneously in order to fortify determinations or estimations of an actual elopement of the patient. 
     As exemplified in  FIGS.  1 - 3  and  12 - 16   , the patient monitoring system  10  can include the estimation networks  54  and the elopement detection system  240  for capturing various data points  50  that can be used for monitoring a patient  14  within the care setting  12 . Using the elopement detection system  240 , the camera  40  and other components of the monitoring unit  16  can be used for capturing the data points  50  that can relate to any one of various features of the patient  14  and other individuals (non-patients  316 ) present within the care setting  12 . The data points  50  observed and tracked by the elopement detection system  240  can include or relate to a combination of facial features  310 , as well as components of clothing  312  for each person observable within the video feed  42 . Using these data points  50 , the processor  48  for the elopement detection system  240  is configured to associate the data points  50  for the combination of facial features  310  with the data points  50  for the components of clothing for each person to define a confirmed association  314  for at least the patient  14 . The elopement detection system  240  can also generate these confirmed associations  314  for other non-patients  316  present within the care setting  12 . 
     Where the elopement detection system  240  generates the confirmed associations  314  for a plurality of people in the care setting  12 , the processor  48  is further configured to identify, for each confirmed association  314 , whether that person is a patient  14  or a non-patient  316 . This identification of the various confirmed associations  314  allows the elopement detection system  240  to track the patient  14  as the patient  14  moves through the care setting  12 , and as other people within the care setting  12  move around the patient  14 . The elopement detection system  240  is also configured to verify the identity of the confirmed association  314  of the patient  14  by comparing updated data points  50  from the video feed  42  with the data points  50  of the confirmed association  314 . Accordingly, the processor  48  periodically compares the confirmed association  314  for the patient  14  and other individuals within the care setting  12  with updated data points  50  captured from the video feed  42 . 
     Referring again to  FIGS.  1 - 3  and  12 - 16   , it is contemplated that the updated data points  50  relate to similar data points  50  that were previously captured to ascertain and define each of the confirmed associations  314  for the people within the care setting  12 . Using the elopement detection system  240 , an alert or elopement warning  282  can be activated when the confirmed association  314  of the patient  14  cannot be verified based upon the comparison with the updated data points  50 . Accordingly, where the patient  14  changes clothes  312 , the data points  50  for the combination of facial features  310  and the components of clothing  312  for the confirmed association  314  no longer match. In this condition, the alert can be activated for an elopement indication  330 . Additionally, the alert for the elopement indication  330  can be activated where the confirmed association  314  of the patient  14  can no longer be found within the video feed  42 , in particular, after the video feed  42  is able to be moved using the PTZ tracking system. 
     According to the various aspects of the device, as exemplified in  FIGS.  1 - 3  and  12 - 16   , the elopement detection system  240  for the patient monitoring system  10  can be activated when the patient  14  leaves a particular boundary line  130 . This boundary line  130  can be a virtual line or bounding box surrounding the bed  78  or surrounding a non-movable seating area, such as a couch, recliner or other similar stationary seating. Accordingly, when the patient  14  stands up within the care setting  12  and is able to ambulate, or be moved within a wheelchair through the care setting  12 , the elopement detection system  240  can activate to define the various confirmed associations  314  related to each person within the care setting  12 . These confirmed associations  314  are then tracked at least for as long as the patient  14  is outside of the various predetermined boundary lines  130  of the care setting  12 . The activation of the elopement detection system  240  can activate automatically or a prompt can be delivered to the observer client  247  that the elopement detection system  240  needs to be activated. 
     Referring again to  FIGS.  1 - 3  and  12 - 16   , according to various aspects of the device, the elopement detection system  240  can generate and verify a confirmed association  314  with respect to the patient  14  at any time the patient  14  is within the video feed  42 . Accordingly, while the patient  14  is in bed  78  or within stationary seating, the elopement detection system  240  can capture the data points  50  related to at least the combination of facial features  310 , and, where visible, the components of clothing  312 , to generate the confirmed association  314  for the patient  14 . When the patient  14  gets out of bed  78  or stands up from the seated position, the elopement detection system  240  can then activate and can verify the previously generated confirmed association  314 . 
     According to the various aspects of the device, the components of clothing  312  that can be ascertained using the elopement detection system  240  can include any one of various clothing-related data points  50 . These data points  50  can include, but are not limited to, clothing outline, clothing color, clothing pattern, transition  354  from torso  350  to legs  352 , leg covering configuration, and other similar clothing-related data points  50 . The transition  354  from torso  350  to legs  352  can include features such as a belt, waistband, change in clothing from a shirt  356  to a dress  358  or pants  360 , a lack of change or transition  354  in the case of a hospital gown  362 , and other similar transitions. The leg covering configuration typically includes distinguishing between pants  360 , skirts and dresses  358 , hospital gowns  362 , and other similar leg coverings. The goal of capturing the data points  50  related to the components of clothing  312  is to differentiate between a patient  14  within the care setting  12  and a non-patient  316  within the care setting  12 , such as doctors, visitors, hospital staff, and other individuals. 
     According to the various aspects of the device, the combination of facial features  310  that are used to define the confirmed association  314  can include any one of various data points  50 . These data points  50  can include, but are not limited to, outline of hair  380 , outline of face  382 , relative locations of various facial features  310 , and other similar data points  50 . By way of example, and not limitation, the relative locations of the facial features  310  can include the relative locations of any two or more of left eye  384 , right eye  386 , mouth  388 , end of nose  390 , bridge of nose  392 , left ear  394 , right ear  396 , chin  398 , and other similar facial features  310 . Additionally, the combination of facial features  310  can include, but are not limited to the relative distances between any two or more of the various facial features  310  identified herein. 
     The goal of capturing these data points  50  related to the combination of facial features  310  is to distinguish the various individuals present within the care setting  12 . These data points  50  are not meant to capture identity, but are rather intended to differentiate between the various individuals so that a patient  14  can be differentiated from a non-patient  316 . This is done by determining the data points  50  related to the components of clothing  312  for the individual as being related to a patient  14  or a non-patient  316 . The components of clothing  312  can then be associated with the combination of facial features  310  to form a confirmed association  314 . Again, this confirmed association  314  is not used to identify any information other than distinguishing between a patient  14  and a non-patient  316 . Use of these data points  50  is configured to be captured, in an anonymous fashion, such that the identity of the individual is not readily ascertainable or is not ascertainable based upon a review of the particular data points  50  captured by the elopement detection system  240 . 
     According to various aspects of the device, the elopement detection system  240  is meant to capture a base or minimum amount of data points  50  related to the combination of facial features  310  and the components of clothing  312 . In certain instances, multiple people may have similar combinations of facial features  310  (such as family members and the like) and/or similar components of clothing  312  (such as co-workers, teams, or hospital staff). Where the base amount of data points  50  are not able to perform or determine confirmed associations  314  that are distinguishable from one another, additional data points  50  may be captured so that the confirmed associations  314  can be distinguishable at least between patient  14  and non-patients  316  within the care setting  12 . 
     Referring again to the elopement detection system  240 , as exemplified in  FIGS.  1 - 3  and  12 - 16   , when capturing data points  50  from the video feed  42  related to the components of clothing  312 , the elopement detection system  240  can monitor leg coverings to observe certain differences that may be indicative of a hospital gown  362  rather than pants  360  or a dress  358 . By way of example, and not limitation, the elopement detection system  240  may observe two discretely visible legs  352  that may be indicative of a pair of pants  360 , shorts or certain street clothes. Additionally, certain colors or patterns present on the clothing  312  as well as a transition  354  between torso  350  and legs  352  may also be indicative of street clothes, or a hospital gown  362 . Using these data points  50 , components of clothing  312  can be utilized for distinguishing a patient  14  versus a non-patient  316 . These data points  50  related to components of clothing  312  are then associated with combinations of facial features  310  to form the confirmed association  314  related to a patient  14  or a non-patient  316 . 
     As described herein, it is contemplated that, in certain aspects of the device, the confirmed association  314  with respect to the patient  14  is the only confirmed association  314  that is made and verified using the elopement detection system  240 . In such an aspect of the device, the confirmed association  314  of the patient  14  can be continually verified using updated data points  50  from the buffered sections  46  of the video feed  42 . 
     According to various aspects of the device, the elopement detection system  240  typically activates an alert when an elopement indicator  280  is verified, such as the patient  14  exiting the care setting  12 . It is contemplated that the elopement detection system  240  can be placed in communication with certain scheduling features of the care facility. These scheduling features can be known appointments or other times when the patient  14  is scheduled to leave the care setting  12 . In this instance, the elopement detection system  240  can compare a particular elopement indicator  280  with the schedule related to the particular patient  14 . In this manner the elopement detection system  240  can verify that the patient  14  is not attempting an elopement, but is rather exiting the care setting  12  to attend a scheduled event. 
     The elopement detection system  240  can also recognize when certain authorized individuals are present within the care setting  12 , such as hospital staff. In certain situations, it may be necessary for a member of the hospital staff to escort the patient  14  outside of the care setting  12 , such as for physical therapy. The member of hospital staff can provide an indication or authorization that they are authorized to help the patient  14  leave the care setting  12 . In this manner, the movements of the patient  14  can be discriminated and distinguished between elopement events and scheduled or authorized actions. 
     Referring again to  FIGS.  1 - 3  and  12 - 16   , the elopement detection system  240  can undergo a verification step for at least the confirmed association  314  of the patient  14  at regular intervals when the patient  14  is outside of the boundary line  130 . This verification step can also occur when there is an obstruction, such as a person, a fixture, or other object that passes between the patient  14  and the monitoring unit  16 . In this condition, the patient  14  can be temporarily out of view within the video feed  42  or at least partially obstructed within the video feed  42 . When the patient  14  reappears or is fully in view within the video feed  42 , the verification step can be performed to verify the confirmed association  314  of the patient  14 . 
     It is contemplated that the elopement detection system  240  can capture data points  50  of individuals in the care setting  12  for the patient  14  related to different sides  410  of the corresponding individual. In this manner, as a person is walking through the care setting  12 , the person may be turned to the side  410  in relation to the camera  40  or may have their back  412  turned to the camera  40 . For each person, the elopement detection system  240  can capture data points  50  that will enable the elopement detection system  240  to generate a confirmed association  314  that relates to each side  410  and the back  412  of the person. 
     By way of example, and not limitation, as a person moves through the care setting  12  data points  50  can be tracked that relate to the portion of the person that is facing the camera  40 . Accordingly, the data points  50  related to the combination of facial features  310  that are facing the camera  40  can be tracked. As the person turns, a different combination of facial features  310  can be tracked as data points  50  for the confirmed association  314 , where these data points  50  are visible within the video feed  42 . Similarly, the data points  50  related to the components of clothing  312  can also vary as the person moves and turns within the care setting  12 . Accordingly, the data points  50  that relate to the combination of facial features  310  and the components of clothing  312 , and the confirmed association  314  that is generated from these data points  50  can be in the form of a three-dimensional set  430  of data points  50 . This three-dimensional set  430  of data points  50  and the resulting confirmed association  314  can be tracked regardless of which way the person is facing in relation to the camera  40 . Accordingly, the elopement detection system  240  can track the patient  14  and non-patients  316  as they move and turn within the care setting  12 . 
     Referring now to  FIGS.  1 - 3  and  12 - 17   , having described various aspects of the patient monitoring system  10 , and the elopement detection system  240 , a method  800  is disclosed for operating the elopement detection system  240 . According to the method  800 , a step  802  includes activating the elopement detection system  240  when the patient  14  is outside of a predetermined boundary line  130 . Step  804  includes analyzing buffered sections  46  of the video feed  42  to identify the combination of facial features  310  and components of clothing  312  for a patient  14  that is outside of the predetermined boundary line  130 . According to the method  800 , step  806  includes associating the combination of facial features  310  with the components of clothing  312 , or vice versa, for each person within the care setting  12  to define and distinguish the various confirmed associations  314 . After these confirmed associations  314  are determined, the method  800  includes step  808  for verifying the confirmed association  314  of a patient  14  by comparing the combination of facial features  310  with the components of clothing  312  of the patient  14  with respect to updated data points  50  that are ascertained within the buffered sections  46  of the video feed  42 . Where the confirmed association  314  cannot be verified or where the confirmed association  314  is no longer able to be verified, the elopement detection system  240  can activate an alert related to a possible elopement with respect to the patient  14  (step  810 ). The unverified status of the patient  14  with respect to the confirmed association  314  can be based upon the patient  14  changing clothes  312  such that the combination of facial features  310  no longer matches the components of clothing  312  that was previously part of the confirmed association  314 . This lack of verification can also be a result of the patient  14  exiting the care setting  12 . 
     As described herein, the elopement detection system  240 , as with components of the patient monitoring system  10 , generally, can be used for ascertaining when a particular adverse event  22 , such as elopement has occurred. Additionally, over time, these data points  50  and confirmed associations  314  and movements of the confirmed associations  314 , in particular with respect to the patient  14 , can be anonymized and recorded within a memory  52 . Over time, this memory  52  can be accessed for comparing current movements of a patient  14 , or other individual, with these past anonymized events to form a prediction with respect to a possible elopement indication  330 . By way of example, and not limitation, various movements of a patient  14  who is outside of a predetermined boundary line  130  may be indicative of an elopement event. Also, movements of non-patients  316 , such as visitors, visiting the patient  14  may also be indicative of an elopement event. As the library of elopement indications  330  is generated and built upon over time, refinement of these elopement indicators  280  can become more robust and substantial such that a prediction can be made with increasing accuracy over time with respect to an attempted elopement from the care setting  12 . Accordingly, use of the elopement detection system  240  can be used to both verify the existence of an elopement as well as a prediction with respect to a possible elopement. 
     It is to be understood that variations and modifications can be made on the aforementioned structure without departing from the concepts of the present invention, and further it is to be understood that such concepts are intended to be covered by the following claims unless these claims by their language expressly state otherwise.