Patent Publication Number: US-2016224839-A1

Title: System to determine events in a space

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority from U.S. Provisional Patent Application No. 62/111,710 titled “System To Determine Events In Space”, which was filed on Feb. 4, 2015 and which is incorporated fully herein by reference 
    
    
     TECHNICAL FIELD 
     The present invention relates to the detection of activity or certain events, such as falls, that occur in an arbitrary space. More specifically, the present invention relates to a remote sensor that analyzes images in a room of a home to determine if occupants of that room have fallen or participated in other predetermined events such as sitting, standing or having visitors. 
     BACKGROUND INFORMATION 
     The global trend of an aging populace is well known; this creates a challenge in caring for these older people while still respecting their independence and privacy. “Aging-in-place” attempts to enable older people to live in their own homes as long as practical. It should be no surprise that 89% of elders want to stay in their own homes, and from both a personal and a societal perspective aging-in-place is considerably less expensive. However, aging-in-place can also put elders at risk, especially if they live by themselves; as of 2014 approximately 30% of the 40M community-dwelling elders, or about 12M people, live alone. One of the biggest risks to older people living by themselves is falls. 
     Falls are the leading cause of injury and death for older people. From an individual perspective, one-in-three people over 65, or 14.7M people, fall each year resulting in 2.4M emergency department visits, 722,000 hospitalizations and 22,900 deaths. Even minor falls can result in significant changes in independence. Up to 75% of patients who fall do not recover their pre-fall level of function. If an elder has fallen once, there is a 60% chance they will fall again within a year. Over one half of elders who fall are unable to get up without assistance and they are more likely to suffer additional complications and poorer prognoses. Patients who had fallen at home but were found in less than one hour had a total mortality of 12% but patients who had been helpless for more than 72 hours had a mortality rate of 67%. From a societal perspective, the cost of care for falls in 2012 was about $30B and, given the growing elder population, is anticipated to reach $67.7 billion by the year 2020. Older people fear moving to a nursing home or losing their independence more than they fear death. Unfortunately, for people living alone, a fall can lead to many hours of pain and helplessness on the floor until someone happens to discover them. 
     In addition to falls, there are other events that may be of interest to those caring for older people who live by themselves. The elder&#39;s general level of activity is important, especially for people who have congestive heart failure—less activity means they are getting sicker. Knowing how much a person sleeps can be a predictor of certain illnesses. Knowing if the elder has left the house, or has unanticipated visitors, is important for people with dementia. Unusual toileting patterns are a leading indicator of certain illnesses, especially urinary tract infections. 
     One may generalize relevant events (i.e. events of interest) into three categories. Emergent events (such as falls) which need immediate attention; safety events (such as when a demented person leaves the house) which also require immediate attention; and habitual events (such as sleep patterns) that don&#39;t require immediate intervention but are useful for looking at long-term patterns of disease progression. The system described here attempts to provide caregivers timely data on all three of these event categories. 
     Ideally, since systems to enable aging-in-place are installed in people&#39;s homes, they should be as non-obtrusive as possible. It should not require the older person to wear anything or change their lifestyle in any way. 
     The aging population along with its accompanying desire and challenge of enabling aging-in-place have been apparent for many years, and hence there have been many prior art attempts to develop system that address this concern. 
     The simplest and most common solution to the detection of emergencies among the elderly is not a true detection system, but rather simply employs a “panic button”. Systems of this type are often called Personal Emergency Response Systems (PERS), and are provided by companies such as Philips LifeLine, Framingham, Mass. If a person has fallen or otherwise needs help, they push a button on a transmitter that is worn around their neck or on their wrist. This transmitter sends a radio signal to a receiver/speaker-telephone, which is plugged into the telephone line. The reception of the radio signal causes the receiver/speaker-telephone to call a preprogrammed telephone number of a response center, where the phone is answered by an operator. The operator can then use the speaker-telephone to ask the victim if they need help. It should be noted that these systems do not generally provide any event data related to habitual or safety events; they are focused on emergent events. Even then, the obvious and significant limitations of this approach include: (i) the need for the elderly person to push the button, which may be difficult if the person is unconscious or has dementia so forgets the button; (ii) the elderly person must always have the button within reach (even at night); (iii) the button/transmitter must be within radio range of the receiver/speaker-phone; and (iv) many elderly people do not enjoy wearing the button. 
     Another prior art approach is to have a potential fall victim wear an accelerometer. This accelerometer is tuned such that if the person wearing the device falls down, the accelerometer detects the force of impact and sends a radio signal to a similar receiver/speaker-phone as described above. There are many variations on this theme in the prior art. 
     An example of this type includes a system which describes a fall-sensor accelerometer that is integrated into a mobile phone. Commercial products based on the accelerometer approach are offered by Philips Lifeline (Framingham, Mass.) and Tunstall (Yorkshire, UK). Systems of this type primarily attempt to overcome historically significant limitations such as false alarms generated when the patient sits or lays down abruptly. However, none of the prior art overcomes the fundamental flaw in the approach that the potential fall victim must wear the device on their person constantly—even at night. Other limitations include (i) the relatively high rate of false alarms generated from normal activities of daily living (ADL) or having the sensing accelerometer accidentally drop to the floor; (ii) the relatively high cost of such a device; (iii) like the PERS above, the sensing device must be within radio range of the receiver/speaker-phone; and, similar to the PERS, (iv) many elderly patients do not enjoy wearing the accelerometer. 
     Another prior art solution is the whole-house monitoring systems or “Smart Homes.” Prior art systems of this type have the potential to indirectly address the problem of fall detection by determining if the elder&#39;s normal ADL habits are compromised. These systems rely on sensors placed throughout the elder&#39;s home that communicate to a computer that infers ADL activities. For example, if a motion sensor in the bedroom normally senses movement at approximately 7:00 AM every morning, then one day if there has been no motion sensed by 8:00 AM, the system may infer that something is wrong and call for help. 
     An example of prior art systems of this type employs an algorithmic approach to gathering data and inferring ADL levels from the data. These systems are severely limited because (i) they only work with a single person living in the home; (ii) they require complex and expensive computer and sensor infrastructures to be installed throughout the entire home; and (iii) most significantly, they typically take many tens-of-minutes to hours before they determine that a pattern is truly changed and hence an alarm for an emergent event should be generated—these are many hours that a fall victim is potentially lying in pain on the floor. 
     More direct monitoring approaches have also been tried. Indeed, a video monitoring system has also been suggested to detect falls. While this approach again has the advantage of allowing remote detection of falls, it has a very significant limitation in that it requires video cameras to be constantly monitoring all the rooms of the elder&#39;s home. This creates obvious and significant privacy concerns. 
     Another prior art system B 2  describes utilizing ceiling-mounted Doppler radar units which determine a person&#39;s distance from the floor; if the distance measure indicates that the person is closer to the floor, an alarm is generated. While this system is valuable in that it is passive (doesn&#39;t require the elder to wear anything), the ceiling-mounted devices are difficult to install and expensive. As described, it also only detects falls and no other activities. 
     Another prior art passive fall detection system illuminates a potential fall victim with infrared light and uses infrared depth sensors to determine a point on the person&#39;s body, then calculates if that point gets closer to the ground. Infrared depth sensors are used in the Microsoft (Redmond, Wash.) Kinect game sensor. The challenge with these devices is that their resolution decreases significantly as a function of distance; they are optimized for a range of 8-10 feet; it is desirable to be able to monitor an entire room (which could be 20+ feet long) with a single device. Such prior art devices can typically only detect falls and not other events. 
     Another prior art device is a combination system that uses an on-body accelerometer similar to those described above, and a camera. If the accelerometer detects a fall, an image from the camera is analyzed to confirm the fall. While this approach must help reduce the false alarms created by having only one sensor, it unfortunately has the disadvantages of both accelerometer- and video-based solutions. Namely, it requires the person to remember to constantly wear the accelerometer and has the privacy concerns of video monitoring. 
     Yet another prior art system is a passive fall detection system that uses two sensors to establish upper and lower zones in a room. The outputs of these sensors are monitored and compared to known “fall signatures”; the system essentially determines if infrared energy moves from the upper into the lower zone of the room and, if so, determines that a fall must have occurred. This “dual zone” approach is subject to a high false alarm rate because the system cannot distinguish a fall from laying down in bed or a fast movement to sit down. Since the system only looks at infrared energy it cannot distinguish pets from humans, which also generates false positive alarms. The system also will not work there is more than one person in the room. Finally, while this system can identify movement as well as falls, it cannot identify events such as visitors, bathroom use, etc. 
     Some prior art systems use a single sensor installed at a known distance from the floor. Based on this known distance, a reference line is established which essentially divides the room into two zones. Motion information from above and below the reference line is analyzed to determine if the motion moved from above the line to below the line; if this is the case it is determined to indicate a fall. Since some systems analyze an image (as opposed to simply the infrared energy), it is hypothetically less prone to false alarms from pets. However, this approach still suffers from high false positives because the system cannot distinguish a fall from laying down in bed or a fast movement to sit down. It is also subject to the obvious disadvantage of needing to be accurately and precisely placed a known distance from the floor, which complicates installation. 
     SUMMARY 
     Based on the aging population and the desire for older people to live in their own homes, there is a need for a system to passively monitor emergent, safety and habitual events in the home. The system should be able to detect all emergent or safety events, be inexpensive, unobtrusive, easy to install, fast to alarm, have a low false alarm rate and not raise privacy concerns among the occupants of the house. Such a system will be described below. 
     The system of the present invention is simple enough to be installed and used by the elder, does not require special networking infrastructure (including an Internet connection), and does not require the elder to wear a special device, push any buttons if they fall or change their lifestyle in any way. The system can detect a variety of events, including but not limited to activity, falls, getting in and out of bed, visitors, leaving the house, sitting, standing, and the use of the toilet. The system is also highly immune to false alarms caused by pets, crawling children, laying down in bed or the elder purposely getting down on the floor. Finally, the system is inexpensive enough to be available to virtually anyone of any economic means. 
     The system of the present invention may include an imager that can capture an image of any arbitrary space. This imager can sense visible images or infrared images. The resolution of the images can be relatively crude—32×32 pixels will be assumed in the subsequent examples. This reduces the processing power and also reduces privacy concerns because no discernable features can be obtained. The system can capture images sequentially and subsequent images can be processed in such a way to remove stationary elements of the image. For example, if an image is captured at time T(1) it can be represented by a 32×32 matrix. A subsequent frame can be captured at time T(2), again represented by a 32×32 matrix. These two matrices can arbitrarily be labeled the F(1) and F(2) for the first and second frame respectively. F(2) and be subtracted from F(1)—if there is no activity in the field of the images the resultant matrix, R(2), will be zero. If there is activity in the room, the resultant will have only the active portion of the field. In this way, all the stationary elements of the room (furniture, etc.) will be removed and only the object that is moving will remain. 
     In a similar means, the range-finder can capture data regarding the distances of the various objects in the space at time T(1) and T(2). This data can also be subtracted; as with the image data, if there is no activity in the room the resultant will be zero. If there is moving, the resultant, D(2), will be the distance of the moving objects. For example, if the range-finder is ultrasonic, the output for a single “ping” at a given time is time-versus-amplitude data. If there is no activity in the room, a subsequent “ping” will return a similar time-versus-amplitude data so when these two data points are subtracted the result will be zero. However, if there is movement in the room the resultant will be the distance of the moving object for the sensor. In this way, an accurate distance measurement can be made of only the moving objects in the room, independent of any other objects. 
     Objects closer to the imager appear bigger than objects further away. For example, a person who is 6 feet tall may occupy the entire frame of a captured image if they are standing right in front of the camera and only a quarter of the frame if they are standing 20 feet in front of the camera. To compensate for this, a predetermined calibration factor is determined for the imaging system; this also compensates for the lens and camera optics. For a given distance, the calibration factor corrects the captured image and allows the actual height of the moving object in the image to be calculated. In the previous example, because we know how far the person is from the imager, and can thus apply the correct calibration factor, we can calculate their height correctly as 6 feet height regardless of how high they appear to be in the captured frame. This calibration factor may be a mathematical equation or a set of factors (one for each distance). For example, if one is using a set of factors to correct the images and if the objective of the system is cover a room 20 feet long, one calibration matrix would be required for all potential distances. Practically speaking, one may assume that 20 different matrices, one for every foot from the imaginer, can be used. 
     Based on the distance D(2), the appropriate calibration factor is applied to image R(2); this gives us a matrix, M( 2 ) that contains the height of all the moving objects in the frame. This process repeats as long as there is activity in the room, resulting in a series of matrices M(n), M(n+1), M(n+2), etc. that correspond to the heights of the moving objects in the room. These matrices are then analyzed for various predetermined events. 
     For example, if there is a resultant matrix at all we know there is activity—this event can be transmitted to the central processor for further analysis. If the matrix M(n) shows multiple moving objects, one can surmise there are multiple people in the room and hence visitors. 
     Subsequent matrices can be analyzed as a percentage of previous matrices to determine if a fall has occurred. For example, if matrix M(n) has a moving object of arbitrary height h in it, and matrix M(n+1) shows an object that is 20% of h, one may surmise that a fall has occurred. If the object in M(n+1) is at a higher percentage, for example 50%, one may assume the person has sat down in a chair. Conversely, if the M(n+1) is 200% of M(n), one may assume the person has stood up. If the sensor is known to be in a bedroom, similar logic can be used to determine if someone is getting into or out of bed. 
     The present features a system for detecting events in a predetermined space comprising an imager, configured for capturing one or more images of a predetermined space and for providing one or more image signals representing the captured one or more images of the predetermined space. The invention also features a range-finder, disposed proximate the imager, and configured for determining a distance of one or more objects located in the predetermined space from the imager, and for providing at least one distance signal. 
     A processor is coupled to the imager and the range-finder, and responsive to the captured one or more images of the predetermined space received from the imager and the at least one distance signal, and programmed to calibrate the captured one or more images of the predetermined space based on a predetermined calibration factor; analyze the calibrated captured one or more images of the predetermined space to determine if certain predetermined events have occurred in the predetermined space; and generate an output indicative of the determination that one or more of the certain predetermined events have occurred. 
     The system also includes a transmitting device, coupled to the processor and responsive to the processor generated output indicative of the determination that one or more of the certain predetermined events have occurred, for transmitting the output of the processor. 
     In one embodiment, the imager is a camera and the imager captures an image by capturing one of infrared or thermal energy. The imager may be a thermopile or a pyroelectric infrared (PIR) element. The rangefinder may be a radio-frequency (RF) range-finder or an optical range-finder. 
     The system image calibration factor may be selected from one or more calibration factors including a mathematical equation, a look up table and a matrix. 
     The system of claim  1 , wherein the events to be detected are selected from events consisting of activity, fall, sitting down, standing up, multiple people in the predetermined space and a button push. 
     The processor generated output may be one or more of a group of outputs including a wireless connection, a Wi-Fi output, a cellular output, a Bluetooth output, a wired connection output, an Ethernet output, a low-voltage alarm connection, a call to a nurse, a call to a family member, a light and an audible alarm. 
     The system processor may be programmed to analyze the calibrated captured one or more images to determine if the predetermined event is a person getting into or out of bed. 
     The invention also features a method for detecting events comprising the acts of capturing at least one image of a predetermined space using an imaging device determining the distance of one or more objects located in the predetermined space from the imaging device. A processor is programmed to receive the captured at least one image and the determined distance; calibrate the captured and received at least one image based on a predetermined calibration factor; analyze the calibrated image and responsive to the analyzing, determining if certain predetermined events have occurred in the predetermined space; generate an output responsive to the determining that certain predetermined events have occurred; and transmitting the output of the processor to a receiving device. 
     The invention also features a system for detecting events in a predetermined space comprising an imager, configured for capturing one or more images of a predetermined space and for providing one or more image signals representing the captured one or more images of the predetermined space and a range-finder, disposed proximate the imager, and configured for determining a distance of one or more objects located in the predetermined space from the imager, and for providing at least one distance signal. A sound capturing device is also provided in this embodiment and is configured for capturing at least one of a plurality of predetermined sounds in the predetermined space, and responsive to the capturing, for providing a captured sound signal indicative of the detection of at least one of the plurality of predetermined sounds in the predetermined space. 
     A processor is coupled to the imager, the range-finder and the sound capturing device, and responsive to the captured one or more images of the predetermined space received from the imager, the at least one distance signal and the captured sound signal, is programmed to: calibrate the captured one or more images of the predetermined space based on a predetermined calibration factor; analyze the calibrated captured one or more images of the predetermined space to determine if certain predetermined events have occurred in the predetermined space; analyze the captured sound signal; and responsive to the act of analyzing the calibrated captured one or more images of the predetermined space and analyzing the captured sound signal, determining that one or more of the certain predetermined events have occurred and generating an output indicative of the determination that one or more of the certain predetermined events have occurred. 
     A transmitting device is coupled to the processor and is responsive to the processor generated output indicative of the determination that one or more of the certain predetermined events have occurred, for transmitting the output of the processor. 
     In another embodiment, the invention features a system for detecting events in a predetermined space utilizing a sound capturing device, the system comprising a sound capturing device, configured for capturing at least one of a plurality of predetermined sounds in the predetermined space, and responsive to the capturing, for providing a captured sound signal indicative of the detection of at least one of the plurality of predetermined sounds in the predetermined space. 
     A processor is coupled to the sound capturing device, and responsive to the captured sound signal, is programmed to: analyze the captured sound signal; and responsive to the act of analyzing the captured sound signal, determines that one or more of the certain predetermined events have occurred and subsequently generates an output indicative of the determination that one or more of the certain predetermined events have occurred. A receiving device is coupled to the processor and responsive to the processor generated output indicative of the determination that one or more of the certain predetermined events have occurred, for receiving the output of the processor indicative that one or more of the certain predetermined events have occurred. 
     In yet another embodiment, the invention features a method for detecting events utilizing a sound capturing device wherein the method comprises the acts of capturing at least one of a plurality of predetermined sounds in the predetermined space, and responsive to the capturing, for providing a captured sound signal indicative of the detection of at least one of the plurality of predetermined sounds in the predetermined space. The method provides a processor, coupled to the sound capturing device, and responsive to the captured sound signal, is programmed to: analyze the captured sound signal and responsive to the act of analyzing the captured sound signal, determining that one or more of the certain predetermined events have occurred; and generating an output indicative of the determination that one or more of the certain predetermined events have occurred; and transmitting the output of the processor to a receiving device. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       These and other characteristics of the event system will be more fully understood by reference to the following detailed description in conjunction with the attached drawings, in which: 
         FIG. 1  is a schematic block diagram of the system according to the present invention; 
         FIGS. 2A-2C  represent side views of a room with the system of the present invention mounted to a wall within a room; 
         FIGS. 3A-3D  represent a set of matrices representing the images captured by the imager described in the present invention wherein  FIG. 3A  is a first image in which only a piece of furniture is in the room;  FIG. 3B  is a subsequent image of the same predetermined space and in which a person has entered the space;  FIG. 3C  is the resultant image of the subtraction of the images in  FIGS. 3A and 3B ; and  FIG. 3D  is an image wherein the person that entered the room in  FIG. 3B  has moved further away from the imager but such distance cannot be determined using solely the imager but must utilize the range-finder according to one aspect of the present invention; 
         FIGS. 4A-4D  are a set of output graphs representing the data returned by an ultrasonic range finder, wherein  FIG. 4A  is a first output;  FIG. 4B  is a subsequent output;  FIG. 4C  is the resultant output of the subtraction of the outputs of  FIGS. 4A  from  4 B; and  FIG. 4D  is illustrates the output from the range-finder of the present invention as applied to the person in  FIG. 3D  that has moved further away from the imager; 
         FIG. 5A  represents a room calibration matrix utilized to create a height calibration factor matrix for each position in a room; and  FIG. 5B  is a side view representation of a height pole used to generate the height calibration factors for a room; 
         FIG. 6A  is a resultant matrix of an image taken in a room;  FIG. 6B  is a matrix of the image of  FIG. 6A  to which the room calibration factors computed as described in connection with  FIG. 5  showing the computed actual height of the object in the room; 
         FIG. 7  is a flow chart describing the high-level processing steps of the system operating in accordance with the present invention; and 
         FIG. 8  is a flow chart describing the detailed processing steps of the present invention which are performed to determine events. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The present invention features and discloses a system and method that determines if certain events have occurred in an arbitrary space. The foundation of the system of the present invention is a pyro-electric sensor that detects activities—a souped up burglar alarm detector—capable of detecting motion, sound and/or distance; either all together, independently or in various combinations. By putting one of these sensors in each important room, the present invention can figure out where the elderly person (or other person of interest) is and how active they are in each room as a function of time. The recorded information is then stored and trended allowing the system to look for changes and issue alerts on events that might be problematic. For example, an increase in nighttime bathroom use across 2 nights typically means an elderly woman has a urinary tract infection). 
       FIG. 1  depicts an exemplary embodiment of such an event detection system  100  according to the teachings of the present invention. The illustrated system  100  includes an imager  101  which may be sensitive to visible, infrared or other energy. Imager  101  may be a standard imager such as a QVGA or VGA camera or it may be a low-resolution imager such as those used in optical mice. Regardless of the native resolution of the imager, the image may be processed to reduce its resolution such that images are obscured so as to not provide/disclose any personal information or identification data. For example, the image may be 32×32 pixels. Imager  101  may also have a lens  102  to enhance its field-of-view. For example, lens  102  may have a 180 degree view, a so-called “fish-eye” lens, to enable the imager  101  to capture images of an entire room. System  100  may also have an illuminator  109  which may create visible or infrared light to illuminate the field of view as necessary for the imager  101 . 
     System  100  also includes a range-finding device  103 . The range-finding device  103  may be based on sound-waves, such as ultrasound, radio frequency, such as ultra-wideband, or light, such as a laser. Imager  101  with its accompanying lens  102  and range-finder  103  may be functionally co-located to be in the same enclosure or is separate devices, located in close proximate to one another. Imager  101  and range-finder  103  are connected to processor  104  using appropriate interconnections  110  and  111  such as a serial bus or other practical means. It will be apparent to one having ordinary skill that there are a variety of means to interconnect the components of the system  100  without changing the form or function of the system. 
     Processor  104  is typically battery operated and contains memory  105  and is programmed to execute processing steps such as described in  FIGS. 7 and 8  to process the data obtained by imager  101  and range-finder  103  and determine if certain events have occurred. Data about these events, and/or other data as appropriate, may be sent by the processor  104  to other devices or systems through wireless link  106  or a wired link  108 . The wireless link  106  may be WiFi, cellular, UHF, optical, Bluetooth or other appropriate technology and may have a radio  106  or antenna  107 . The wired link  108  may be Ethernet, serial, low-voltage, contact closure, or other appropriate technology. Processor  104  may also have one or more visible and/or audible indicators such as LED  113  or a local or remotely activated audible alarm  114  to indicate various events. Processor  104  may also connect to various wired or wireless input devices  112  such as buttons or a keyboard. 
     An additional feature of the present invention is providing a microphone  115  integral with, in connection with or alternately in place of the image sensor  101  in any given room or space. The microphone listens only for very specific sounds. There are currently  8  sounds that are listened for. These include (but are not limited to) toilet flushes, water running, smoke alarm signals, door bells, microwave oven beeps, telephone rings, TV sounds and conversation in general. 
     For example, in the bathroom the system might listen for water running and toilet flushes or the absence of such sounds. In the example above, this sound sensing allows the system to determine that a person is using the sink or tub, taking a shower, or using the toilet. Using this sound information either alone or in connection with the image and range-finder information allows the system to more accurately detect events of interest and to distinguish events of interest from “normal” events that are not of concern. 
       FIGS. 2A-2C  depict a side view of a room  204  with the system  100  mounted to the left wall of the room. There are three different configurations of the room. In room  204   a ,  FIG. 2A , the system  100   a  is mounted on the left wall and there is a chair  203   a  and a table  202   a . In room  204   b    FIG. 2B , there is the same system  100   b  mounted on the wall, the chair  203   b  in the same location as depicted in  204   a  and the table  202   b , also in the same location. However, in room  204   b  a person  201   b  has entered the field. In room  204   c    FIG. 2C , the same system,  100   c , and the same stationary furniture chair  203   c , and table  202   c  are illustrated. In room  204   c , the person  201   c  has moved toward table  202   c  and away from system  100   c . For the sake of this description we will assume the person  201  walked straight away from sensor  100   c  and did not move in any other direction. 
       FIGS. 3A-3D  depict the 32×32 pixel images captured from the imager  100  in  FIG. 2 . Image  301   FIG. 3A  represents the view of the room depicted in room  204 A in  FIG. 2A  as seen by imager  100   a  wherein the tall chair  203   a  from  FIG. 2A  is shown in this image as  203   d . Note that this image capture is representative of step  701  in  FIG. 7 . The tall chair  203   a  overlaps the table  202   a  from  FIG. 2A  which is shown as  202   d  in image  301 ,  FIG. 3A . Note that the chair and table overlap, so the bottom part of both the chair and the table appear to be one object in image  301   FIG. 3A . 
     In  FIG. 3B  image  302  is a new image taken by system  100  (this corresponds to step  702  in  FIG. 7 ) and also corresponds to the room depicted as  204   b  in  FIG. 2B . In this representation, the imager  100  has again captured chair  203  and table  202  and these are shown as  203   e  and  202   e  respectively. However a person  201   e  has entered the frame (which is analogous to  201   b  in  FIG. 2B ). 
     When processing step  703  from  FIG. 7  is applied to images  301  and  302  in  FIGS. 3A and 3B , the resulting image is  303 ,  FIG. 3C . Note that the chair and table have both disappeared as they did not move and hence were “subtracted” out. The person  201   d  remains in the image however. If there was no change in the captured images the result of subtracting the two images  301  and  302  will be zero which means that there is no motion in the room and the system simply goes on to capture more images as depicted in step  710  in  FIG. 7 . 
     Image  304  in  FIG. 3D  shows the image  201   f  of a person depicted as  201 C in  FIG. 2C . When image  304  from  FIG. 3D  is compared to image  302  in  FIG. 3B , the person  201   f  is analogous to person  201   b  in  FIG. 2B  and has moved directly away from the imager but is in the same location in all the other dimensions as shown in  FIG. 2C . In reality, the image  201   f  in  FIG. 3D  should be slightly shorter than image  201   d  or  201   e  as the person  201  has moved farther away from the imager of the system  100   c , but the relatively low resolution of the imager  101  makes this difficult to discern and is the essential reason range-finder  103  is required in the system. Note that chair  203   f  and table  202   f  look the same as depicted in frames  301  and  302 . 
     One way to determine range is to use an ultrasonic range-finder as described in connection with range-finder  103  in  FIG. 1 . These are widely used for automotive parking systems so are readily available and relatively inexpensive.  FIGS. 4A-4D  show the data set that results when the ultrasonic range-finder  103  is part of system  100 . When the range-finder  103  sends out a “ping” or other device appropriate signal to assess the distance of objects from the sensor, the result is a set of data points that show the amplitude of the returned signal as a function of time, depicted as image  400   FIG. 4A . Since the speed of sound is known, a simple calculation of distance=rate*time that provides the bottom axis of  FIG. 4A  is also a measure of distance from the sensor  103  and imager  100 . 
     Graph  405   FIG. 4A  shows the data from a “ping” associated with image  204   a    FIG. 2A . Spike  401   a  corresponds to the table ( 202  in  FIGS. 2A-2C ) and spike  402   a  corresponds to the chair ( 203  in  FIGS. 2A-2C ). The chair  203  is larger in cross section, which causes more of the ultrasonic energy to be returned and hence spike  402  is larger than spike  401 . 
     Graph  406   FIG. 4B  shows a subsequent ping after a person  201  has moved into the field; this is analogous to the scenario depicted in image  204   b  in  FIG. 2B . In this case, there is a new spike  403   a  in the graph  406 . This signal is due to the new object in the room, the person  201 . Just as image frame (n+1) was subtracted from frame (n) to leave only the moving object in the result in  FIGS. 3A-3D , if data from graph  406  in  FIG. 4B  is subtracted from the data in graph  405   FIG. 4A , a single spike  403   b ,  FIG. 4C , is left depicted as shown in graph  407 . This is described as step  705  in  FIG. 7 . The spike  403   b  represents the distance between the moving object and the sensor. 
     In a similar fashion, graph  408   FIG. 4D  shows spike  404  which is the distance the person  201   c  is from the sensor in scene  204   c  in  FIG. 2C . Note that the amplitude of  404  is roughly the same as spike  403   b  as the person has the same basic cross-section, but the distance is farther, as depicted in  FIG. 2C  and thus the range-finder is used to complete the system&#39;s “view” into the room by being able to capture data in three dimensions namely, distance from the imager, and position in the X and Y dimension. 
     At this point in the processing, the system  100  has an image that contains only the moving object(s) in the room as well as accurate distance measurements of these objects(s). Next, based on the distance measurement, the calibration factors are applied to the image to determine the actual heights of the object(s) in the image. 
       FIGS. 5A and 5B  show one method for creating the calibration factors.  FIG. 5 a    depicts a room  501  of approximately 20 feet deep and 32 feet wide. It is understood that the actual size of the room is arbitrary and the 20×32 foot room in  FIG. 5A  is only one example. The distances in feet from the lower wall to the back wall are labeled  502  (the vertical axis) while the distances from the left to right walls are labeled  503  (horizontal axis). The event detection system  100 A from  FIG. 1  is mounted on the front wall, half way between the left and right walls, i.e. at location (0,16), represented by the black rectangle and is labeled  504 . 
       FIG. 5B  is a marker  505  that is eight feet tall with each foot of vertical height marked in a contrasting color,  506 . The marker is on wheels  507  which allows it to be easily moved. Marker  505  is manually moved to each 1 foot by 1 foot grid location in  FIG. 5A  and an image is captured by system  100 A of the marker in that location. This will result in 20×32 or 640 different images. Each of these images is then analyzed to create a location specific calibration factor that correlates the number of pixels captured by the imager in that grid location with each of the heights marked on marker  505  for each and every grid location. In other words, when the marker is in the center of the room at location (10,16) for example, the imager  101  may show that the 8 foot indicator on the marker corresponds to 32 pixels and the 4 foot indicator corresponds to 16 pixels. Therefore, at this given location, each pixel represents (8×12)/32=3 inches. In this example, each of the 640 calibration locations will have a unique calibration factor. One may create a matrix with 32 columns and 20 rows that contains these calibration factors; the rows of this matrix correspond to the distance an object is from the sensor and the columns correspond to where the object is with respect to the left or right of the sensor. It is understood that there are many methods of creating the calibration factors, including developing mathematical equations, convolutions, or other means. As long as the optical characteristics of imager  101  and lens  102  don&#39;t change, the calibration factors determined should apply to all situations where the system is deployed. This means that, assuming distance from the imager to the moving object (or any object in the room for that matter) is known, the appropriate row of the calibration factor matrix can be applied to the images captured to obtain an actual height of the objects. 
     The image of the object depicted in  FIG. 3B  as  201   d  can be simplified—if there is any data in a given cell it will be assigned a value of “1” and if there is no data it will be assigned a value of “0” as described in step  706  in  FIG. 7 . The resulting 32×32 image matrix is depicted at  601   a  in  FIG. 6A . For ease in illustration, the row and column numbers are noted as  602   a  and  603   a  respectively. Note that in  FIG. 6A , the actual image  604   a  is shaded simply to help the reader understand the method. 
     Based on the distance between the moving object and the system  100  that has been determined by range-finder  103 , the appropriate row of the calibration matrix can be selected. The calibration factors in each of the 32 columns can then be multiplied by the image matrix  601   a  in  FIG. 6A  as depicted in step  707  in  FIG. 7 . The result is a 32×32 matrix with the true height in inches of the object captured. This is depicted as  602  in  FIG. 6B . In the example given, the maximum height of the image is 72 inches, as shown in cells (6,26), (7,26) and (8,26) in  FIG. 6B . 
     For a single image, we now have a 32×32 matrix with the actual heights of objects that are in the field of the imager as depicted in  FIGS. 2 and 3 , this single image and its corresponding matrix  602  can be labeled (n). In reality there is a time sequence of these matrices; each matrix corresponds to one frame that is captured at a certain frame rate, which can be labeled n, n+1, n+2, n+3 . . . etc. so we also have a series of matrices. The matrices can then be compared one to the other which allows the system  100  to determine what is of interest namely, if a person has fallen, stopped moving and the like and to identify this as an “event”. 
       FIG. 7  shows the overall summary of the processing that occurs to create this series of matrices that can be analyzed for changes that correspond to events. Step  708  is further explained in  FIG. 8 . If the processing in  FIG. 8  reveals that an event being watched for has occurred, the event is outputted by the appropriate means such as by means of electronic signal, audible or visual means described above. 
       FIG. 8  is one means of analyzing the series of matrices  602  from  FIG. 6B . If matrix  602 ( n ) is non-zero, by definition there is motion in the room and this is the first event that is defined, as depicted in step  8 . 1 . Next, it is first determined how many moving objects are in the room. This is done by scanning the columns of matrix  602 ( n ) for maximum values (step  8 . 2 . 1 ) that are greater than 36″, (step  8 . 2 . 2 ). As shown in step  8 . 2 . 3 , if there are contiguous columns that have similar values, these columns are deemed to be part of a single figure. If the maximum value in a column drops below 36″, then raises again, this is deemed to be a second figure, step  8 . 2 . 4 ; this is how multiple figures or people in a single frame are detected. This continues until the number of figures, designated m, is determined in each frame n. 
     The maximum values for each of these figures is defined as max(m). If m&gt;1, then there is more than one figure in the room and an event of visitors is deemed true. 
     Each individual figure m, m+1, m+2, etc. in subsequent matrices n+1, n+2, n+3, etc. is analyzed (step  8 . 3 ) to see if the maximum height of an individual has decreased dramatically over a short period of time. In step  8 . 3 . 1 . 1 , it is checked to see if the maximum height of the figure has dropped below 24 inches. If it hasn&#39;t (step  8 . 3 . 1 . 1 . 1 ) it is determined that there is no fall and the process continues. If the figure has dropped below 24″, subsequent frames are analyzed in step  8 . 3 . 1 . 1 . 2  to determine if the height stays below 24 inches. After n+2 frames, if this is still the case, the event is defined as a fall. It should be noted that the absolute height of 24″ in arbitrary and presented here only as a representative example. A relative height, a percentage, or other appropriate means could also be used. 
     Step  8 . 3 . 2  determines if a figure has sat down in the frame. This occurs in a way similar to a fall except step  8 . 3 . 2 . 1  first tests to assure the figure is &gt;48″ (if it isn&#39;t,  8 . 3 . 2 . 2  continues) then  8 . 3 . 2 . 3  tests to see if the maximum value is subsequently less than 48″ but more than 24″; if this is the case it is determined that someone went from a standing to a sitting event. 
     Similar to  8 . 3 . 2 ,  8 . 3 . 3  determines if there is a transition from sitting to standing. Step  8 . 3 . 3 . 1  determines if the figure is between 24 and 48″ tall in frame n, then  8 . 3 . 3 . 3  determines if the figure becomes &gt;48″ tall; if this is the case, it is concluded that the figure has moved from a sitting to a standing event. 
     Numerous modifications and alternative embodiments of the present invention will be apparent to those skilled in the art in view of the foregoing description. Accordingly, this description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the best mode for carrying out the present invention. Details of the structure may vary substantially without departing from the spirit of the present invention, and exclusive use of all modifications that come within the scope of the appended claims is reserved. 
     Modifications and substitutions by one of ordinary skill in the art are considered to be within the scope of the present invention, which is not to be limited except by the allowed claims and their legal equivalents.