Respiration monitor

Information

  • Patent Grant
  • 10874332
  • Patent Number
    10,874,332
  • Date Filed
    Wednesday, November 21, 2018
    6 years ago
  • Date Issued
    Tuesday, December 29, 2020
    4 years ago
Abstract
A system for respiration monitoring includes a garment, which is configured to be fitted snugly around a body of a human subject, and which includes, on at least a portion of the garment that fits around a thorax of the subject, a pattern of light and dark pigments having a high contrast at a near infrared wavelength. A camera head is configured to be mounted in proximity to a bed in which the subject is to be placed, and includes an image sensor and an infrared illumination source, which is configured to illuminate the bed with radiation at the near infrared wavelength, and is configured to transmit a video stream of images of the subject in the bed captured by the image sensor to a processor, which analyzes movement of the pattern in the images in order to detect a respiratory motion of the thorax.
Description
FIELD OF THE INVENTION

The present invention relates generally to sleep monitoring, and particularly to apparatus, systems and methods for video-based monitoring of a sleeping infant.


BACKGROUND

Video-based sleep monitors for infants are known in the art. For example, U.S. Pat. No. 8,922,653 describes a crib mobile and surveillance system which communicates video data captured by a camera within a mobile member housing, and sounds received by a microphone disposed in a base, to a handheld monitor. The video data are displayed and broadcast in real time on a monitor screen on the handheld monitor to remotely monitor a child lain in a crib having the present device.


U.S. Pat. No. 9,530,080, whose disclosure is incorporated herein by reference, describes systems and methods for monitoring babies with cameras using a centralized computation and storage center that allows using visual output signals for computer vision and machine learning analysis and high-level reasoning of baby movements. The system comprises a camera located at a predefined working point above a baby's crib, and one or more communication networks between components of the system including a web-based network for in-depth computer vision and machine learning analysis of the visual output signals by an analysis server.


PCT International Publication WO 2017/196695, whose disclosure is incorporated herein by reference, describes a video monitoring system, which includes a camera head, including an infrared illumination source and an image sensor. A mount is configured to hold the camera head in a fixed location and orientation above a crib, so that the image sensor captures images of the crib and an intervention region adjacent to the crib from a fixed perspective.


As another example, U.S. Patent Application Publication 2013/0342693 describes a video-enabled baby monitoring system including a transmitter with a camera feature, which captures motion and includes microprocessors that generate a series of video signal codes, which are transmitted at specific radio frequencies to a dedicated receiver unit. The transmitter unit also includes an infrared light source and a sound capture source, wherein the sound capture source generates sound signal codes. Another unit provides for enhanced, convenient data transfer from the transmitter unit and may be selected from a number of adaptor docking stations; or a smart phone platform; or a docking cradle with Wi-Fi capability.


Systems for monitoring the breathing of a sleeping person are also known in the art. For example, U.S. Patent Application Publication 2004/0005083 describes a method and system for monitoring breathing movement of an infant and for detecting and predictably estimating regular cycles of breathing movements. Another disclosed aspect of the invention is directed to detect and report irregularity of breathing activity of an infant, such as cessation and non-periodicity, which suggests a likelihood of SIDS.


U.S. Patent Application Publication 2015/0105670 relates measurement of vital signs such as a respiratory rate or a heart rate. In particular, a system for determining a vital sign of a subject comprises an imaging unit for obtaining video data of the subject, a marker directly or indirectly attached to a body of the subject, wherein the marker comprises a graphical pattern, an image processing unit for detecting said marker in said video data, and an analysis unit adapted to extract a vital sign parameter related to the vital sign of the subject from said video data and to determine the vital sign from said vital sign parameter.


U.S. Patent Application Publication 2016/0345832 describes a system and method for monitoring biological status through contactless sensing that includes a sensing device with at least one video imaging device; a sensor data processing unit, wherein the sensor processing unit when in a respiratory sensing mode is configured to extract a set of primary components of motion from image data from the video imaging device; a biological signal processor that when in a respiratory sensing mode is configured to identify at least one dominant component of motion and generate a respiratory signal; and a monitor system.


SUMMARY

Embodiments of the present invention that are described hereinbelow provide systems and methods for monitoring respiration of a sleeping infant or other human subject.


There is therefore provided, in accordance with an embodiment of the invention, a system for respiration monitoring, including a garment, which is configured to be fitted snugly around a body of a human subject, and which includes, on at least a portion of the garment that fits around a thorax of the subject, a pattern of light and dark pigments having a high contrast at a near infrared wavelength. A camera head is configured to be mounted in proximity to a bed in which the subject is to be placed, and includes an image sensor and an infrared illumination source, which is configured to illuminate the bed with radiation at the near infrared wavelength, and is configured to transmit a video stream of images of the subject in the bed captured by the image sensor to a processor, which analyzes movement of the pattern in the images in order to detect a respiratory motion of the thorax.


In some embodiments, the human subject is an infant, the bed is a crib, and the garment includes a swaddling cloth. In one embodiment, the swaddling cloth is configured as a sack, having a size and shape suitable for containing the body of the infant. Alternatively, the swaddling cloth is configured as a band, having a size and shape suitable to surround the thorax of the infant.


In a disclosed embodiment, the pattern extends around both a front and a back of the thorax when the garment is fitted around the body of the subject.


In some embodiments, the pattern includes an arrangement of light or dark geometrical shapes, which repeat across at least the portion of the garment, and the processor is configured to identify and track the geometrical shapes in the images in order to analyze the movement. In a disclosed embodiment, the shapes have respective corners, and the processor is configured to identify and track the corners in the images in order to analyze the movement.


Additionally or alternatively, the processor is configured to calibrate a scale of the images responsively to a distance between the repeating shapes. In one embodiment, the processor is configured to apply the scale in measuring the respiratory motion. Additionally or alternatively, the processor is configured to apply the scale in measuring one or more dimensions of the subject.


In a disclosed embodiment, the processor is configured to identify, responsively to the movement of the pattern, a deviation of at least one of an amplitude and a rate of the respiratory motion from a predefined normal range, and to issue an alert in response to the deviation.


There is also provided, in accordance with an embodiment of the invention, a method for respiration monitoring, which includes providing a garment, which is configured to be fitted snugly around a body of a human subject, and which includes, on at least a portion of the garment that fits around a thorax of the subject, a pattern of light and dark pigments having a high contrast at a near infrared wavelength. A bed in which the subject is placed is illuminated with radiation at the near infrared wavelength. A video stream of images of the subject in the bed is captured while illuminating the bed at the near infrared wavelength. Movement of the pattern in the images is analyzed in order to detect a respiratory motion of the thorax.


The present invention will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings in which:





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a block diagram that schematically illustrates a system for infant sleep monitoring, in accordance with an embodiment of the invention;



FIG. 2 is a schematic top view of a sleep monitoring device mounted over an infant's crib, in accordance with an embodiment of the invention;



FIGS. 3A and 3B are schematic front and back views, respectively, of a swaddling sack used in monitoring respiration of an infant, in accordance with an embodiment of the invention;



FIGS. 4A and 4B are schematic outer and inner views, respectively of a swaddling band used in monitoring respiration of an infant, in accordance with another embodiment of the invention; and



FIG. 5 is a flow chart that schematically illustrates a method for respiration monitoring, in accordance with an embodiment of the invention.





DETAILED DESCRIPTION OF EMBODIMENTS
Overview

Concern over proper respiration leads many parents of young infants to monitor their child's nighttime breathing. A wide range of product offerings exist for this purpose, but they tend to be uncomfortable, unreliable, or both.


Embodiments of the present invention that are described herein offer a new solution to the problem of infant respiration monitoring, using a novel garment, which facilitates both reliable detection of respiratory motion and infant comfort, while fitting snugly around the infant's body. (“Snug” in this context means that the garment fits tightly enough so that the cloth will expand and contract as the infant's thorax expands and contracts in respiration, without being so tight as to constrict respiratory motion.)


Substantially any suitable type of garment can be used for the present purposes. By way of example, the embodiments described below refer specifically to a swaddling cloth, such as a swaddling sack, having a size and shape suitable for containing the entire body of the infant, or a swaddling band, which fits around the thorax. The use of a swaddling cloth is advantageous in promoting both sound sleep and reliable monitoring. The principles of the present invention, however, are by no means limited to this type of garment, and may alternatively be applied in monitoring the respiration of other subjects, including older children, as well as adults.


Regardless of the specific configuration, the garment comprises a pattern of light and dark pigments on at least a portion of the garment that fits around the thorax of the subject. The light and dark pigments are chosen so that the pattern has a high contrast at a near infrared (NIR) wavelength, meaning specifically a wavelength between 800 nm and 1500 nm. This wavelength range is useful due to the availability of intense, inexpensive solid-state light sources, such as light-emitting diodes (LEDs), and the sensitivity of common image sensors in this range, while being invisible to human eyes. The terms “light” and “dark” in relation to the pigments mean, in the context of the present description and in the claims, that the “light” pigment reflects at least 50% of the incident radiation at a chosen illumination wavelength in the NIR, while the “dark” pigment reflects less than 20% of the incident light at this wavelength. The term “high contrast” in this context means that when the pattern is illuminated uniformly at the chosen wavelength, the intensity of reflected light received by the image sensor from the light pigment is at least twice that received from the dark pigment.


In the disclosed embodiments, a camera head is mounted in proximity to a bed in which the subject is to be placed. The camera comprises an image sensor and an infrared illumination source, which illuminates the crib at a chosen wavelength in the NIR. The image sensor captures images of the subject in the bed (for example, of an infant in a crib, as shown in the figures that are described below). The camera head transmits a video stream of these images to a processor, which analyzes movement of the pattern in the images in order to detect the respiratory motion of the thorax. When the processor detects a deviation of the amplitude and/or rate of the respiratory motion from a predefined normal range, it issues an alert and thus enables a caregiver to intervene immediately. The garment can be designed so that the pattern extends around both the front and the back of the thorax; the respiratory motion can thus monitored reliably regardless of whether the subject is lying on his or her front, back or side. Alternatively, the pattern may appear only the front of the thorax.


In some embodiments, the pattern comprises an arrangement of light or dark geometrical shapes, which repeat periodically across at least the portion of the garment that extends across the thorax. This sort of pattern facilitates fast and accurate processing of the images by the processor. For example, the processor can identify corners of the geometrical shapes and track these corners in the images in order to analyze the movement of the pattern.


When the actual, physical sizes of the shapes on the swaddling cloth are known, the processor can calibrate the scale of the images based on the distance (in pixels) between the repeating shapes in the images, and can then apply this scale in measuring the respiratory motion and/or measuring one or more dimensions of the subject. These latter measurements include, but are not limited to, the distance an infant being monitored traveled in a crib, the distance crawled, the magnitude of head movements, the magnitude of limb movements, the magnitude of twitches, head circumference, head shape, head volume, height, body volume, weight and BMI estimations, limb length, distance between the eyes, forehead height, general facial dimensions, shoulder width, hip width, hip angles, foot angles, torso length. The calibrated scale can also be used to improve object detection algorithms by normalizing the image size to minimize the size tolerance of detection of objects such as the crib, the infant, and its head.


Although the figures and the embodiments described below refer to certain specific swaddling cloth designs, with a certain pattern shown by way of example, other sorts of garments and other patterns that can be used for the present purposes will be apparent to those skilled in the art after reading the present description. All such alternative embodiments are considered to be within the scope of the present invention.


System Description


FIG. 1 is a block diagram that schematically illustrates a system 20 for infant sleep monitoring, in accordance with an embodiment of the invention. System 20 comprises a monitoring camera head 22, which is mounted in a fixed location and orientation above a crib 24, in which an infant 26 is sleeping in a residence 28. Alternatively, camera head 22 may be mounted in another suitable location in proximity to the crib, mounted on a wall or tabletop, for example.


For purposes of image capture, an infrared (IR) light-emitting diode (LED) 25 on the lower side of camera head 22 illuminates the sleeping infant 26. A diffuser can be used to spread the infrared light uniformly across the crib. Camera head 22 also comprises an infrared-sensitive image sensor 23, which may conveniently have a standard 4:3 aspect ratio to fit the size of a standard crib 24. The resolution and sensitivity of image sensor 23 can be optimized for night conditions, and specifically for the wavelength range of LED 25. Further details of camera head 22, including its internal components and modes of operation, are described in the above-mentioned PCT International Publication WO 2017/196695 (particularly in FIGS. 4A/B and 5 and the corresponding description in the specification on pages 8-9). This PCT publication also describes different ways of mounting the camera head above or alongside the crib.


Camera head 22 transmits digitized streaming video, and possibly other signals, as well, over a local network to a router 30, typically via a wireless local area network (LAN) link, such as a Wi-Fi connection, or a wired link, such as an Ethernet connection. Camera head 22 transmits the digitized video data in packets that are addressed so that router 30 forwards the video packets both to a local client device 32 on the local network and via a public network 36, such as the Internet, to a remote server 38. Client device 32 typically comprises a smart phone, tablet or personal computer, which enables a caregiver 34 in another room of residence 28 to monitor infant 26, even when there is no Internet connection available. Server 38 makes video images and other data available to authorized remote client devices 44, thus enabling a caregiver 46 to monitor infant 26 at any location where there is access to public network 36. The Wi-Fi or other local network connection provides reliable video streaming from camera head 22 to client device 32 with high bandwidth and low latency, even if the external Internet connection is not working. As long as the Internet is connected, however, the video stream is also transmitted to server 38 for purposes of analysis and retransmission.


Server 38 typically comprises a general-purpose computer, comprising a processor 40 and a memory 42, which receives, stores and analyzes images from camera head 22 in residence 28 and similarly from other cameras in other residences (not shown). In the present embodiment, processor 40 analyzes the images in order to detect and measure respiratory motion of the thorax of infant 26, and to provides caregivers 34 and 46 with reports and (when required) alerts regarding the infant's breathing patterns. Processor 40 typically performs these functions under the control of software, which may be downloaded to server 38 in electronic form, over a network, for example, as well as stored on tangible, non-transitory computer-readable media, such as magnetic, optical or electronic memory media. Alternatively or additionally, some or all of these processing and monitoring functions may be performed locally, for example by a microprocessor in camera head 22 and/or by suitable application software running on processors in client devices 32 and/or 44.



FIG. 2 is a schematic top view showing details of the deployment and use of monitoring camera head 22 over crib 24, in accordance with an embodiment of the invention. Infant 26 in crib 24 is wrapped in a swaddling sack 52, with a periodic pattern printed on a portion 54 of the swaddling sack that fits around the infant's thorax. In this embodiment, monitoring camera head 22 stands against a wall over crib 24. Camera head 22 is held, for example, at the end of an arm at the upper end of a tripod mount behind crib 24, at the midpoint of the long side of the crib. Camera head 22 in this embodiment is positioned and adjusted so that the camera head has a field of view 50 from a fixed perspective that encompasses at least the area of crib 24. This perspective provides server 38 with image information that can be analyzed conveniently and reliably to detect respiratory motion of infant 26. Alternatively, the camera head may be mounted in any other suitable location in proximity to crib 24 that gives a view of the infant that is suitable for monitoring movement of the pattern on the swaddling cloth or other garment.



FIGS. 3A and 3B are schematic front and back views, respectively, of swaddling sack 52, which is used in monitoring the respiration of infant 26, in accordance with an embodiment of the invention. Before putting infant 26 into crib 24, the caregiver inserts the infant into swaddling sack 52, and then fastens a band 58 snugly around the infant's thorax. Hook and loop patches 60 and 62 (such as Velcro® patches) may be used for this purposes, or any other suitable type of fastener that is known in the art.


Portion 54 of swaddling sack 52 comprises a periodic pattern of light and dark pigments, extending around both the front and back of sack 52, with a high contrast at the near infrared wavelength of LED 25. In this example, the pattern comprises two types of dark geometrical shapes 56, but alternatively other patterns may be used, with one or more different shapes, in either dark or light pigment. The sharp corners of the shapes in this example facilitate detection and tracking of the shapes by an image processor for the purpose of detecting respiratory motion. The dimensions of shapes 56 are typically between 5 and 50 mm, and camera head is designed so that each shape will have an extent between 3 and 30 pixels in the images captured by image sensor 23, although larger or smaller shapes and magnifications may alternatively be used.



FIGS. 4A and 4B are schematic outer and inner views, respectively, of a swaddling band 70 used in monitoring respiration of an infant, in accordance with another embodiment of the invention. The size and shape of band 70 are suitable to surround the thorax of an infant, with the band fastened snugly around the thorax by hook and loop patches 72 and 74. Band 70 leaves the infant free to move his or her arms and legs, in contrast to sack 52. The pattern of shapes 56 on band 70 is similar to that on sack 52, as described above.


Monitoring of Respiratory Motion


FIG. 5 is a flow chart that schematically illustrates a method for respiration monitoring, in accordance with an embodiment of the invention. This method is described hereinbelow, for the sake of concreteness and clarity, with reference to the elements of system 20 and swaddling sack 52, as shown and described above. Alternatively, the method may be applied, mutatis mutandis, in other system configurations and/or using other sorts of swaddling cloth, as noted earlier. Specifically although certain image processing functions and communication functions are described below as being carried out by server 38, at least some of these functions may be carried out by other means, such as client devices 32 and 44 and/or a processor (not shown) that is embedded in camera head 22.


Processor 40 initiates the method of FIG. 5 upon receiving a video stream from camera head 22 and a signal (for example from the camera head or from client device 32) indicating that monitoring is to begin. Processor 40 finds a region of interest (ROI) that contains a part of the pattern on swaddling sack 52 in an initial image frame, at an ROI identification step 80. The ROI may be identified automatically by analyzing the image to locate the pattern on swaddling sack 52 (or swaddling band 70, for example). Processor 40 may access features of the pattern or patterns that are used on various types of swaddling cloth, such as the sizes and forms of shapes 56, in memory 42, in order to identify the ROI more easily. Alternatively or additionally, the caregiver may indicate the location of the ROI to the processor, for example by pointing to the ROI in an image from camera head 22 that is displayed on client device 32.


In some embodiments, processor 40 finds the locations of the corners of shapes 56 in the ROI on swaddling sack 52, at a corner finding step 82. (“Corners” are not necessarily right angles, and can be formed that the meeting point of edges at different angles, for example angles ranging between 60° and 120°.) Corners can be found by filtering the part of the image that is contained in the ROI with filters that are matched to the geometrical features of the pattern, for example. Once the corners are found, processor 40 can calibrate the scale of the images by comparing the distance between the repeating shapes in the images to the known distance between the geometrical features on the swaddling sack.


Once the ROI and pattern features have been identified in the initial image, processor 40 receives and processes succeeding images in the video stream, at an image input step 84. In each image, the processor tracks the movement of each of the corners in the ROI that was identified previously. This step may use techniques such as feature mapping or the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm in order to measure motion from frame to frame with sub-pixel accuracy. If the ROI is lost in a given image or sequence of images, for example due to large movements of infant 26, processor 40 may search over an entire image in order to recover the location of the ROI and may then return to step 80.


Alternatively or additionally, processor 40 may apply other sorts of image processing algorithms in identifying and tracking movement of the pattern, not necessarily depending on corners. For example, template shift registration can be used to track shapes having no clear corners (such as circles). As another example, normal flow algorithms can be used in tracking object edges.


Since respiratory movement from one frame to the next is very small, processor 40 averages the motion of each corner (or other feature) over multiple successive frames and/or multiple feature locations, at a movement averaging step 88. When infant 26 is breathing, the infant's thorax expands and contracts periodically, so that all of the corners within a given ROI on swaddling sack 52 will move in the same direction from frame to frame, with a temporal variation that is approximately sinusoidal. The average movement will therefore be substantial. (The ROI is chosen at step 80 to be large enough to contain multiple shapes 56, but small enough so that all the shapes in the ROI move in the same direction during respiration.) On the other hand, when infant 26 is not breathing, the directions of movement of the different corners will be random and will thus cancel out upon averaging. Processor 40 may also identify large movements that are not periodic as corresponding to the infant moving in his or her sleep or being awake.


Processor 40 analyzes the averaged movement signal over time in order to detect periodic motion that is characteristic of respiration, at a movement analysis step 90. For this purpose, processor 40 may filter the averaged signal using a set of bandpass filters with frequency bands distributed through the range of normal respiration frequencies. For example, processor 40 may apply a set of six bandpass filters that together span the normal breathing range, which is about 20-60 breaths/minute for infants under one year old. Any suitable type of bandpass filter with sufficiently sharp edges may be used for this purpose, for instance a fifth-order Butterworth filter, as is known in the art. Alternatively, larger or smaller numbers of filters, of this or other types, may be used, depending upon the available computing power and the level of precision that is required to avoid false alarms.


After filtering the signal at step 90, processor 40 chooses the frequency band with the highest amplitude, at a band selection step 92. Processor 40 compares this amplitude to a preset threshold, at a motion checking step 94. If the amplitude is below threshold, and remains below threshold for longer than a preset time limit, processor 40 issues an alert, at an alarm step 96. Infants may normally stop breathing for short periods, typically up to 20 sec, and then resume breathing normally. The time limit at step 94 may thus be set, for example, to 20 sec. If processor 40 does not detect respiratory motion of sufficient amplitude over a period longer than this limit, it may send a notification to client device 32 at step 96, for instance, or take more drastic action, such as triggering a visible and/or audible alarm in residence 28. For example, processor 40 may activate a visible light and/or audio speaker in camera head 22.


When processor 40 detects respiratory motion of sufficient amplitude at step 94, it also checks the infant's respiratory frequency, at a rate checking step 98. If the highest motion amplitude was found to be in the highest or lowest frequency band of the filters applied at step 92, for example, the processor may conclude that the breathing rate is abnormally high or low. In this case, too, processor 40 will issue an alert at step 96. Otherwise, the processor concludes that the infant's breathing is normal and continues to capture and analyze further images as described above. Optionally, processor 40 may continually issue updates to client devices 32 and 44, even when infant 26 is breathing normally, to indicate the breathing status and respiratory rate of the infant.


As noted earlier, although the embodiments described above relate specifically to infant respiration monitoring, using a patterned swaddling cloth, the principles of the present invention may similarly be applied using other sorts of garments with suitable pattern, not only for infants but also for older subjects. It will thus be appreciated that the embodiments described above are cited by way of example, and that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and subcombinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art.

Claims
  • 1. A system for respiration monitoring, comprising: a band, having a size and shape suitable to surround and be fastened snugly around a thorax of an infant, and which comprises a pattern of light and dark pigments having a high contrast at a near infrared wavelength and extending around a front, a back and sides of the thorax, while leaving the infant free to move his or her arms and legs; anda camera head, which is configured to be mounted in proximity to a bed in which the infant is to be placed, and which comprises an image sensor and an infrared illumination source, which is configured to illuminate the bed with radiation at the near infrared wavelength, and which is configured to transmit a video stream of images of the infant in the bed captured by the image sensor to a processor, which analyzes movement of the pattern in the images in order to detect a respiratory motion of the thorax regardless of whether the infant is lying on his or her front, back or side.
  • 2. The system according to claim 1, wherein the pattern comprises an arrangement of light or dark geometrical shapes, which repeat across at least the portion of the garment, and wherein the processor is configured to identify and track the geometrical shapes in the images in order to analyze the movement.
  • 3. The system according to claim 2, wherein the shapes have respective corners, and wherein the processor is configured to identify and track the corners in the images in order to analyze the movement.
  • 4. The system accordingly to claim 2, wherein the processor is configured to calibrate a scale of the images responsively to a distance between the repeating shapes.
  • 5. The system according to claim 4, wherein the processor is configured to apply the scale in measuring the respiratory motion.
  • 6. The system according to claim 4, wherein the processor is configured to apply the scale in measuring one or more dimensions of the infant.
  • 7. The system according to claim 1, wherein the processor is configured to identify, responsively to the movement of the pattern, a deviation of at least one of an amplitude and a rate of the respiratory motion from a predefined normal range, and to issue an alert in response to the deviation.
  • 8. A method for respiration monitoring, comprising: providing a band, having a size and shape suitable to surround and be fastened snugly around a thorax of an infant, and which comprises a pattern of light and dark pigments having a high contrast at a near infrared wavelength and extending around a front, a back and sides of the thorax, while leaving the infant free to move his or her arms and legs; andilluminating a bed in which the infant is placed with radiation at the near infrared wavelength;capturing a video stream of images of the infant in the bed while illuminating the bed at the near infrared wavelength; andanalyzing movement of the pattern in the images in order to detect a respiratory motion of the thorax regardless of whether the infant is lying on his or her front, back or side.
  • 9. The method according to claim 8, wherein the pattern comprises an arrangement of light or dark geometrical shapes, which repeat across at least the portion of the swaddling garment, and wherein analyzing the movement comprises identifying and tracking the geometrical shapes in the images.
  • 10. The method according to claim 9, wherein the shapes have respective corners, and wherein identifying and tracking the geometrical shapes comprises identifying and tracking the corners in the images.
  • 11. The method accordingly to claim 9, wherein identifying and tracking the geometrical shapes comprises calibrating a scale of the images responsively to a distance between the repeating shapes.
  • 12. The method according to claim 11, wherein analyzing the movement comprises applying the scale in measuring the respiratory motion.
  • 13. The method according to claim 11, and comprising applying the scale in measuring one or more dimensions of the infant.
  • 14. The method according to claim 8, wherein analyzing the movement comprises identifying, responsively to the movement of the pattern, a deviation of at least one of an amplitude and a rate of the respiratory motion from a predefined normal range, and issuing an alert in response to the deviation.
  • 15. The method according to claim 11, and comprising analyzing the movement so as to measure a distance traveled by the infant in the bed.
  • 16. The system according to claim 1, wherein the pattern is printed over an entire area of the band.
  • 17. The system according to claim 1, wherein the band comprises hook and loop patches, which fasten the band snugly around the thorax of the infant.
CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Patent Application 62/589,587, filed Nov. 22, 2017, which is incorporated herein by reference.

US Referenced Citations (206)
Number Name Date Kind
D220534 Selden et al. Apr 1971 S
3938500 Simmons Feb 1976 A
4047684 Kobayashi Sep 1977 A
4240603 Chiariello Dec 1980 A
D268458 Schoenig et al. Apr 1983 S
4561339 Jensen Dec 1985 A
D289835 Schoenig et al. May 1987 S
4712756 Kester et al. Dec 1987 A
D302652 Spaeth, Jr. Aug 1989 S
D314873 Wenger et al. Feb 1991 S
5032919 Randmae Jul 1991 A
5295490 Dodakian Mar 1994 A
D345905 Hallgren Apr 1994 S
5446548 Gerig et al. Aug 1995 A
5692719 Shepherd et al. Dec 1997 A
5914660 Mesibov Jun 1999 A
5996814 Workman et al. Dec 1999 A
D421447 Eason et al. Mar 2000 S
6113455 Whelan et al. Sep 2000 A
D450339 Eason et al. Nov 2001 S
6991384 Davis Jan 2006 B1
7035432 Szuba Apr 2006 B2
D519990 Lazor May 2006 S
7177386 Mostafavi Feb 2007 B2
D540564 Tai et al. Apr 2007 S
D552659 Stephens et al. Oct 2007 S
D553848 Barker et al. Oct 2007 S
7277122 Sakai Oct 2007 B2
D557035 Huang et al. Dec 2007 S
D557320 Fisher et al. Dec 2007 S
D559090 Nawrocki Jan 2008 S
7318051 Weston et al. Jan 2008 B2
7397380 Smolsky Jul 2008 B1
D574159 Howard Aug 2008 S
7470167 Clark Dec 2008 B2
D585395 Cho et al. Jan 2009 S
7477285 Johnson Jan 2009 B1
7624074 Weston et al. Nov 2009 B2
D606106 Kim et al. Dec 2009 S
D614223 Kim et al. Apr 2010 S
7696888 Swan et al. Apr 2010 B2
7720259 Gordon May 2010 B2
7774032 Swan et al. Aug 2010 B2
D624108 Wang et al. Sep 2010 S
D624109 Wang et al. Sep 2010 S
D627815 Oba et al. Nov 2010 S
7827631 Holman Nov 2010 B2
7905667 Barker Mar 2011 B2
D635940 Cho et al. Apr 2011 S
D640692 Waisman-Diamond Jun 2011 S
D644450 Walter et al. Sep 2011 S
D645466 Woo et al. Sep 2011 S
D647866 Chen et al. Nov 2011 S
D649945 Kim et al. Dec 2011 S
D657977 Belitz Apr 2012 S
D659690 Huang et al. May 2012 S
8218871 Angell et al. Jul 2012 B2
D676005 Wood et al. Feb 2013 S
D685355 Holleman et al. Feb 2013 S
8461996 Gallagher Jun 2013 B2
8471899 Johnson Jun 2013 B2
8484774 Cohen Jul 2013 B2
8539620 Wynh Sep 2013 B1
D692939 Huang et al. Nov 2013 S
8638364 Chen et al. Jan 2014 B2
8640280 Gutierrez Feb 2014 B2
8646126 Carta Feb 2014 B2
8663126 Al Thalab Mar 2014 B1
8675059 Johnson et al. Mar 2014 B2
8676603 Johnson et al. Mar 2014 B2
8836751 Ballantyne et al. Sep 2014 B2
D719153 Lim et al. Dec 2014 S
D720384 Holmen et al. Dec 2014 S
8922653 Reeve Dec 2014 B1
D722637 Baty et al. Feb 2015 S
8953674 Henson Feb 2015 B2
D724462 Bould et al. Mar 2015 S
D727388 Huang et al. Apr 2015 S
D733780 Chen et al. Jul 2015 S
9075290 Thieman Jul 2015 B1
D741932 Huang et al. Oct 2015 S
D742770 Windstrup et al. Nov 2015 S
9191629 Lee Nov 2015 B2
D746350 Li et al. Dec 2015 S
9215428 Babineau et al. Dec 2015 B2
D746709 Heath et al. Jan 2016 S
9268465 Yari Feb 2016 B1
D750992 Perez et al. Mar 2016 S
D754234 Lee et al. Apr 2016 S
D755876 Moss et al. May 2016 S
9330343 Nakano May 2016 B2
D759012 Golden et al. Jun 2016 S
D759621 Maxwell et al. Jun 2016 S
D765756 Liu et al. Sep 2016 S
9445008 Yamazaki Sep 2016 B2
D768015 Yang et al. Oct 2016 S
D771175 Choi et al. Nov 2016 S
D773948 Schneid et al. Dec 2016 S
9530080 Glazer Dec 2016 B2
D778192 Bolger et al. Feb 2017 S
D788207 Glazer et al. May 2017 S
D793996 Katz et al. Aug 2017 S
9721180 Prasad et al. Aug 2017 B2
D798365 Glazer et al. Sep 2017 S
D798366 Glazer et al. Sep 2017 S
D803289 Glazer et al. Nov 2017 S
D821479 Cabral et al. Jun 2018 S
D822641 Belitz Jul 2018 S
D824681 Vaughn Aug 2018 S
D837222 Janzen et al. Jan 2019 S
20020097155 Cassel Jul 2002 A1
20030057749 Buono Mar 2003 A1
20030233806 Kuebler et al. Dec 2003 A1
20040005083 Fujimura et al. Jan 2004 A1
20040005088 Jeung Jan 2004 A1
20040082874 Aoki Apr 2004 A1
20050034485 Klefstad-Sillonville Feb 2005 A1
20050065655 Hong et al. Mar 2005 A1
20050069207 Zakrzewski et al. Mar 2005 A1
20050119560 Mostafavi Jun 2005 A1
20050244094 Allsop Nov 2005 A1
20050285941 Haigh et al. Dec 2005 A1
20060028656 Venkatesh Feb 2006 A1
20060109375 Ho et al. May 2006 A1
20060262966 Eck Nov 2006 A1
20070058039 Clark Mar 2007 A1
20070073122 Hoarau Mar 2007 A1
20070076935 Jeung et al. Apr 2007 A1
20070133975 Lin Jun 2007 A1
20070156060 Cervantes Jul 2007 A1
20070177792 Ma et al. Aug 2007 A1
20070200930 Gordon Aug 2007 A1
20070285259 Desrosiers et al. Dec 2007 A1
20070285570 Desrosiers et al. Dec 2007 A1
20080011344 Barker Jan 2008 A1
20080016624 Osborn Jan 2008 A1
20080077020 Young Mar 2008 A1
20080106421 Adams May 2008 A1
20080107305 Vanderkooy et al. May 2008 A1
20080146911 Miyake Jun 2008 A1
20080180537 Weinberg et al. Jul 2008 A1
20080309765 Dayan et al. Dec 2008 A1
20090066671 Kweon et al. Mar 2009 A1
20090091617 Anderson Apr 2009 A1
20090278934 Ecker et al. Nov 2009 A1
20100060448 Larsen et al. Mar 2010 A1
20100134609 Johnson Jun 2010 A1
20100182136 Pryor Jul 2010 A1
20100202659 Hamalainen Aug 2010 A1
20100241018 Vogel Sep 2010 A1
20110044533 Cobb Feb 2011 A1
20110118608 Lindner et al. May 2011 A1
20110129210 McGucken Jun 2011 A1
20110230115 Wang et al. Sep 2011 A1
20110261182 Lee et al. Oct 2011 A1
20110295583 Hollack et al. Dec 2011 A1
20110310247 Rensin Dec 2011 A1
20110313325 Cuddihy Dec 2011 A1
20120002045 Tony et al. Jan 2012 A1
20120062735 Rivera Mar 2012 A1
20120069193 Ramegowda Mar 2012 A1
20120075464 Derenne et al. Mar 2012 A1
20130072823 Kahn et al. Mar 2013 A1
20130123639 Ando May 2013 A1
20130144178 Halperin et al. Jun 2013 A1
20130169735 Barker Jul 2013 A1
20130182107 Anderson Jul 2013 A1
20130241730 Saitwal et al. Sep 2013 A1
20130250063 Lee et al. Sep 2013 A1
20130296716 Kurzenberger Nov 2013 A1
20130342691 Lewis Dec 2013 A1
20130342693 Lee Dec 2013 A1
20140046231 Barlow Feb 2014 A1
20140070957 Longinotti-Buitoni Mar 2014 A1
20140072206 Eaton Mar 2014 A1
20140092247 Clark et al. Apr 2014 A1
20140121540 Raskin May 2014 A1
20140140592 Lasenby et al. May 2014 A1
20140142435 Bernal May 2014 A1
20140160349 Huang et al. Jun 2014 A1
20140168397 Greco et al. Jun 2014 A1
20140204207 Clark et al. Jul 2014 A1
20140247334 Johnson et al. Sep 2014 A1
20140253709 Bresch et al. Sep 2014 A1
20140267625 Clark et al. Sep 2014 A1
20140270494 Sawhney et al. Sep 2014 A1
20140288968 Johnson Sep 2014 A1
20140334058 Galvan et al. Nov 2014 A1
20140336479 Ando Nov 2014 A1
20150045608 Karp Feb 2015 A1
20150094606 Mestha Apr 2015 A1
20150105608 Lipoma Apr 2015 A1
20150105670 Bresch et al. Apr 2015 A1
20150109441 Fujioka Apr 2015 A1
20150141762 Heinrich May 2015 A1
20150288877 Glazer Oct 2015 A1
20150302717 Denittis et al. Oct 2015 A1
20160015278 Campo Jan 2016 A1
20160074764 Chen Mar 2016 A1
20160183695 Veron Jun 2016 A1
20160328993 Brogioli Nov 2016 A1
20160345832 Pavagada Nagaraja et al. Dec 2016 A1
20170095170 Verkurijsse et al. Apr 2017 A1
20170319114 Kaestle Nov 2017 A1
20190098260 Glazer Mar 2019 A1
20190205655 Matsuoka Jul 2019 A1
Foreign Referenced Citations (8)
Number Date Country
204862242 Dec 2015 CN
2292124 Mar 2011 EP
1999049656 Sep 1999 WO
2013016603 Jan 2013 WO
2013170032 Nov 2013 WO
2014012070 Jan 2014 WO
2015091582 Jun 2015 WO
2017196695 Nov 2017 WO
Non-Patent Literature Citations (30)
Entry
Dalal et al.,“Histograms of Oriented Gradients for Human Detection”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 8 pages, 2005.
Derpanis., “Overview of the RANSAC Algorithm”, New York University , Version 1.2, 2 pages, May 13, 2010.
Felzenszwalb et al., “Object Detection with Discriminatively Trained Part Based Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence ,vol. 32 , Issue 9 , pp. 1627-1645, Sep. 2009.
Glazer et al., “One-Class Background Model”, ACCV 2012: Computer Vision—ACCV Workshops, pp. 301-307, 2012.
Weinland., “A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition”, Institut National De Recherche En Informatique Et En Automatique, Research Report RR-7212, 54 pages, Feb. 2010.
Poppe., “Vision-based human motion analysis: An overview”, Computer Vision and Image understanding 108, pp. 4-18, 2007.
Moeslund et al., “A Survey of Computer Vision-Based Human Motion Capture”, Computer Vision and Image Understanding 81, pp. 231-268, 2001.
Kientz, et al., “KidCam: Toward an Effective Technology for the Capture of Children's Moments of Interest”, Proceedings of 7th International Conference on Pervasive Computing, pp. 115-132, Nara, Japan, May 11-14, 2009.
U.S. Appl. No. 29/608,324 office action dated Sep. 20, 2018.
Viola et al., “Rapid Object Detection Using a Boosted Cascade of Simple Features”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511-218, Feb. 2001.
Lam et al., “Mobile Video Stream Monitoring System”, Proceedings of the 11th ACM International Conference on Multimedia, 2 pages, Nov. 2-8, 2003.
Raskar, et al., “Prakash: Lighting Aware Motion Capture using Photosensing Markers and Multiplexed Illuminators”, ACM Transactions on Graphics, vol. 26, No. 3, Article 36, 12 pages, Jul. 2007.
Alcantarilla et al., “KAZE Features”, Proceedings of European Conference on Computer Vision, pp. 214-227, vol. 7577, Florence, Italy, Oct. 7-13, 2012.
Alcantarilla et al., “Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces”, 24th British Machine Vision Conference (BMVC), Bristol, UK, 11 pages, Sep. 9-13, 2013.
NANIT—Camera/Floorstand assembly, 6 pages, Retrieved on Aug. 13, 2018 (published date unknown) https://support.nanit.com/hc/en-us/articles/235605608-Camera-Floor-stand-assembly.
International Application # PCT/US2018/62166 search report dated Feb. 19, 2019.
NANIT, “NANIT Smart Baby Monitor and Wall Mount”, pp. 1-10, Dec. 9, 2017.
U.S. Appl. No. 29/612,968 office action dated Feb. 5, 2019.
NANIT Camera and floor stand, 1 page, Retrieved on Mar. 29, 2017 (published date unknown) https://store.nanit.com/.
Cowboystudio Photography Photo Studio Flash Mount Three Umbrellas Kit With Light Stand (online), http://www.sears.com/cowboystudio-photography-photo-studio-flash-mount-three/p-SPM8700940502?plpSellerId=AmiVentures Inc&prdNo-2&blockNo=2&blockType=G2#>, 3 pages, Retrieved on Feb. 24, 2017 (published date unknown).
Nest Cam Indoor security camera, 1 page, Retrieved on Mar. 1, 2017 (published date unknown) https://www.amazon.com/Nest-Indoor-security-camera-Amazon/dp/B00WBJGUA2?psc=1>.
Flir FX Portable Interchangeable Wi-Fi Camera, 2 pages, Mar. 6, 2014 http://geeknewscentral.com/2014/03/06/flir-fx-portable-interchangeable-wi-fi-camera/>.
NANIT Multi-Stand, 4 pages, Dec. 5, 2016 https://www.amazon.com/Nanit-N102-Multi-Stand-White/dp/B01MDKHTL7.
NANIT, “How do I reset my Nanit camera?”, 2 pages, Dec. 9, 2016 https://support.nanit.com/hc/en-us/articles/235804047-How-do-I-reset-my-Nanit-camera.
Glazer et al., U.S. Appl. No. 29/612,968, filed Aug. 6, 2017.
Glazer et al., U.S. Appl. No. 16/091,989, filed Oct. 7, 2018.
European Application # 17796611.6 search report dated Jan. 15, 2020.
European Application # 17796611.6 search report dated Nov. 7, 2019.
U.S. Appl. No. 16/091,989 office action dated Nov. 29, 2019.
CN Application # 2017800264834 office action dated Jan. 3, 2020.
Related Publications (1)
Number Date Country
20190150798 A1 May 2019 US
Provisional Applications (1)
Number Date Country
62589587 Nov 2017 US