EP2630624A1 - Procédé et appareil de mesure du mouvement d'une personne - Google Patents

Procédé et appareil de mesure du mouvement d'une personne

Info

Publication number
EP2630624A1
EP2630624A1 EP11834761.6A EP11834761A EP2630624A1 EP 2630624 A1 EP2630624 A1 EP 2630624A1 EP 11834761 A EP11834761 A EP 11834761A EP 2630624 A1 EP2630624 A1 EP 2630624A1
Authority
EP
European Patent Office
Prior art keywords
person
tracking
region
path
phenotypic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP11834761.6A
Other languages
German (de)
English (en)
Other versions
EP2630624A4 (fr
Inventor
Mahalaxmi Gita Bangera
Roderick A. Hyde
Muriel Y. Ishikawa
Edward K. Y. Jung
Jordin T. Kare
Eric C. Leuthardt
Nathan P. Myhrvold
Elizabeth A. Sweeney
Clarence T. Tegreene
David B. Tuckerman
Thomas Allan Weaver
Jr. Lowell L. Wood
Victoria Y. H. Wood
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Searete LLC
Original Assignee
Searete LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US12/925,407 external-priority patent/US20110166937A1/en
Priority claimed from US12/928,703 external-priority patent/US9024814B2/en
Priority claimed from US12/930,043 external-priority patent/US9019149B2/en
Application filed by Searete LLC filed Critical Searete LLC
Publication of EP2630624A1 publication Critical patent/EP2630624A1/fr
Publication of EP2630624A4 publication Critical patent/EP2630624A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0261—Targeted advertisements based on user location

Definitions

  • a system for tracking a path of a person includes a plurality of micro-impulse radars (MIRs) configured to probe a respective plurality of regions and a computing resource operatively coupled to the plurality of MIRs.
  • the computing resource is configured to receive signals or data from at least a portion of the plurality of MIRs, correlate the signals or data to at least one phenotypic identity or at least one individual identity, and infer or determine a path or a path characteristic between the regions of at least one person corresponding to the at least one phenotypic identity or individual identity.
  • a method for tracking the movement of a person includes extracting a new (second) human phenotypic identity from a MIR signal from a (second) region.
  • the second phenotypic identity is compared to one or more first phenotypic identities extracted from at least one MIR signal from at least one first region, and the second phenotypic identity is correlated to at least one of the one or more first phenotypic identities to determine movement between regions by a person corresponding to the second phenotypic identity.
  • a system for tracking the movement of persons includes a plurality of MIRs configured to probe respective regions. At least one processor is operatively coupled to the plurality of MIRs and configured to perform signal analysis to determine at least one phenotypic profile corresponding to a person. An electronic controller is configured to receive the phenotypic profile from the processor, associate the phenotypic profile to one or more previously received phenotypic profiles, and correlate the associated phenotypic profiles to time or locations of respective probed regions.
  • an apparatus includes a MIR configured to detect a speed or velocity associated with a person and a controller operatively configured to select media content for display to the person responsive to the velocity or speed.
  • a method includes operating a MIR to detect a speed or velocity associated with a person selecting media content for display to the person responsive to the velocity or speed.
  • FIG. 1 is a simplified block diagram of a micro-impulse radar (MIR), according to an embodiment.
  • MIR micro-impulse radar
  • FIG. 2 is a flow chart showing an illustrative process for determining the presence of a person in a region with the MIR of FIG. 1, according to an embodiment.
  • FIG. 3 is a flow chart showing an illustrative process for determining a physiological parameter of a person in a region with the MIR of FIG. 1, according to an embodiment.
  • FIG. 4 is a diagram of a system for tracking the motion of persons using MIRs, according to an embodiment.
  • FIG. 5 is a block diagram including a computing resource showing data that can be carried by a non-transient computer readable medium.
  • FIG. 6 is a diagram of a system for tracking the motion of a person including previous and current paths, and possible future destinations, according to an embodiment.
  • FIG. 7A illustrates an arrangement where at least two regions accessed by respective MIRs are separated and substantially not overlapping.
  • FIG. 7B illustrates an arrangement where at least two regions accessed by respective MIRs are overlapping.
  • FIG. 7C illustrates an arrangement where at least one region accessed by an MIR is a subset of another regions accessed by another MIR.
  • FIG. 7D illustrates an arrangement where a first region accessed by a first MIR and a second region accessed by a second MIR are substantially coincident.
  • FIG. 8 is a flow chart illustrating a method for tracking the motion of persons using MIRs, according to an embodiment.
  • FIG. 9 is a flow chart showing a method for detecting a speed or velocity associated with a person with a MIR, and selecting media content for output to the person, according to an embodiment.
  • FIG. 1 is a simplified block diagram of a micro-impulse radar (MIR) 101 , according to an embodiment.
  • a pulse generator 102 is configured to output a relatively short voltage pulse that is applied to a transmit antenna 104.
  • a typical transmitted pulse width can be between about two hundred picoseconds and about 5 nanoseconds, for example.
  • the voltage pulse can be conditioned and amplified (or attenuated) for output by a transmitter 108.
  • the transmitter 108 can transmit the voltage pulse or can further condition the pulse, such as by differentiating a leading and/or trailing edge to produce a short sub-nanosecond transmitted pulse.
  • the voltage pulse is typically not modulated onto a carrier frequency. Rather the voltage pulse transmission spectrum is the frequency domain transform of the emitted pulse.
  • the MIR 101 can probe a region 1 10 by emitting a series of spaced voltage pulses. For example the series of voltage pulses can be spaced between about 100 nanoseconds and 100 microseconds apart.
  • the pulse generator 102 emits the voltage pulses with non-uniform spacing such as random or pseudo-random spacing, although constant spacing can be used if interference or compliance is not a concern. Spacing between the series of voltage pulses can be varied responsive to detection of one or more persons 1 12 in the region 1 10. For example, the spacing between pulses can be relatively large when a person 1 12 is not detected in the region 1 10. Spacing between pulses can be decreased (responsive to one or more commands from a controller 106) when a person 1 12 is detected in the region 1 10. For example, the decreased time between pulses can result in faster MIR data generation for purposes of more quickly determining information about one or more persons 1 12 in the region 1 10.
  • the emitted series of voltage pulses can be characterized by spectral components having high penetration that can pass through a range of materials and geometries in the region 1 10.
  • An object 1 12 (such as a person) in the probed region 1 10 can selectively reflect, refract, absorb, and/or otherwise scatter the emitted pulses.
  • a return signal including a reflected, refracted, absorbed, and/or otherwise scattered signal can be received by a receive antenna 1 14.
  • the receive antenna 1 14 and transmit antenna 104 can be combined into a single antenna.
  • a filter (not shown) can be used to separate the return signal from the emitted pulse.
  • a probed region 1 10 can be defined according to an angular extent and distance from the transmit antenna 104 and the receive antenna 1 14. Distance can be determined by a range delay 1 16 configured to trigger a receiver 1 18 operatively coupled to the receive antenna 1 14.
  • the receiver 1 18 can include a voltage detector such as a capture-and-hold capacitor or network.
  • the range delay corresponds to distance into the region 1 10. Range delay can be modulated to capture information corresponding to different distances.
  • a signal processor 120 can be configured to receive detection signals or data from the receiver 1 18 and the analog to digital converter 122, and by correlating range delay to the detection signal, extract data corresponding to the probed region 1 10 including the object 1 12.
  • the MIR 101 can include a second receive antenna 1 14b.
  • the second receive antenna can be operatively coupled to a second receiver 1 18b coupled to an output of the range delay 1 16 or a separate range delay (not shown) configured to provide a delay selected for a depth into the region 1 10.
  • the signal processor 120 can further receive output from a second A/D converter 122b operatively coupled to the second receiver 1 18b.
  • the signal processor 120 can be configured to compare detection signals received by the antennas 1 14, 1 14b. For example, the signal processor 120 can search for common signal characteristics such as similar reflected static signal strength or spectrum, similar (or corresponding) Doppler shift, and/or common periodic motion components, and compare the respective range delays corresponding to detection by the respective antennas 1 14, 1 14b. Signals sharing one or more characteristics can be correlated to triangulate to a location of one or more objects 1 12 in the region 1 10 relative to known locations of the antennas 1 14, 1 14b. The triangulated locations can be output as computed ranges of angle or computed ranges of extent.
  • a first signal corresponding to a reflected pulse received by an antenna element 1 14 can be digitized by an analog-to-digital converter (A/D) 122 to form a first digitized waveform.
  • a second signal corresponding to the reflected pulse received by a second antenna element 1 14b can similarly be digitized by and A/D 122b (or alternatively by the same A/D converter 122) to form a second digitized waveform.
  • the signal processor 120 can compare the first and second digitized waveforms and deduce angular information from the first and second digitized waveforms and known geometry of the first and second antenna elements.
  • a second pulse can be received at a second range delay 1 16 value and can be similarly signal processed to produce a second set of angular information that maps a second surface at a different distance. Depth within a given range delay can be inferred from a strength of the reflected signal. A greater number of signals can be combined to provide additional depth information. A series of pulses can be combined to form a time series of signals corresponding to the object 1 12 that includes movement information of the object 1 12 through the region 1 10.
  • the object 1 12 described herein can include one or more persons.
  • the signal processor 120 outputs MIR data.
  • the MIR data can include object location information, object shape information, object velocity information, information about inclusion of high density and/or conductive objects such as jewelry, cell phones, glasses including metal, etc., and physiological information related to periodic motion.
  • the MIR data can include spatial information, time-domain motion information, and/or frequency domain information.
  • the MIR data can be output in the form of an image.
  • MIR data in the form of an image can include a surface slice made of pixels or a volume made of voxels.
  • the image can include vector information.
  • the MIR data from the signal processor 120 is output to a signal analyzer 124.
  • the signal analyzer 124 can be integrated with the signal processor 120 and/or can be included in the same MIR 101 , as shown.
  • the signal processor 120 can output MIR data through an interface to a signal analyzer 124 included in an apparatus separate from the MIR 101 .
  • a signal analyzer 124 can be configured to extract desired information from MIR data received from the signal processor 120. Data corresponding to the extracted information can be saved in a memory for access by a data interface 126 or can be pushed out the data interface 126.
  • the signal analyzer 124 can be configured to determine the presence of a person 1 12 in the region 1 10.
  • MIR data from the signal processor can include data having a static spectrum at a location in the region 1 10, and a periodic motion spectrum corresponding to the location characteristic of a human physiological process (e.g.
  • the signal analyzer 124 can be configured to determine a number of persons 1 12 in the region 1 10.
  • the signal analyzer 124 can be configured to determine the size of a person and/or relative size of anatomical features of a person 1 12 in the region 1 10.
  • the signal analyzer 124 can be configured to determine the presence of an animal 1 12 in the region 1 10.
  • the signal analyzer 124 can be configured to determine movement and/or speed of movement of a person 1 12 through the region 1 10.
  • the signal analyzer 124 can be configured to determine or infer the orientation of a person 1 12 such as the direction a person is facing relative to the region 1 10.
  • the signal analyzer 124 can be configured to determine one or more physiological aspects of a person 1 12 in the region 1 10.
  • the signal analyzer 124 can determine presence of a personal appliance such as a cell phone, PDA, etc. and/or presence of metalized objects such as credit cards, smart cards, access cards, etc.
  • the signal analyzer 124 can determine the presence of an associated article such as a carts or hand truck, a baby strollers, a bicycle, wheeled luggage, a wheel chair, a walker, crutches, a cane, or other object that can be carried, pushed, pulled, or ridden by the person 1 12.
  • the signal analyzer 124 can infer the gender and age of one or more persons based on returned MIR data.
  • male bodies can generally be characterized by higher mass density than female bodies, and thus can be characterized by somewhat greater reflectivity at a given range.
  • Adult female bodies can exhibit relatively greater harmonic motion ("jiggle") responsive to movements, and can thus be correlated to harmonic spectra characteristics. Older persons generally move differently than younger persons, allowing an age inference based on detected movement in the region 1 10.
  • the signal analyzer 124 can determine a demographic of one or more persons 1 12 in the region 1 10.
  • MIR data can include movement corresponding to the beating heart of one or more persons 1 12 in the region 1 10.
  • the signal analyzer 124 can filter the MIR data to remove information not corresponding to a range of heart rates, and determine one or more heart rates by comparing movement of the heart surface to the MIR signal rate.
  • the one or more heart rates can further be characterized according to a confidence factor, depending on statistical certainty regarding the determined one or more heart rates.
  • the signal analyzer 124 can determine one or more respiration rates by measuring movement corresponding to the chest or diaphragm of one or more persons 1 12.
  • the signal analyzer 124 can determine movement, a direction of movement, and/or a rate of movement of one or more persons 1 12 in the region 1 10. Operation of the signal analyzer 124 is described in greater detail below by reference to FIGS. 2 and 3.
  • An electronic controller 106 can be operatively coupled to the pulse generator 102, the transmitter 108, the range delay 1 16, the receiver 1 18, the analog-to-digital converter 122, the signal processor 120, and/or the signal analyzer 124 to control the operation of the components of the MIR 101 .
  • the electronic controller 106 can also be operatively coupled to the second receiver 1 18b, and the second analog-to-digital converter 122b.
  • the data interface 126 can include a high speed interface configured to output data from the signal analyzer 124. Alternatively, for cases where signals are analyzed externally to the MIR, the data interface 126 can include a high speed interface configured to output MIR data from the signal processor 120.
  • the data interface 126 can include an interface to the controller 106.
  • the controller 106 can be interfaced to external systems via a separate interface (not shown).
  • FIG. 2 is a flow chart showing an illustrative process 201 for determining the presence of one or more persons 1 12 in the region 1 10 with the signal analyzer 124 of the MIR 101 , according to an embodiment.
  • MIR data is received as described above in conjunction with FIG. 1.
  • the MIR data can correspond to a plurality of probes of the region 1 10.
  • the MIR data can be enhanced to facilitate processing. For example, grayscale data corresponding to static reflection strength as a function of triangulated position can be adjusted, compressed, quantized, and/or expanded to meet a desired average signal brightness and range.
  • velocity information corresponding to Doppler shift, and/or frequency transform information corresponding to periodically varying velocity can similarly be adjusted, compressed, quantized, and/or expanded.
  • Systematic, large scale variations in brightness can be balanced, such as to account for side-to-side variations in antenna coupling to the region. Contrast can be enhanced such as to amplify reflectance variations in the region.
  • a spatial filter can be applied. Application of a spatial filter can reduce processing time and/or capacity requirements for subsequent steps described below.
  • the spatial filter may, for example, include a computed angle or computed extent filter configured to remove information corresponding to areas of contrast, velocity, or frequency component(s) having insufficient physical extent to be large enough to be an object of interest.
  • the spatial filter may, for example, identify portions of the region 1 10 having sufficient physical extent to correspond to body parts or an entire body of a person 1 12, and remove features corresponding to smaller objects such as small animals, leaves of plants, or other clutter.
  • the spatial filter can remove information corresponding to areas of contrast, velocity, or frequency component(s) having physical extent greater than a maximum angle or extent that is likely to correspond to a person or persons 1 12.
  • the spatial filter applied in step 206 can eliminate small, low contrast features, but retain small, high contrast features such as jewelry, since such body ornamentation can be useful in some subsequent processes.
  • the step of applying the spatial filter 206 can further include removing background features from the MIR data. For example, a wall lying between an antenna 104, 1 14 and the region 1 10 can cast a shadow such as a line in every MIR signal. Removal of such constant features can reduce subsequent processing
  • an edge-finder can identify edges of objects 1 12 in the region 1 10. For example, a global threshold, local threshold, second derivative, or other algorithm can identify edge candidates. Object edges can be used, for example, to identify object shapes, and thus relieve subsequent processes from operating on grayscale data. Alternatively, step 208 can be omitted and the process of identifying objects can be performed on the grayscale MIR data.
  • processed data corresponding to the MIR data is compared to a database to determine a match.
  • the object data received from step 202 can be compared to corresponding data for known objects in a shape database.
  • Step 210 can be performed on a grayscale signal, but for simplicity of description it will be assumed that optional step 208 was performed and matching is performed using object edges, velocity, and/or spectrum values.
  • the edge of an object 1 12 in the region 1 10 can include a line corresponding to the outline of the head and torso, cardiac spectrum, and movements characteristic of a young adult male.
  • a first shape in the shape database can include the outline of the head and torso, cardiac spectrum, density, and movements characteristic of a young adult female and/or the head and torso outline, cardiac spectrum, density, and movements characteristic of a generic human.
  • the differences between the MIR data and the shape database shape can be measured and characterized to derive a probability value. For example, a least-squares difference can be calculated.
  • the object shape from the MIR data can be stepped across, magnified, and stepped up and down the shape database data to minimize a sum-of-squares difference between the MIR shape and the first shape in the shape database.
  • the minimum difference corresponds to the probability value for the first shape.
  • step 212 if the probability value for the first shape is the best probability yet encountered, the process proceeds to step 214.
  • the first probability value is the best probability yet encountered. If an earlier tested shape had a higher probability to the MIR data, the process loops back from step 212 to step 210 and the fit comparison is repeated for the next shape from the shape database.
  • step 214 the object type for the compared shape from the shape database and the best probability value for the compared shape are temporarily stored for future comparison and/or output.
  • the compared shape from the shape database can be identified by metadata that is included in the database or embedded in the comparison data. Proceeding to step 216, the process either loops back to step 210 or proceeds to step 218, depending on whether a test is met. If the most recently compared shape is the last shape available for comparison, then the process proceeds to step 218.
  • step 218 the object type and the probability value is output.
  • the process can then loop back to step 202 and the process 201 can be repeated.
  • the process 201 loops from step 216 back to step 210.
  • the next comparison shape from a shape database is loaded.
  • the comparison can proceed from the last tested shape in the shape database. In this way if the step 218 to 202 loop occurs more rapidly than all objects in the shape database can be compared, the process eventually works its way, through the entire shape database.
  • the shape database can include multiple copies of the same object at different orientations, distances, and positions within the region. This can be useful to reduce processing associated with stepping the MIR shape across the shape database shape and/or changing magnification.
  • the object type can include determination of a number of persons 1 12 in the region 1 10.
  • the shape database can include outlines, cardiac and/or respiration spectra, density, and movement characteristics for plural numbers of persons.
  • the shape library can include shapes not corresponding to persons. This can aid in identification of circumstances where no person 212 is in the region 210.
  • process 201 can be performed using plural video frames such as averaged video frames or a series of video frames.
  • steps 212, 214, and 216 can be replaced by a single decision step that compares the probability to a predetermined value and proceeds to step 218 if the probability meets the predetermined value. This can be useful, for example, in embodiments where simple presence or absence of a person 212 in the region 210 is sufficient information.
  • the signal analysis process 201 of FIG. 2 can be performed using conventional software running on a general-purpose microprocessor.
  • the process 201 can use various combinations of hardware, firmware, and software; and can include the use of a digital signal processor.
  • FIG. 3 is a flow chart showing an illustrative process 301 for determining one or more particular physiological parameters of a person 1 12 in the region 1 10 with the signal analyzer 124 of the MIR 101 , according to an embodiment.
  • the process 301 of FIG. 3 can be performed conditional to the results of another process such as the process 201 of FIG. 2. For example, if the process 201 determines that no person 1 12 is in the region 1 10, then it can be preferable to continue to repeat process 201 rather than execute process 301 in an attempt to extract one or more particular physiological parameters from a person that is not present.
  • step 302 a series of MIR time series data is received.
  • the process 301 generally needs the time series data received in step 302 to have a temporal capture relationship appropriate for extracting time-based information.
  • the MIR time series data can have a frame rate between about 16 frames per second and about 120 frames per second. Higher capture rate systems can benefit from depopulating frames, such as by dropping every other frame, to reduce data processing capacity requirements.
  • step 304 the MIR video frames can be enhanced in a manner akin to that described in conjunction with step 204 of FIG. 2.
  • step 304 can include averaging and/or smoothing across multiple MIR time series data.
  • a frequency filter can be applied. The frequency filter can operate by comparing changes between MIR time series data to a reference frequency band for extracting a desired physical parameter. For example, if a desired physiological parameter is a heart rate, then it can be useful to apply a pass band for periodic movements having a frequency between about 20 cycles per minute and about 200 cycles per minute, since periodic motion beyond those limits is unlikely to be related to a human heart rate.
  • step 304 can include a high pass filter that removes periodic motion below a predetermined limit, but retains higher frequency information that can be useful for determining atypical physiological parameters.
  • a spatial filter can be applied.
  • the spatial filter may, for example, include a pass band filter configured to remove information corresponding to areas of contrast having insufficient physical extent to be large enough to be an object of interest, and remove information corresponding to areas too large to be an object of interest.
  • the spatial filter may, for example, identify portions of the region 1 10 having sufficient physical extent to correspond to the heart, diaphragm, or chest of a person 1 12, and remove signal features corresponding to smaller or larger objects.
  • the step of applying the spatial filter 308 can further include removing background features from the MIR data. For example, a wall lying between an antenna 104, 1 14 ( 1 14b) and the region 1 10 can cast a shadow such as a line in every instance of MIR data.
  • movement such as periodic movement in the MIR time series data is measured.
  • a periodic motion is to be measured, a time- to-frequency domain transform can be performed on selected signal elements.
  • a rate of movement of selected signal elements can be determined.
  • periodic and/or non-periodic motion can be measured in space vs. time.
  • Arrhythmic movement features can be measured as spread in frequency domain bright points or can be determined as motion vs. time.
  • subsets of the selected signal elements can be analyzed for arrhythmic features.
  • plural subsets of selected signal elements can be cross-correlated for periodic and/or arrhythmic features.
  • one or more motion phase relationships between plural subsets of selected signal features, between a subset of a selected signal feature and the signal feature, or between signal features can be determined.
  • a person with a hiccup can be detected as a non-periodic or arrhythmic motion superimposed over periodic motion of a signal element corresponding to the diaphragm of the person.
  • a physiological parameter can be calculated.
  • MIR data can include data having a periodic motion spectrum corresponding to the location characteristic of a human physiological process (e.g. heartbeat and/or breathing).
  • Step 312 can include determining one or more heart rates by comparing movement of the heart surface to the MIR signal rate. The one or more heart rates can further be characterized according to a confidence factor, depending on statistical certainty regarding the determined one or more heart rates.
  • step 3 12 can include determining one or more respiration rates by measuring movement corresponding to the chest or diaphragm of one or more persons.
  • FIG. 4 is a diagram of a system 401 for tracking the motion of a person 1 12, according to an embodiment.
  • the system 401 includes plurality of MIRs 101 a, 101b configured to probe a respective plurality of regions 402, 404.
  • a computing resource 412 is operatively coupled to the plurality of MIRs 101 a, 101 b, for example via a computer network 410.
  • the computing resource 412 is configured to receive signals or data from at least a portion of the plurality of MIRs 101a, 101 b.
  • the plurality of MIRs can provide MIR signals or data including information corresponding to human attributes, or the plurality of MIRs can perform processing to convert the information corresponding to human attributes into phenotypic profiles including the attributes.
  • the computing resource 412 can be configured to correlate the signals or data from the MIRs to at least one phenotypic identity 1 12' and/or at least one individual identity corresponding to the person 1 12.
  • the computing resource 412 can be further configured to infer or determine a travel path or a path characteristic 406 between the regions 402, 404 taken by the at least one person 1 12 corresponding to the at least one phenotypic identity 1 12' or individual identity. For example, the computing resource 412 can determine that a first phenotypic identity 1 12'a sensed by a first MIR 101 a in a first region 402 at a first time corresponds to the same person 1 12 as a second phenotypic identity 1 12'b sensed by a second MIR 101 b in a second region 404 at a second time. From the two observed times and locations 402, 404, the computing resource 412 can infer that the person 1 12 traveled along a path 406 between the two regions. Similarly, the computing resource 412 can also infer or determine paths taken by persons between a larger plurality of regions, as described more fully in conjunction with FIG. 6, below.
  • the system 401 can also include media output apparatuses 408 operatively coupled to the computing resource 412.
  • the system 401 can include media output apparatuses 408a, 408b respectively configured to output media to the regions 402, 404 and at least one person 1 12 in the regions.
  • the media output apparatuses 408a, 408b can include one or more of a portable media player, an electronic display, configurable signage, a video screen, or a loudspeaker.
  • the computing resource 412 can be further configured to select at least one media parameter for a media output apparatus 408b responsive to the inferred or determined path or path characteristic 406.
  • the computing resource 412 can be configured to select a media source (not shown) or transmit media corresponding to the at least one media parameter to the media output apparatus 408b.
  • the computing resource 412 can also be configured to operate the media output apparatus 408b according to the at least one media parameter.
  • the computing resource 412 can cause a personal media player 408 carried by the person 1 12 to output media to the person corresponding to the parameter.
  • the at least one media parameter can include a configuration corresponding to an instance of a presence of the at least one person 1 12 in a previously visited region 402.
  • the at least one media parameter can include time synchronization of a media file or media stream with the media file or media stream output to the person 1 12 in the previously visited region 402. This can provide, for example, substantially uninterrupted receipt of a media file or stream across a plurality of media output devices 408a, 408b, etc. as the person 1 12 travels between regions 402, 404, etc. into which the media output devices deliver the media.
  • the computing resource 412 can select media parameters including one or more of an advertising characteristic, an advertising message, a help message, a program choice, a music genre, channel favorites, a media library, an audio volume, an audio balance, an audio equalization, an audio mode, a video mode, a receiver configuration, a media source, or a television channel.
  • media parameters including one or more of an advertising characteristic, an advertising message, a help message, a program choice, a music genre, channel favorites, a media library, an audio volume, an audio balance, an audio equalization, an audio mode, a video mode, a receiver configuration, a media source, or a television channel.
  • FIG. 5 is a block diagram including the computing resource 412, showing data that can be carried by a non-transient computer readable medium 414.
  • the operation of the system 401 is described below by reference to FIGS. 4 and 5.
  • the computing resource 412 can be further configured to determine or infer one or more preferences, interests, or consumer characteristics of the at least one person 1 12, and select at least one media parameter for one or more media output apparatuses 408a, 408b responsive to the received or inferred one or more preferences, interests, or consumer characteristics.
  • the phenotypic identities 1 12' or individual identities of persons 1 12 determined from the MIR signals or data can be carried as data 502 on the non-transient computer readable medium 414.
  • the computing resource 412 may also record and/or read path or path characteristic data 504 carried by the computer readable medium 414. Based on the data 504 corresponding to the path or path characteristics 406 the computing resource can infer one or more preferences, interests, or consumer characteristics corresponding to the person 1 12. For example, the regions 402, 404 may have known or inferred characteristics. The region characteristics can be referred to as location attributes. The path or path characteristic 406 can be correlated to at least one location attribute 506 corresponding to at least one previously visited region 402 to determine or infer the one or more preferences, interests or consumer characteristics. As shown in FIG. 5, the computing resource 412 can be configured to read the at least one location attribute 506 from the non-transient computer readable medium 414.
  • the computing resource can receive the at least one location attribute from a remote resource via a network 410.
  • a location attribute can include one or more of a business located proximate the region 402, a business type located proximate to the region 402, a service offered proximate to the region 402, a product offered proximate to the region 402, a parameter of media presented proximate to the region 402, media content presented proximate to the region 402, an exhibit proximate to the region 402, a map proximate to the region 402, a view from the region 402, a utility proximate to the region 402, an apparatus proximate to the region 402, a door or passage proximate to the region 402, a gaming designation corresponding to the region 402, a social environment corresponding to the region 402, or an activity associated with the region 402.
  • the computing resource 412 can infer that the person 1 12 has an interest in books, and could respond favorably to an advertisement for an online bookstore.
  • the computing resource 412 might infer that the person may be hungry, and may respond favorably to an advertisement for another nearby restaurant.
  • the computing resource 412 can be configured to infer one or more preferences, interests, or consumer characteristics responsive to one or more time durations that the at least one person 1 12 remained in at least one previously visited region 402. Lingering in a region 402 may indicate interest in something associated with the region. Similarly, the computing resource 412 can be configured to infer one or more preferences, interests, or consumer characteristics responsive to one or more movements of the at least one person in at least one previously visited region 402. For example, standing still near the door of the restaurant referenced above, may indicate that the person had read the menu and considered whether to enter and eat.
  • the computing resource 412 can be configured to infer one or more preferences, interests, or consumer characteristics responsive to one or more physiological or physical characteristics exhibited by the at least one person while in the at least one previously visited region 402. Similarly, a time of transit of the at least one person between discontinuous regions 402, 404 can be used by the computing resource 412 to infer one or more preferences, interests, or consumer characteristics. For example, person in a hurry can be less responsive to an advertising message or may prefer receiving terse prompts compared to a person who takes a longer time to travel between regions 402, 404, who may be more receptive to an advertising message, or who may prefer or be in need of more verbose prompts. For example, slower travel between regions 402, 404 could be indicative of a need for directions, which the computing resource 412 can cause to be offered on the media output apparatus 408b.
  • the computing resource 412 can cooperate with MIRs 101 a, 101 b to determine a phenotypic identity 1 12' of a person 1 12.
  • a phenotypic identity includes observable characteristics of a person, and can include physical and/or physiological attributes that are captured by an MIR 101.
  • Signals or data received from at least a portion of the plurality of MIRs 101a, 101 b can include one or more of attributes of the at least one person 1 12.
  • the computing resource 412 can be configured to construct a phenotypic profile from the attributes, and either save or convert the phenotypic profile to a new phenotypic identity (if no match is attempted or found) or match the phenotypic profile to at least one previously known or cataloged phenotypic identity 1 12' corresponding to the one or more attributes.
  • a phenotypic identity can be unique across a range of regions 402, 404 accessed or monitored by the MIRs 101 a, 101 b, or, especially in systems 101 that measure a large area or large crowds, more than one person 1 12 can correspond to the same phenotypic identity, at least for phenotypic identities that include a relatively small number of attributes. A greater number of attributes and/or a longer observation time can be used to determine more phenotypic identity variables, and help to differentiate between individuals. Similarly, determination or inference of a plurality of paths 406 can help to differentiate between similar phenotypic identities.
  • a phenotypic identity or individual identity from among a small number (e.g., two) or a relatively large number of persons.
  • a small number of attributes can be sufficient to differentiate between the persons.
  • body size alone could be sufficient to determine whether a person is an adult or a child.
  • a larger number of attributes can typically be determined to provide a relatively high probability of an accurate determination and/or differentiation between persons.
  • the one or more attributes of the at least one person can include at least one physical attribute and at least one physiological attribute.
  • a physical attribute can include at least one of body size, body mass, height, body shape, posture, body permittivity, associated articles, and/or detectable body ornamentation.
  • the attributes can include a characteristic movement such as a characteristic voluntary movement or a characteristic involuntary movement.
  • the characteristic movement can include a reactive movement.
  • a physiological attribute can include at least one of heart rate, an intracyclic heartbeat characteristic, breathing rate, a rate or magnitude of inhalation, a rate or magnitude of exhalation, a tremor of all or part of a body, an intracyclic breathing characteristic, or an intercyclic breathing
  • the phenotypic identity can include data corresponding to one or more of a size of a person, a shape of a person, density of a person, detectable ornamentation associated with a person, detectable clothing worn by a person, a heart size, a posture, a head-to-body size ratio, body movements, an in utero fetus, a prosthesis, a personal appliance, a heart rate, a heart arrhythmia, a respiration rate, a respiration irregularity, a diaphragm motion, a diaphragm spasm, and/or a detectable health attribute.
  • the phenotypic identity can include data corresponding to the detected physical attributes and/or physiological data.
  • a phenotypic identity can include structured data corresponding to "gender: male, carrying: cell phone, glasses, heart rate: 60-65, height: 6'-2"; or "gender: female, carrying: computer, fetus 2 nd trimester, heart rate: 55-60, height: 5 '-6".”
  • the signals or data received from at least a portion of the plurality of MIRs 101 a, 101 b can include at least one phenotypic identity 1 12' corresponding to the at least one person 1 12. That is, the MIRs 101 can do some or all of the signal analysis necessary to extract attributes corresponding to the person 1 12, and construct a phenotypic profile.
  • a phenotypic profile can be a structured set of data listing attributes and/or probabilities of attributes.
  • the MIR 101 can match the attributes to an existing phenotypic identity 1 12' or create a new phenotypic identity 1 12', and transmit the phenotypic identity 1 12' to the computing resource 412.
  • Correlation of the signals or data from the MIR(s) 101 to at least one phenotypic identity 1 12' or at least one individual identity can includes at least one of accessing a database or a look-up table, illustrated as sections 502, 508 of the computer readable medium 414.
  • the phenotypic identity 1 12' can be written as a stored phenotypic identity 502 in one or more non-transient computer readable medium 414 in or operatively coupled to the computing resource 412.
  • the computing resource 412 can optionally determine at least one individual identity 508 corresponding to the at least one phenotypic identity 1 12'.
  • Individual identities in an identification library can correspond to assigned individual characteristics not correlated to actual known identities of individuals.
  • the plurality of individual identities can correspond to individual aliases representative of individual persons.
  • the plurality of individual identities can correspond to actual known identities of individuals.
  • Correlating MIR-captured attributes to a phenotypic identity and/or correlating the phenotypic identity to an individual identity can include accessing a database or a look-up table.
  • the database or look-up table can include records
  • phenotypic identities 502 that include cells providing physical, physiological, and other attributes such as those listed above. Attributes can be compared to determine a best fit from among the records 502. Similarly, a phenotypic identity 502 can be compared to known phenotypic identities to determine a best fit to an individual identity 508.
  • the best fit record can also include a person's name, one or more cells that act as an index to a person's preferences, and/or other indicators of the individual person. According to embodiments, correlating the temporary identity to an individual identity does not necessarily require determining information that can explicitly identify the person (e.g., provide an actual identity), because in some applications all that is required is the determination of preferences corresponding to the individual identity.
  • the "individual identity" can include an index number, and the person can remain anonymous to the system; or it can include a conventional identity including the person's name, for example.
  • the computing resource 412 can access one or more preferences 510 corresponding to the at least one individual identity 508.
  • the preferences 510 can include previously inferred preferences corresponding to the individual identity or, in cases where the individual identity is correlated to an actual person, can include preferences previously entered by the person, or mined from data related to the person's previous activities.
  • the computing resource 412 can determine a media selection 512, which can include media parameters predicted from the preferences, and/or can include a media file or stream that the person has been recently receiving.
  • FIG. 6 is a diagram illustrating a system for tracking the motion of a person including previously visited region(s) 402, a currently visited region 404, and possible future regions 602, 604, according to an embodiment.
  • the computing resource 412 can also formulate one or more predicted paths 514a, 514b, 514c from the current path 504 (which can include an accumulation of previous paths) and/or from the preferences 5 10.
  • the predicted path 5 14 can include at least one arrival time of the at least one person at a future region 602. For example, if a previous path 406 was found to correspond to a direct (e.g.
  • the computing resource 412 can predict that the person 1 12 will similarly take a direct path 514a to the predicted future region 602, rather than a more circuitous or slower path 514b, 514c.
  • the computing resource can determine a media parameter that includes a media output start time corresponding to the predicted time of arrival of the at least one person 1 12 to a vicinity 602 of the media output apparatus 408f.
  • the computing resource 412 can also determine one or more other media parameters with which to operate the media output apparatus 408f to output media to the predicted region 602.
  • the computing resource 412 can predict a plurality possible future regions 602, 604 that will be visited by the at least one person 1 12, and receive MIR l O l f, 101 g signals or data from the plurality of possible future regions 602, 604 to determine an actually visited one of the possible future regions 602. The computing resource 412 can then predict another future path (not shown) responsive to the actually visited region 602. The actually taken path 5 14a can be combined with other actual paths 504 in the computer-readable media 414. The process can be repeated as the person 1 12 transits an area corresponding to a plurality of regions 402, 404, 602, 604.
  • the media parameter(s) determined by the computing resource 412 can include directions to another location.
  • the computing resource 412 can be configured to infer or determine preferences, interests, or consumer characteristics of the at least one person 1 12, and output directions to or a suggestion to visit one or more other regions of interest.
  • the other region(s) of interest can, for example, offer a product or service similar to a previously visited region where the person lingered or otherwise showed an interest. If a person lingered in or near a previous region characterized by a pleasing view, the computing resource 412 can cause a subsequent media output apparatus 408 to suggest a route to another location with a view. If a person previously was in a space characterized by high activity, the computing resource 412 can cause a subsequent media output apparatus 408 can suggest alternative routes to a restroom and a quiet sitting area.
  • Correlating the signals or data from the MIR(s) 101 to at least one phenotypic identity 1 12' or at least one individual identity can include selecting from a limited set of phenotypic or individual identities.
  • the limited set of individual identities can, for example, be associated with an occupancy record of persons in or anticipated to be in a region 402, 404, 602 accessed by the MIR 101.
  • the computing resource can be further configured to generate an occupancy record for the person 1 12, the occupancy record including a position of the person, a speed of the person, a velocity of the person, a direction of motion of the person, an orientation of the person, a time associated with presence of the person, a time of arrival of the person to a region, a time of departure of the person from the region, and/or the path of the person through the plurality of regions.
  • the computing resource 412 can send the occupancy record to a third party or external database and/or combine the occupancy record with another occupancy record associated with the person 1 12.
  • the computing resource 412 can be further configured to flag a phenotypic identity 1 12' or individual identity as "in-use” during a time period in which the phenotypic identity 1 12' or individual identity is present in one of the plurality of regions 402, 404, 602.
  • the "in-use" flag is applicable to one of the plurality of regions 404 where the phenotypic identity or individual identity is present.
  • the computing resource 412 can then perform analysis on a subsequent MIR signal or data to determine that the phenotypic identity 1 12' or individual identity is no longer present in the region 404, and remove the "in use” flag from the individual identity (or phenotypic identity).
  • the "in-use" flag can apply across a subset greater than one or all of the plurality of regions.
  • the computing resource 412 can be configured to exclude individual identities having "in use” flags during the correlation of at least one phenotypic identity 1 12' to at least one individual identity. In applications where phenotypic identities 1 12' are uniquely associated with an individual identity, this can reduce processing requirements by excluding individual identities that have already been determined to be present.
  • the computing resource 412 can use an "in-use" flag to track intersecting paths taken by persons 1 12 having similar phenotypic expressions.
  • the computing resource 412 can be configured to infer departure paths 514a, 514b, 514c taken by similar "in-use" phenotypic identities from a region 404 responsive to arrival paths 406, 406b, 406c taken by the similar "in-use” phenotypic identities to the region 404.
  • the computing resource 412 can infer that the persons likely continued along a direction similar to their respective earlier directions.
  • the computing resource 412 can infer or determine a plurality of independent paths 406 of a plurality of persons 1 12 corresponding to phenotypic identities 1 12' or individual identities between a plurality of regions 402, 404. In this way, the computing resource 412 can keep track of the paths taken by the two individual persons.
  • the correlation of the signals or data to at least one phenotypic identity 1 12' or at least one individual identity includes performing a joint fit of two or more sets of human attributes included in the signals or data to a plurality of phenotypic identities.
  • the correlation of the signals or data to at least one phenotypic identity 1 12' or at least one individual identity can include performing a joint fit of two or more sets of phenotypic identities 1 12' included in the signals or data to a plurality of individual identities.
  • FIG. 7A illustrates an arrangement where at least two of the plurality of regions 402, 404 are separated and substantially not overlapping.
  • FIG. 7B illustrates an arrangement where at least two of the plurality of regions 402, 404 are overlapping.
  • FIG. 7C illustrates an arrangement where at least one of the plurality of regions 402 is a subset of another of the plurality of regions 404.
  • FIG. 7D illustrates an arrangement where a first of the plurality of regions 402 and a second of the plurality of regions 404 are substantially coincident.
  • one or more of the plurality of regions 402 can be in motion relative to another of the plurality of regions 404. For example, this can occur when a region 402 accessed by a MIR 101 includes all or a portion of a car, bus, train, boat, airplane, or other moving platform.
  • FIG. 8 is a flow chart illustrating a method 801 for tracking the motion of persons using MIRs, according to an embodiment.
  • a new or second human phenotypic identity is extracted from a MIR signal from a second region.
  • Step 802 can include probing the second region with a MIR, receiving scattered MIR radiation from the second region with a receiver, and generating the MIR signal from the received scattered MIR radiation.
  • the MIR signal can include information related to attributes of a person.
  • the MIR signal can be analyzed to extract the attributes.
  • the attributes can be formatted to create a phenotypic profile and/or a phenotypic identity.
  • the phenotypic identity and location or designation of the region is saved.
  • the second phenotypic identity is compared to one or more first phenotypic identities previously extracted from at least one MIR signal from at least one first region. Proceeding to step 806, if a correlation was made between the second phenotypic identity and at least one first phenotypic identity, the process 801 proceeds to step 808. If no correlation is made, the process can loop to step 802. If, at step 806, it is determined that a correlation between the second phenotypic identity and one or more first phenotypic identities was made, the correlated phenotypic identities are used, in step 808, to determine movement between the first and second regions by a person corresponding to the second (and first) phenotypic identity.
  • the phenotypic identity or identities extracted from the MIR signal in step 802 can correspond to attributes of a person that is in the region.
  • the MIR signal or data can include one or more of a size of a person, a shape of a person, density of a person, detectable ornamentation associated with a person, detectable clothing worn by a person, a heart size, a posture, a head-to-body size ratio, body movements, an in utero fetus, a prosthesis, a personal appliance, a heart rate, heart arrhythmia, a respiration rate, a respiration irregularity, a diaphragm motion, a diaphragm spasm, or a detectable health attribute.
  • a phenotypic identity can be associated with at least one physical attribute and at least one physiological attribute.
  • the one or more first phenotypic identities compared to the second phenotypic identity in step 804 can be extracted from the at least one MIR signal from the at least one first region. Additionally or alternatively, the one or more first phenotypic identities or corresponding individual identities can be received via a computer network.
  • the phenotypic identities can be compared by performing a statistical analysis of similarities between phenotypic identities and/or by performing a statistical analysis of differences between phenotypic identities.
  • Step 804 can include correlating the second phenotypic identity to at least one of the one or more first phenotypic identities by performing a joint fit of two or more first phenotypic identities to two or more second phenotypic identities.
  • the second phenotypic identity can be correlated to an individual identity of a person. Accordingly, the second and at least one first phenotypic identities can be compared by comparing correlated individual identities.
  • Step 808 can include correlating the movement between regions to at least one time of movement or time interval between presence in the regions.
  • correlating to determine movement can include selecting phenotypic identities to maximize similarities or minimize differences according to a joint fit between a plurality of second phenotypic identity and two or more first phenotypic identities.
  • the second region and the at least one first region can be substantially non-overlapping.
  • the second region and the at least one first region can be separated by a distance greater than or equal to the physical extents of the first and second regions.
  • Step 808 can include determining, from the time or time interval and one or more physical distances between the regions, one or more speeds of travel of the person.
  • the process 801 can proceed to step 810, where a future movement such as a future path and/or speed of the person can be inferred from the time or time interval between the person being in the regions and one or more physical distances between the regions.
  • a future movement such as a future path and/or speed of the person can be inferred from the time or time interval between the person being in the regions and one or more physical distances between the regions.
  • a consumer profile for the person corresponding to the second phenotypic identity is inferred.
  • the consumer profile can be inferred from a time or time interval of travel between at least the first region and the second region and one or more product displays or advertisements at or between the regions.
  • the consumer profile can be based on an inference of the person lingering at or near product displays or advertisements.
  • the consumer profile can be based on a history of movements of the person.
  • the consumer profile can be based on a transaction history, detected behavior in at least one first or the second region, or movement between the regions of the person corresponding to the second phenotypic identity.
  • the consumer profile can include a prediction of purchasing behavior.
  • step 814 an advertisement or product display likely to receive a positive response from the person (based on the consumer profile) can be selected and provided to the person.
  • step 814 can include providing electronic guidance to direct the person to a location corresponding to a product or service indicated by the consumer profile.
  • an MIR can also determine movement of a person within a single region.
  • the movement detected in a single region can optionally be combined with information derived from other regions, or can be used in the absence of "neighboring" region information to infer a preference, response, and/or consumer profile of the person.
  • One or more media output apparatuses can then be used to present helpful and/or commercial information to the person.
  • a process 901 starts with step 902, wherein a MIR can be operated to detect a speed or velocity associated with a person. For example, referring to FIG.
  • determining the speed or velocity of the person 1 12 can include comparing successive ranges of the person. As a person 1 12 moves toward or away from the transmitting antenna 104 and receiving antenna 1 14, attenuation or reflection of the micro-impulse will occur respectively earlier or later relative to a given range delay.
  • determining the speed or velocity of the person 1 12 can include measuring at least one Doppler shift corresponding to the person. That is, a person 1 12 moving away from the transmitting antenna 104 and receiving antenna 1 14 will reflect a micro- impulse such that frequency components of the backscattered micro-impulse are red- shifted. Similarly, a person 1 12 moving toward the transmitting antenna 104 and receiving antenna 1 14 will reflect a micro-impulse such that frequency components of the backscattered micro-impulse are blue-shifted. The speed or velocity of the person 1 12 can be determined at least in part from the red-shift or blue-shift of the micro-impulse.
  • backscatter from the transmitted micro-impulse can be received through two or more receiving antennas 1 14, 1 14b separated from one another.
  • the speed or velocity of the person 1 12 can be determined by comparing successive positions or Doppler shifts corresponding to the person relative to the two or more receiving antennas 1 14, 1 14b. In a way, this can be viewed as triangulating the successive positions or successive speed or velocity components.
  • the speed or velocity of the person 1 12 can be determined by comparing successive positions or Doppler shifts corresponding to the person relative to two or more transmitting antennas 104. This can also be viewed as triangulating the successive positions or successive speed or velocity components.
  • step 904 media content is selected for display to the person responsive to the velocity or speed associated with the person.
  • step 904 can include outputting a query statement including data corresponding to the speed or velocity, and receiving a consumer profile or media selection responsive to the query statement.
  • a media output apparatus is controlled to output the media content to the person.
  • the media output apparatus can include one or more of a video display, a static electronic display, a loudspeaker, or a personal media player.
  • the process 901 can include selecting at least one media parameter other than media content.
  • Example media parameters are described above.
  • step 902 can include detecting a plurality of human attributes corresponding to the person.
  • the plurality of human attributes can include at least one physical attribute and at least one physiological attribute. Human attributes are described above.
  • the process can proceed to optional step 908 where one or more human attributes are assembled into a phenotypic profile.
  • the phenotypic profile can also include the speed or velocity of the person.
  • the process 901 can then proceed to optional step 910, where the phenotypic profile is correlated to a phenotypic identity or individual identity corresponding to the person.
  • optional step 912 can include outputting a query statement including data corresponding to the phenotypic profile, phenotypic identity, or individual identity, and receiving a consumer profile or media selection responsive to the query statement.
  • Step 904 can thus include selecting the media content responsive to the phenotypic profile, phenotypic identity or individual identity.
  • the process 901 can include selection of a media output apparatus that best corresponds to a path taken by the person.
  • Optional step 914 includes inferring or determining a path of the person.
  • Optional step 916 then includes receiving, determining, or inferring an apparatus identity. This can be used to select one or more of a plurality of media output apparatuses.
  • step 916 can include outputting a query statement including data corresponding to the speed or velocity, and receiving an output apparatus identity responsive to the query statement.
  • Step 906 can thus include causing the media to be output to the person responsive to the apparatus identity.
  • the media output apparatus identity can correspond to a media output apparatus positioned to be seen or heard by the person after the person moves away from the MIR.
  • step 906 can include causing the media to be output on a media output apparatus positioned to be seen or heard by the person as the person travels along a path inferred from one or more of the speed, velocity, or a consumer profile.
  • the process 901 can include step 918 of receiving data from or otherwise communicating with other MIRs and/or other controllers.
  • step 918 can include operating one or more second MIRs to detect a second speed or velocity associated with the person, transmitting the speed or velocity to one or more second controllers, or receiving a second speed or velocity from one or more second controllers.
  • Step 914 can thus include cooperating with one or more second MIRs or controllers to plot a path traveled by at least the person.
  • Step 916 can include cooperating with one or more second MIRs or controllers to select a media output apparatus responsive to the speed or velocity and/or path information determined in step 914.
  • step 912 can include cooperating with one or more second MIRs or controllers to cause the media to be output to the person responsive to a consumer profile, or a media selection selected responsive to the speed or velocity.
  • the process 901 can include tracking the speed or velocity of a plurality of persons substantially simultaneously.
  • the plurality of persons can then have media content selected and directed to an appropriate media output apparatus as a function of the MIR data and/or information sent to or received from other MIRs and/or controllers.

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Abstract

L'invention porte sur un ou sur plusieurs radars à micro-impulsions (MIR) qui sont configurés pour déterminer le mouvement d'au moins une personne. Des supports peuvent être émis pour la personne en réponse au mouvement.
EP11834761.6A 2010-10-20 2011-10-20 Procédé et appareil de mesure du mouvement d'une personne Withdrawn EP2630624A4 (fr)

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US12/925,407 US20110166937A1 (en) 2010-01-05 2010-10-20 Media output with micro-impulse radar feedback of physiological response
US12/928,703 US9024814B2 (en) 2010-01-05 2010-12-16 Tracking identities of persons using micro-impulse radar
US12/930,043 US9019149B2 (en) 2010-01-05 2010-12-22 Method and apparatus for measuring the motion of a person
PCT/US2011/001790 WO2012054087A1 (fr) 2010-10-20 2011-10-20 Procédé et appareil de mesure du mouvement d'une personne

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