as) United States
`a2) Patent Application Publication co) Pub. No.: US 2010/0217533 Al
` Nadkarniet al. (43) Pub. Date: Aug. 26, 2010
`
`
`
`US 20100217533A1
`
`(54)
`
`(75)
`
`IDENTIFYING A TYPE OF MOTION OF AN
`OBJECT
`Inventors:
`
`Vijay Nadkarni, San Jose, CA
`(US); Jeetendra Jangle, Fremont,
`CA (US); John Bentley, Santa
`Clara, CA (US); UmangSalgia,
`Nigadi (IN)
`
`Correspondence Address:
`Law Office of Brian Short
`P.O. Box 641867
`San Jose, CA 95164-1867 (US)
`(73) Assignee:
`LABURNUM NETWORKS
`.
`INC., San Jose, CA (US)
`
`$
`
`(21) Appl. No.:
`
`12/560,069
`
`(22)
`
`Filed:
`
`Sep. 15, 2009
`
`Related U.S. Application Data
`(60) Provisional application No. 61/208,344,filed on Feb.
`23, 2009.
`o,
`.
`.
`Publication Classification
`
`(51)
`
`(52)
`(57)
`
`Int. Cl.
`(2006.01)
`GO1P 15/00
`(2006.01)
`GO6F 19/00
`(2006.01)
`GO6F 17/18
`UWS. C1. oo ececccccccceeerteeees 702/19; 702/141 3 702/179
`ABSTRACT
`
`A method of identifying a type of motion of an animate or
`inanimate object is disclosed. The method includes generat-
`ing an acceleration signaturebasedonthe sensedacceleration
`of the object. The acceleration signature is matched with at
`least one of a plurality of stored acceleration signatures,
`wherein each stored acceleration signatures corresponds with
`type of motion. The type of motionof the objectis identified
`based on the statistical matching or exact matching of the
`acceleration signature.
`
`620
`
`Generating an acceleration signature based on the sensed acceleration of the object
`
`61
`
`Matching the acceleration signature with at least one of a plurality of stored
`acceleration signatures, wherein each stored acceleration signatures corresponds with
`type of motion
`
`Identifying the type of motion of the object based onthestatistical matching or exact
`matching of the acceleration signature
`
`
`
`63
`
`1
`
`APPLE 1008
`
`APPLE 1008
`
`1
`
`

`

`Patent Application Publication
`
`Aug. 26,2010 Sheet 1 of 9
`
`US 2010/0217533 Al
`
`FIGURE 1
`
`2
`
`

`

`Patent Application Publication
`
`Aug. 26,2010 Sheet 2 of 9
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`US 2010/0217533 Al
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`Slow Falling and lying down summersault
`
`Acceleration o
`
`vgrannengnmmntpin
`Slippingandfallingonbackon a bouncysurface (air mattress) Acceleration
`
`FIGURE 2A
`
`
`
`FIGURE 2B
`
`3
`
`

`

`Patent Application Publication
`
`Aug. 26,2010 Sheet 3 of 9
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`US 2010/0217533 Al
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`Fallon face with knees flexed
`
`Acceleration
`¥
`T
`
`
`
`FIGURE 2C
`
`4
`
`

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`Patent Application Publication
`
`Aug. 26,2010 Sheet 4 of 9
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`US 2010/0217533 Al
`
`
`
`~
`
`| Lf
`
`AID
`
`320
`
`Compare
`
`330
`
`Library
`
`340
`
`
`
`
`312
`
`Acc.
`a4
`
`Acc.
`316
`
`
`
`Audio.
`360
`
`370
`
`|
`
`Controller
`=
`
`Communication
`
`FIGURE 3
`
`5
`
`

`

`Patent Application Publication
`
`Aug. 26,2010 Sheet 5 of 9
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`US 2010/0217533 Al
`
`Monitoring an activity of a person the motion detection device is attached
`410
`
`Performing instantaneous computations over raw signals to compute atomic motions
`along with gravity vector andtilt vector
`420
`
`430
`
`Applying series of digitalfilters to remove noise in the atomic motions data
`
`~~Performing state analysis on series of atomic data samples~~~
`440
`Tna
`eee
`
`—.
`
`Perioidic State Analysis
`445
`
`Transcient State Analysis
`450
`
`
`
`Formation of macro motion signatures
`460
`
`A learning system providing the right model for the user from a set of model
`470
`
`480
`
`Pre-building a motion database of motion libraries
`
`FIGURE 4
`
`6
`
`

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`Patent Application Publication
`
`Aug. 26, 2010 Sheet 6 of 9
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`US 2010/0217533 Al
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`
`
`
`
`
`
`
`
`Deviation in Motion
`
`
`Pattern Detected,
`Monitor
`recorded and
`
`
`Activity
`510
`
`reported
`
`
`515
`
`
`Fall Report
`
`
`Acknowledged
`Large acceleration
`
`
`threshold exceeded,
`580
`
`
`
`record audio
`
`
`
`920
`
`
`Another large
`
`Monitor
`
`acceleration
`
`
`Fall
`Prob. Fail
`
`magnitude detected
`
`
`Reported
`530
`575
`525
`
`
`
`
`
`
`545
`
`Normal Movement
`Detected, Audio Rec.
`Stopped
`
`Analysis of motion,
`positions indicates
`normal activity
`
`560
`
`Short Period of
`
`Inactivity
`535
`
`
`Send Alert, recording
`
`
`and analysis sent
`
`
`570
`
`Prob. Fall
`
`
`Detected
`
`340
`
`
`
`
`Fall
`
`Detected
`565 Period ofInactivity
`
` 550
`
`
`
`Analysis of motion,
`
`positions indicates a
`fall occurred
`
`260
`
`Analysis
`555
`
`FIGURE 5
`
`7
`
`

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`Patent Application Publication
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`Aug. 26,2010 Sheet 7 of 9
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`US 2010/0217533 Al
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`Generating an acceleration signature based on the sensed acceleration of the object
`
`610
`
`620
`
`Matching the acceleration signature with at least one ofa plurality of stored
`acceleration signatures, wherein each stored acceleration signatures corresponds with
`type of motion
`
`Identifying the type of motion of the object based on the statistical matching or exact
`matching of the acceleration signature
`
`
`
`30
`
`FIGURE6
`
`8
`
`

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`Patent Application Publication
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`Aug. 26,2010 Sheet 8 of 9
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`US 2010/0217533 Al
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`
`
`The motion detection device determining what network connections are available to
`the motion detection device
`
`
`10
`
`
`
`
`
`The motion detection device distributing at least some of the acceleration signature
`matching processing if processing capability is available to the motion detection device
`
`though available network connections
`720
`
`
`
`
`
`FIGURE 7
`
`9
`
`

`

`Patent Application Publication
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`Aug. 26,2010 Sheet 9 of 9
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`US 2010/0217533 Al
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`810
`Blue Tooth
`
`ihLL
`
`wee
`
`
`300
`
`“
`
`
`
`Processor
`
`rome
`
`aeNN
`
`NoNetwork
`
`Available
`
`Processor
`830
`
`Cellular
`820
`
`\
`ale
`
`
` 850
`
` Processor
`
`Home
`Base Station
`840
`
`FIGURE 8
`
`10
`
`10
`
`

`

`US 2010/0217533 Al
`
`Aug. 26, 2010
`
`IDENTIFYING A TYPE OF MOTION OF AN
`OBJECT
`
`RELATED APPLICATIONS
`
`[0001] This patent application claims priority to U.S. pro-
`visional patent application Ser. No. 61/208,344filed on Feb.
`23, 2009 whichis incorporated by reference.
`
`FIELD OF THE DESCRIBED EMBODIMENTS
`
`[0002] The described embodiments relate generally to
`motion detecting. More particularly, the described embodi-
`ments relate to a method and apparatusfor identifying a type
`of motion of an animate or inanimate object.
`
`BACKGROUND
`
`motion of the object is identified based on the statistical
`matching or exact matching of the acceleration signature.
`[0009] Other aspects and advantages of the described
`embodiments will become apparent from the following
`detailed description, taken in conjunction with the accompa-
`nying drawings,illustrating by way of example the principles
`of the described embodiments.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 shows examples ofdifferent types ofmotions
`[0010]
`of a humanbeingthat an object attached to the human being
`can be usedto detect or sense.
`[0011]
`FIGS. 2A, 2B, 2C shows examples of time-lines of
`several different acceleration curves (signatures), wherein
`each signature is associated with a different type of sensed or
`detected motion.
`
`FIG. 4 is a flowchart that includes the steps of an
`[0013]
`example of a method for detecting various motions of daily
`living activities and emergencysituations, suchas, a fall.
`[0014]
`FIG. 5 is a flowchart that includes the steps of a
`method for detection ofa fall.
`
`[0003] There is an increasing need for remote monitoring
`[0012] FIG.3is an example ofa block diagram ofa motion
`
`ofindividuals, animals and inanimate objectsin their daily or
`detection device.
`natural habitats. Many seniors live independently and need to
`have their safety and wellness tracked. A large percentage of
`society is fitness conscious, and desire to have, for example,
`workouts and exercise regimen assessed. Public safety offic-
`ers, such as police and firemen, encounter hazardoussitua-
`tions ona frequentbasis, and need their movements, activities
`FIG. 6 is a flow chart that includes the steps of one
`[0015]
`and location to be mappedoutprecisely.
`example of a method of identifying a type of motion of an
`[0004] The value in such knowledge is enormous. Physi-
`animate or inanimate object.
`cians, for example, like to know their patients sleeping pat-
`[0016]
`FIG. 7 is a flow chart that includes steps of one
`terns so they can treat sleep disorders. A senior living inde-
`example of a method of a motion detection device checking
`pendently wants peace of mindthatif he hasafall it will be
`network availability for improvements in speed and/or pro-
`detected automatically and help summoned immediately. A
`cessing powerof acceleration signature matching.
`fitness enthusiast wants to track her daily workout routine,
`[0017]
`FIG. 8 shows a motion detection device that can be
`capturing the various types of exercises, intensity, duration
`connected to one of multiple networks.
`and caloric burn. A caregiver wants to know that herfatheris
`living an active, healthy lifestyle and taking his daily walks.
`The police would like to know instantly when someonehas
`been involved in a car collision, and whether the victims are
`moving or not.
`[0005] Existing products for the detection of animate and
`inanimate motionsare simplistic in nature, and incapable of
`interpreting anything more than simple atomic movements,
`such as jolts, changes in orientation and thelike. It is not
`possible to draw reliable conclusions about human behavior
`from these simplistic assessments.
`[0006]
`It is desirable to have an apparatus and methodthat
`can accurately monitor motion of either animate of inanimate
`objects.
`
`DETAILED DESCRIPTION
`
`[0018] The monitoring of humanactivities generally falls
`into three categories: safety, daily lifestyle, and fitness. By
`carefully interpreting human movementsit is possible to draw
`accurate and reasonably complete inferences about the state
`of well being of individuals. A high degree of sophistication
`is required in these interpretations. Simplistic assessments of
`humanactivity lead to inaccurate determinations, and ulti-
`mately are of questionable value. By contrast, a comprehen-
`sive assessment leads to an accurate interpretation and can
`prove to be indispensablein tracking the well being andsafety
`of the individual.
`
`SUMMARY
`
`[0007] An embodimentincludes a method of identifying a
`type ofmotion of an animate or inanimate object. The method
`includes generating an acceleration signature based on the
`sensed acceleration of the object. The acceleration signature
`is matched with at least one of a plurality of stored accelera-
`tion signatures, wherein each stored acceleration signatures
`corresponds with type of motion. The type of motion of the
`object is identified based on thestatistical matching or exact
`matching ofthe acceleration signature.
`[0008] Another embodiment includes a method ofidenti-
`fying a type of motion of a person. The method includes
`generating an acceleration signature based on the sensed
`acceleration of an object attached to the person. The accel-
`eration signature is matched with atleast one of a plurality of
`stored acceleration signatures, wherein each stored accelera-
`tion signatures corresponds with type of motion. The type of
`
`To draw accurate inferences about the behavior of
`[0019]
`humans, it turns out that the atomic movements becomesim-
`ply alphabets that include elemental motions. Furthermore,
`specific sequences of elemental motions becomethe vocabu-
`lary that comprises human behavior. As an example, take the
`case of a person who leaves the home and drives to the
`shopping center. In sucha scenario, the behavioral pattern of
`the person is walking to the dooror the house, opening and
`closing the door, walking further to the car, settling down in
`the car, starting the engine, accelerating the car, going
`through a series of stops, starts and turns, parking the car,
`getting out and closing the car door, andfinally walkingto the
`shopping center. This sequence of human behavior is com-
`prised of individual motions such as standing, walking,sit-
`ting, accelerating (in the car), decelerating, and turningleft or
`right. Each individual motion, for example walking, is com-
`prised of multiple atomic movements such as acceleration in
`an upwarddirection, acceleration in a downwarddirection, a
`
`11
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`US 2010/0217533 Al
`
`Aug. 26, 2010
`
`modest forward acceleration with each step, a modest decel-
`eration with each step, and so on.
`[0020] With written prose, letters by themselves convey
`almost no meaningat all. Words taken independently convey
`individual meaning, but do not provide the context to com-
`prehendthesituation. It takes a complete sentence to obtain
`that context. Along the sameline of reasoning, it requires a
`comprehension of a complete sequence of movements to be
`able to interpret human behavior.
`[0021] Although there is an undeniable use for products
`that are able to detect complex human movementsaccurately,
`the key to the success of such technologies lies in whether
`users adopt them or not. The technology needs to capture a
`wide range of human activities. The range of movements
`should ideally extendto all types ofdaily living activities that
`a human being expects to encounter—sleeping, standing,
`walking, running, aerobics, fitness workouts, climbingstairs,
`vehicular movements, falling, jumping and colliding, to name
`some of the more commonones.
`
`Itisimportant to detect humanactivities with a great
`[0022]
`deal of precision.In particular, activities that relate to safety,
`fitness, vehicular movements, and day to daylifestyle pat-
`terns such as walking, sleeping, climbing stairs, are important
`to identify precisely. For example,it is not enough to know
`that a person is walking. One needs to know the pace and
`duration of the walk, and additional knowledge of gait,
`unsteadiness, limping, cadence andthe like are important.
`[0023]
`Itis critical that false positives as well as false nega-
`tives be eliminated. This is especially important for cases of
`safety, such as falls, collisions, and the like. Human beings
`comein all types—short, tall, skinny, obese, male, female,
`athletic, couch potato, people walking with stick/rolator,
`people with disabilities, old and young. The product needs to
`be able to adaptto their individuality andlifestyle.
`[0024] The embodiments described provide identification
`oftypes of motion of an animate or inanimate object. Motion
`is identified by generating acceleration signatures based on
`the sensed acceleration of the object. The acceleration signa-
`tures are compared with a library of motion signature, allow-
`ing the motion of the object
`to be identified. Further,
`sequences of the motions can be determined, allowing iden-
`tification of activities of, for example, a person the objectis
`attachedto.
`
`Just as the handwritten signatures of a given human
`[0025]
`being are substantively similar from one signature instance to
`the next, yet have minordeviations with each new instance, so
`too will the motion signatures of a given humanbe substan-
`tively similar from one motion instanceto the next, yet have
`minor deviations.
`
`[0026] Algorithms used for pattern recognition (signature
`matching) should have the sophistication to accurately handle
`a wide range of motions. Such algorithms should have the
`ability to recognizethe identical characteristics ofa particular
`motion by a given humanbeing, yet allow for minor varia-
`tions arising from human randomness. Additionally,
`the
`devices used to monitor peoples’ movement need to be min-
`iature and easy to wear. These two objectives are fundamen-
`tally opposed. However, the described embodiments provide
`a single cohesive system that is both sophisticated enough to
`detect a wide range of motions.
`[0027] FIG.1 shows examples ofdifferent types ofmotions
`of a humanbeingthat an object attached to the human being
`can be used to detect or sense. The human motions can
`
`include, for example, standing, sleeping, walking, and run-
`
`ning. A first motion 110 can include walking. A second
`motion 120 can include falling. A third motion 130 can
`include running. Each of the motions generates a unique
`motion signature. As will be described, the signatures can be
`universal to, for example, many individuals. Additionally, the
`signatures can have additional characteristics that are unique
`to an individual.
`
`[0028] FIGS.2A,2B,2C shows examplesofdifferent types
`of acceleration and orientation signatures for various sample
`motions by human beings. It should be noted that these sig-
`natures are expected to have certain components that are
`common from one human being to the next, but also have
`certain components that vary from one human to the next.
`[0029] The signatures of FIGS. 2A, 2B, 2C are depicted in
`only one orientation. That is, three accelerometers can be
`used to generate acceleration signatures in the X, Y and Z
`(three) orientations. The signatures of FIGS. 2A, 2B, 2C only
`show the signature of one of the three orientations. It is to be
`understood that matching can use the other orientations as
`well.
`
`FIG. 2A shows an example of an acceleration sig-
`[0030]
`nature of a person doing a slow fall and lying down summer-
`sault. FIG. 2B shows an exampleofan acceleration signature
`ofa person slipping andfalling back on a bouncysurface (for
`example, an air mattress). FIG. 2C shows an acceleration
`signature of a personfall on their face with their kneesflexed.
`By matching an acceleration signature that has been gener-
`ated by sensing the motion of a person with one of many
`stored signatures, the motion ofthe person can be determined.
`[0031]
`FIG. 3 is anexample ofa block diagram ofa motion
`detection device. The motion detection device can be attached
`
`to an object, and therefore, detect motion ofthe object that can
`be identified. Based on the identified motion, estimates ofthe
`behavior and conditions of the object can be determined.
`[0032] The motion detection device includes sensors (such
`as, accelerometers) that detect motion of the object. One
`embodimentofthe sensors includes accelerometers 312, 314,
`316 that can sense, for example, acceleration of the object in
`X,Y and Z directional orientations. It is to be understood that
`other types of motion detection sensors can alternatively be
`used.
`
`[0033] An analogto digital converter (ADC) digitizes ana-
`log accelerometer signals. The digitized signals are received
`by compare processing circuitry 330 that comparesthe digi-
`tized accelerometer signals with signatures that have been
`stored within a library of signatures 340. Each signature
`corresponds with a type of motion. Therefore, when a match
`between the digitized accelerometer signals and a signature
`stored in the library 340, the type of motion experienced by
`the motion detection device can determined.
`
`[0034] An embodimentincludesfiltering the accelerometer
`signals before attempting to match the signatures. Addition-
`ally, the matching process can be made simpler by reducing
`the possible signature matches.
`[0035] An embodiment
`includes identifying a previous
`humanactivity context. That is, for example, by knowingthat
`the previous humanactivity was walking, certain signatures
`can intelligently be eliminated from the possible matches of
`the present activity that occurs subsequent to the previous
`humanactivity (walking).
`[0036] An embodimentincludes additionally reducing the
`numberofpossible signature matches by performing a time-
`domain analysis on the accelerometer signal. The time-do-
`main analysis can be used to identify a transient or steady-
`
`12
`
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`

`US 2010/0217533 Al
`
`Aug. 26, 2010
`
`is, for
`state signature of the accelerometer signal. That
`example, a walk may have a prominentsteady-state signature,
`whereas a fall may have a prominent transient signature.
`Identification of the transient or steady-state signature of the
`accelerometer signal can further reduceor eliminate the num-
`ber of possible signature matches, and therefore, make the
`task ofmatching the accelerometer signature with a signature
`within the library of signature simpler, and easier to accom-
`plish. More specifically, the required signal processing is
`simpler, easier, and requires less computing power.
`[0037] Upon detection of certain types of motion, an audio
`device 360 and/or a global positioning system (GPS) 370 can
`engaged to provide additional informationthat can be used to
`determine the situation of, for example, a human being the
`motion detection device is attachedto.
`
`[0038] Thecondition, or informationrelating to the motion
`detection device can be communicated through a wired or
`wireless connection. A receiver of the information can pro-
`cess it, and make a determination regarding the status of the
`humanbeing the motion detection device is attached to.
`[0039]
`FIG. 4 is a flowchart that includes the steps of an
`example of a method for detecting various motionsof daily
`living activities and emergency situations, such as, a fall. A
`first step 410 includes monitoring an activity of a person the
`motion detection device is attached. Raw signal data is col-
`lected from, for example, an accelerometer sensor. A second
`step 420 includes performing instantaneous computations
`over raw signals to compute atomic motions along with grav-
`ity vectorandtilt vector. A step third step 430 includes apply-
`ing series of digital filters to remove noise in the atomic
`motions data. A fourth step 440 includes performing state
`analysis on series of atomic data samples and forming con-
`text. Depending onthe state analysis, the series of atomic data
`is passed through either a step 445 periodic or steady state
`data analysis or a step 450 transient state data analysis. A sixth
`step 460 includes formation of macro motion signatures. The
`macro motion signatures are built from an output of state
`analysis vectors using known wavelet transformation tech-
`niques (for example, a Haar Transform). The transform per-
`formspattern matching on current motion pattern with exist-
`ing motion pattern library using, for example, DWT (Discreet
`Wavelet Transform) techniques. Complex motion wavelets
`are later matched using statistical pattern matching tech-
`niques, such as, HHMM (Hidden Heuristic Markov Model).
`Thestatistical pattern matching includes detecting and clas-
`sifying events of interest. The events of interest are built by
`observing various motions and orientation states data of an
`animate or inanimate object. This data is used to train the
`statistical model which performs the motion/activity detec-
`tion. Each activity will have its own modeltrained based on
`the observed data. A seventh step 470 includes a learning
`system providing the right model for the user from a set of
`model. It also aids in building newer (personal) patterns
`whichare not in the library for the person whois wearing the
`motion detection device. An eighth step 480 includes pre-
`building a motion database of motionlibraries against which
`motion signatures are compared. The database adds new
`motion/states signature dynamically as they are identified.
`[0040]
`FIG. 5 is a flowchart that includes the steps of an
`example of a method for detecting a fall. A first step 510
`includes monitoring an activity of, for example, a person the
`motion detection device is attached to. A step 515 includes
`recording and reporting in deviations in normal motionpat-
`terns of the person. A step 520 includes detecting the accel-
`
`eration magnitude deviation exceeding a threshold. The
`acceleration magnitude deviation exceeding the threshold
`can be sensed as a probable fall, and audio recording is
`initiated. Upon detection ofthis condition, sound recording of
`the person the motion detection device is connected to can be
`activated. The activation of sound can provide additional
`information thatcan be useful in assessing thesituation ofthe
`person. A step 530 includes monitoring the person after the
`probable fan. A step 525 includes detection of another accel-
`eration having magnitude lesser than the threshold, and con-
`tinuing monitoring of audio. A step 535 includes detecting a
`short period of inactivity. A step 540 includes monitoring the
`person after determininga fall probably occurred. A step 545
`includes subsequently detecting normal types of motion and
`turning offthe audio because the person seemsto be perform-
`ing normalactivity. A step 550 includes monitoring a period
`of inactivity. A step 555 includes additional analysis of
`detected information and signals. A step 560 includes further
`analysis including motiondata, orientation detectionall indi-
`cating the personis functioning normally. A step 560 includes
`determining that a fall has occurred based on the analysis of
`the motion data, and analysis of a concluded endposition and
`orientation of the person. The sound recording can be de-
`activated. A step 565 includes concluding that a fall has
`occurred. A step 570 includes sending an alert and reporting
`sound recordings. A step 575 includesthe fall having been
`reported. A step 580 includes an acknowledgementofthefall.
`[0041]
`FIG. 6 is a flow chart that includes the steps of one
`example of a method of identifying a type of motion of an
`animate or inanimate object. A first step 610 includes gener-
`ating an acceleration signature (for example, a tri-axial) based
`on the sensed acceleration of the object. A second step 620
`includes matching the acceleration signature with atleast one
`of a plurality of stored acceleration signatures, wherein each
`stored acceleration signatures corresponds with type of
`motion. A third step 630 includes identifying the type of
`motion of the object based on thestatistical (pattern) match-
`ing or exact matching ofthe acceleration signature. As will be
`described, the acceleration signal can be created using a
`wavelet transformation.
`
`For embodiments, the type of motion includes at
`[0042]
`least one of atomic motion, elemental motion and macro-
`motion.
`
`[0043] Though embodiments of generating matching
`acceleration signatures are described, it is to be understood
`that additional or alternate embodiments can include gener-
`ating and matching of orientation and/or audio signatures.
`Correspondingly,the first step 610 can include generating an
`acceleration signature, (and/or) orientation and audio signa-
`ture based on the sensed acceleration,orientation ofthe object
`and audio generated by the object, for example, a thud ofa
`fall, or a cry for help.
`[0044] Atomic motion includes butis not limited to a sharp
`jolt, a gentle acceleration, completestillness, a light accelera-
`tion that becomesstronger, a strong acceleration that fades, a
`sinusoidal or quasi-sinusoidal acceleration pattern, vehicular
`acceleration, vehicular deceleration, vehicular left and right
`turns, and more.
`[0045] Elemental motion includes but is not limited to
`motion patterns for walking, running, fitness motions (e.g.
`elliptical machine exercises, rowing,stair climbing, aerobics,
`skipping rope, bicycling .
`.
`. ), vehicular traversal, sleeping,
`sitting, crawling, turning over in bed, getting out of bed,
`getting up from chair, and more.
`
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`

`US 2010/0217533 Al
`
`Aug. 26, 2010
`
`[0046] Macro-motion includes but is not limited to going
`for a walk in the park, leaving home anddriving to the shop-
`ping center, getting out of bed and visiting the bathroom,
`performing household chores, playing a game of tennis, and
`more.
`
`[0047] Each ofthe plurality of stored acceleration signa-
`tures correspondswith a particular type ofmotion. By match-
`ing the detected acceleration signature of the object with at
`least one of a plurality of stored acceleration signatures, an
`estimate or educated guess can be made about the detected
`acceleration signature.
`[0048] An embodiment includes a commonlibrary and a
`specific library, and matching the acceleration signature
`includes matching the acceleration signature with stored
`acceleration signatures of the common library, and then
`matching the acceleration signature with stored acceleration
`signatures of the specific library. For a particular embodi-
`ment, the generallibrary includes universal acceleration sig-
`natures, and the specific library includes personal accelera-
`tion signatures. That is, for example, the stored acceleration
`signatures of the commonlibrary are useable for matching
`acceleration signatures of motions of multiple humans, and
`the stored acceleration signatures of the specific library are
`useable for matching acceleration signatures of motionsof a
`particular human. Additionally, each library can be further
`categorized to reduce the numberof possible matches. For
`example, at an initialization, a user may enter physical char-
`acteristics of the user, such as, age, sex, and/or physical char-
`acteristics (such as, the user has a limp). Thereby, the possible
`signature matches within the generallibrary can be reduced.
`The signature entries within the specific library can be
`learned (built) over time as the human wearing the motion
`detection device goes through normalactivities ofthe specific
`human. The specific library can be added to, and improved
`over time.
`
`[0049] An embodimentincludesfiltering the acceleration
`signals. Additional embodimentinclude reducing the number
`of stored acceleration signature matches by identifying a
`previousactivity of the object, and performing a time domain
`analysis on the filtered acceleration signal to identify tran-
`sient signatures or steady-state signatures of the filtered
`acceleration signal. Thatis, by identifying a previousactivity
`(for example, a human walking of sleeping) the possible
`numberofpresent activities can be reduced,andtherefore, the
`number of possible stored acceleration signature matches
`reduced. Additionally, the transient and/or steady-state sig-
`natures can be used to reduce the numberofpossible stored
`acceleration signature matches, which can improvethe pro-
`cessing speed.
`includes activating audio
`[0050] Another embodiment
`sensing of the object if matches are made with at least por-
`tions of particular
`stored acceleration signatures. For
`example, if the acceleration signature exceeds a threshold
`value, then audio sensing of the object is activated. This is
`useful because the audio information can provide additional
`clues as to what, for example, the condition of a person. That
`is, a fall may be detected, and audio information can be used
`to confirm that a fall hasin fact occurred.
`
`includes transmitting the
`[0051] Another embodiment
`sensed audio. For example, of a user wearing the object has
`fallen, and the fall has been detected, audio information can
`be very useful for determining the condition of the user. The
`audio information can allow a receiver of the audio informa-
`
`tion to determine, for example, if the useris in pain, uncon-
`scious or ina dangeroussituation (for example, in a showeror
`ina fire).
`[0052] An embodimentincludes the object being associ-
`ated a person, and the stored acceleration signatures corre-
`sponding with different types of motionrelated to the person.
`A particular embodiment includes identifying an activity of
`the person based on a sequence ofidentified motions of the
`person. The activity of the person can include, for example,
`falling (the most important in some applications), walking,
`running, driving and more. Furthermore, the activities can be
`classified as daily living activities such as walking, running,
`sitting, sleeping, driving, climbing stairs, and more, or spo-
`radic activities, such as falling, having a car collision, having
`a seizure and so on.
`
`[0053] An embodiment includes transmitting information
`related to the identified type of motion if matches are made
`with particular stored acceleration signatures. The informa-
`tion related to the identified type ofmotion can includeatleast
`one of motions associated with a person the object is associ-
`ated with. The motionscan include, for example, a heartbeat
`of the person, muscular spasms, facial twitches, involuntary
`reflex movements which can be sensed by, for example, an
`accelerometer. Additionally, the information related to the
`identified type of motion can includeat least one of location
`of the object, audio sensed by the object, temperature of the
`object.
`[0054] Another embodimentincludesstoring at least one of
`the plurality of stored acceleration signatures during an ini-
`tialization cycle. The initializing cycle can be influenced
`based on whatthe objectis attachedto. Thatis, initializing the
`stored acceleration signatures (motion patterns) can be based
`on what the object is attached to, which can both reduce the
`numberof signature required to be store within, for example,
`the general library, and therefore, reduce the numberofpos-
`sible matches and reducethe processing required to identify a
`match. Alternatively or additionally, initializing the stored
`acceleration signatures can be based on who the object is
`attached to, which can influence the specific library. The
`initialization can be used to determine motions unique, for
`example, to an individual. For example, a unique motion can
`be identified for a person who walks with a limp, and the
`device can be initialized with motion patterns of the person
`walking with a limp.
`[0055] An embodiment includes initiating a low-power
`sleep modeof the object if sensed acceleration is below a
`threshold for a predetermined amountoftime. Thatis,