CN103717994B - Motion determines - Google Patents
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- CN103717994B CN103717994B CN201280037709.8A CN201280037709A CN103717994B CN 103717994 B CN103717994 B CN 103717994B CN 201280037709 A CN201280037709 A CN 201280037709A CN 103717994 B CN103717994 B CN 103717994B
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Abstract
There is described herein and motion detection algorithm can be used to determine whether sensor has gone through motion event or do not have the system and method for motion event.This sensor can be used for identifying and/or characterizing any sensor of motion.Once receive a signal from this sensor, the moment of this signal can be calculated.It is then possible to these moments are compared for determining whether this signal is gaussian signal.If this signal is a gaussian signal, this algorithm determines that this signal is owing to one does not has motion event to produce.If this signal is a non-Gaussian signal, this algorithm determines that this signal is to produce due to a motion event.
Description
Cross-Reference to Related Applications
This subject application requires U.S. Patent Application Serial Number to be 13/164,136, submits to day to be on June 20th, 2011, marks
The priority of entitled " motion determines (MOTION DETERMINATION) ", present context is incorporated herein by reference.
Technical field
Present disclosure generally relates to determine whether the measured value from sensor is owing to sensor is moved.
Background
Traditional movement detection systems can be in a period of time from sensor observation signal and verify these signals
For this time period in a little scope.If these signals in a little scope, were somebody's turn to do for this time period
Movement detection systems may determine that one does not has motion event to have occurred and that.But, do not have motion event to have occurred and that to one
Determination can take a long time (such as, 8 seconds) so that this movement detection systems inefficiency.
The defect of conventional motion detecting system described above is only used to provide some problems of current techniques
A summary, it is no intended to detailed.The other problems that state of the art exists, and some various non-limits described here
The attendant advantages of property embodiment processed can become the most substantially based on reading following detailed description.
General introduction
In order to provide a basic understanding to aspect more described here, below theme required for protection is proposed
One brief overview.This general introduction is not one and summarizes widely, and be not intended to identify crucial/conclusive element or
Describe the scope of theme required for protection.Its sole purpose is to propose some concepts as carrying subsequently using the form of a kind of simplification
The preamble in greater detail gone out.
Described here is to promote that a quick and reliable indication sensor has the most been moved or quiet
System and method only.This determines can be based on the analysis to the signal from sensor.More precisely, receive from sensor
During to signal, the moment of this signal can be analyzed so that it is determined that whether this signal is gaussian signal.This sensor can include appointing
What produces the sensor that Gaussian noise does not moves simultaneously or produces the response close to this Gaussian noise.Can be in conjunction with this algorithm
The example of the sensor used may include that gyroscope, accelerometer, compass, pressure transducer, range sensor, scope pass
Sensor or like this.This sensor can be used for identifying and/or characterizing any sensor of motion.Input is also
It can be the parameter derived from sensing data (such as quaternary number).
If this signal is confirmed as non-Gaussian signal, then this signal is produced owing to this sensor moves.
But, if this signal is confirmed as gaussian signal, these system and methods may determine that this sensor is not the most moved.As
Really this sensor is a gyroscope, and when these system and methods determine that this signal is a gaussian signal, it may be determined that
Offset of gyroscope.It addition, if known a priori sensor is not the most moved, these system and methods can use identical letter
Breath determines whether this sensor works and/or determine the quality of noise.
Description below and accompanying drawing give some schematic aspect of this specification.But, the instruction of these aspects can be adopted
By only some modes in the various modes of the principle of this specification.When combining these accompanying drawings and considering, other of this specification
Advantage and novel feature become obvious by described in detail below from this specification.
Brief Description Of Drawings
See drawings below and describe the multiple non-limiting and non-exhaustive embodiments that this theme discloses, wherein unless additionally
Illustrating, running through various view, same reference number refers to same parts.
Fig. 1 is a kind of to determine whether sensor has been subjected to a motion event or a system not having motion event
Theory diagram.
Fig. 2 be a kind of for determine sensor whether have been subjected to a motion event or one there is no motion event
The principle process flow diagram flow chart of method.
Fig. 3 is a kind of for determining that whether a signal is the principle process flow diagram flow chart of the method for gaussian signal.
Fig. 4 is one and for the third moment of estimation and has 25 samples and theoretical third moment that standard deviation is 1
(V3The drawing of the cumulative probability function of the difference between).
Fig. 5 is one and for the third moment of estimation and has 25 samples and theory three rank that standard deviation is 1,2 and 3
Square (V3The drawing of the cumulative probability function of the difference between).
Fig. 6 is one and for the third moment of estimation and has 25,50 and 100 samples and standard deviation is 1
Theoretical third moment (V3The drawing of the cumulative probability function of the difference between).
Fig. 7 is a kind of for determining that whether a signal is the principle process flow diagram flow chart of the method for gaussian signal.
Fig. 8 is a Fourth-order moment for estimation and theoretical Fourth-order moment (V4Painting of the cumulative probability function of the difference between)
Figure.
Fig. 9 is one and for the Fourth-order moment of estimation and has 25 samples and theoretical quadravalence that standard deviation is 1,2 and 3
Square (V4The drawing of the cumulative probability function of the difference between).
Figure 10 is the principle process flow diagram flow chart of a kind of method for updating offset of gyroscope.
Figure 11 is a kind of principle process flow diagram flow chart for determining method that sensor the most normally works.
Figure 12 illustrates an exemplary computer network, in the network, it is possible to implement various enforcements described here
Example.
Figure 13 illustrates an exemplary computing environments, in this context, it is possible to implement various embodiments described here.
Figure 14 illustrates an exemplary hand held equipment, in the device, it is possible to implement various enforcements described here
Example.
Figure 15 illustrates an exemplary hand held equipment, in the device, it is possible to implement various enforcements described here
Example.
Describe in detail
There is described herein the various of a kind of remote control equipment and the multiple method being used together with this remote control equipment
Non-limiting example.In the following description, many details are provided so that provide one or more embodiments is thorough
Understand.But, one skilled in the relevant art will recognize that this technology described can be in not having these details
Individual or multiple or put into practice when there is additive method, parts, material, and it is not limited to these concrete details
And example.In other instances, known structure, material and/or operation it are not illustrated in detail in or describe to avoid obscuring certain
A little aspects.
Run through this specification " embodiment " or " a kind of embodiment " is quoted and meant at least one embodiment
Include a concrete characteristic, structure or the feature described with being associated with this embodiment.Therefore, this specification is run through everywhere
Phrase " in one embodiment " or the most all quote same embodiment in " in one embodiment ".Additionally,
In one or more embodiments, special characteristic, structure or feature can combine in any suitable manner.
The word " exemplary " used hereinto means as example, example or displaying.For avoiding doubt, this type of example pair
Theme disclosed here is not restricted.Be described herein any aspect for " exemplary " should not necessarily be construed as than other aspects preferably or
Favorably, and also for getting rid of equivalent exemplary structure known to persons of ordinary skill in the art and technology.Additionally, at certain
In degree, the most do not use term " to include " in detailed description or the claims, list that " having ", " comprising " are similar with other
Word, this type of term is intended to include (" including " that with term the mode of the transitional word as an opening is similar) and does not get rid of and appoint
What additional or other factor.
As used in this specification, term " or " mean to include inclusive " or " rather than exclusiveness " or
Person ".Therefore, except as otherwise noted, or the most high-visible, and " X uses A or B " means that any natural inclusive is arranged
Row.That is, if X uses A；X uses B；Or X not only uses A but also use B, then satisfied in the case of any examples detailed above " X uses A
Or B ".Additionally, article " " and " a kind of " as used in the application and appended claims generally should be explained
For referring to " one or more ", except as otherwise noted or clear from needing to be directed to the context of a kind of single form
Visible.
Referring now to Fig. 1, illustrate one determine sensor whether have been subjected to a motion event or one do not have
The theory diagram of the system 100 of motion event.This system 100 can be a part for an electronic equipment (not shown).Pass through
Citing, and unrestricted, and this electronic equipment can be a mobile phone.
This system 100 can include a sensor chip 102.Such as, this sensor chip 102 can be one integrated
Circuit (IC) chip.This sensor chip 102 can have one or more sensor (such as, on this IC chip) and (not show
Go out).These sensors can be can produce a gaussian signal (such as, this signal can include meet a Gaussian dependence
Data) any type of sensor.Gyroscope can be may include that in conjunction with the example of the sensor that this algorithm uses, add
Velometer, compass, pressure transducer, range sensor, range sensor or like this.This sensor can be can
For identifying and/or characterize any sensor of motion.This system can also use to be derived from sensing data (such as quaternary number)
Parameter.
Such as, these sensors can be motion sensor based on MEMS.One motion sensor based on MEMS
Example is an accelerometer, and this accelerometer may be used for measuring linear acceleration.Based on based on mems accelerometer
Physical mechanism include electric capacity, varistor, electromagnetism, piezoelectricity, ferroelectricity, optics and tunnelling.Based on MEMS
Accelerometer can be made up of a cantilever beam with predetermined test mass (also referred to as detection quality seismic quality)
Simple device.Under the influence of external acceleration, this quality property location deflection from which.Measure in a kind of analog or digital mode
This deflection.Generally, measure at one group of fixed beam and the electric capacity that is attached between the qualitative one group of light beam of this detection.
Other kinds of accelerometer based on MEMS can comprise little adding bottom a dome the least
Hot device, this heater heats the air in this dome thus causes it to rise.Thermocouple on this dome determines the sky of heating
Gas arrives dome from where and leaves the measurement that the deflection at this center is the acceleration being applied to this sensor.Based on MEMS
Accelerometer is generally co-planar operation, and i.e. it is designed to an orientation-sensitive of only plane to mould.By by two
Equipment is vertically integrated on a single mould, can form a twin-axis accelerometer.Additional by increasing by one
Plane external equipment, can measure three axles.The accelerometer with integrated electronics provides reading electronic equipment and self-test
Performance.
Another example of motion sensor based on MEMS is a compass, and it is a kind of for determining relative to the earth
The instrument in the direction of magnetic pole.One compass can include a magnetized pointer, freely by himself and magnetic field of the earth pair
Accurate.Small-sized compass is typically built by two or three magnetic field sensors, and such as Hall element, is a microprocessor
Data are provided.Trigonometry can be used to calculate the correct course relative to this compass.One small-sized compass is often one point
Vertical element, this element output one is oriented ratio or digital or analogue signal with it.A controller or one can be passed through
This signal explained by individual microprocessor.This compass can use the internal electronic equipment of altitude calibration to measure this compass over the ground
The response of signal magnetic field.The example of the most obtainable baby compass includes Honeywell Int Inc (Honeywell
International Inc.) the HMC 1051Z single shaft sold and HMC 1052 twin shaft reluctive transducer, the miniature device of Asahi Chemical Industry
AK 8,973 3 axle electronic compass that part Co., Ltd. (Asahi Kasei Microdevices Corporation) sells and
The AMI 201 that Aichi, Janpan intelligent miniature Co., Ltd. (Aichi Micro Intelligent Corporation) is sold is (double
Axle) and AMI 302 (three axles) electrical compass module.
Another example based on MEMS motion sensor is a gyroscope, and it is a kind of based on the conservation of angular momentum former
Reason is for measuring or maintain the equipment of orientation.Gyroscope based on MEMS uses vibration detection quality.These quality are generally one
Vibrate at individual altofrequency.Along with this sensor outer housing rotates in inertial space, induct in this detection quality a Ke Liao
Profit power.This Coriolis force causes a vibration in an orthogonal plane and can measure the amplitude of this orthogonal motion.This
The equipment of type is also referred to as Coriolis oscillation gyro, this is because along with plane of oscillation is rotated, transducer detect
To response be to be caused by the Coriolis item (" Coriolis force ") in its equation of motion.Can be by a vibrational structure
Gyroscope realizes as a tuning fork resonator, a vibration wheel or a wineglass resonator using MEMS technology.
Those of ordinary skill in the art, it should also be appreciated that this subject innovation is not limited to equipment based on MEMS, discloses at this
Embodiment based on MEMS be exemplary, and can be with any sensor can being combined in a portable equipment
Realize this subject innovation.Gyroscope, accelerometer, sieve can be may include that in conjunction with the example of the sensor that this algorithm uses
Dish, pressure transducer, range sensor, range sensor or like this.This sensor can be used for identifying
And/or characterize any sensor of motion.Such as, quartz transducer can also be used at this.Can also be in this subject innovation
Use on micron or mm-scale, include mechanical part and the other kinds of sensor can being combined with electronic circuitry.
This sensor chip 102 can also have disposal ability and/or performance.Such as, this sensor chip 102 can have
There is a processor (such as, on this IC chip).This processor can be communicatively coupled to this sensor so that at this
Reason device can receive a signal from this sensor.Such as, on this sensor chip 102, this processor can be located in
Near this sensor.This is so that this processor receives a signal and/or data from this sensor and processes this signal
And/or data (such as, according to a kind of motion detection algorithm) are so that it is determined that this signal and/or data are due to a motion event
Or owing to one does not has motion event to produce.However, it is possible to limit the process energy being associated with this sensor chip 102
Power and/or performance (such as, due to dimension constraint).
In order to compensate limited disposal ability and/or performance, can be by this sensor chip 102 and a main process equipment
104 (such as, the CPU elements of a mobile device) are associated.This main process equipment 104 can also have disposal ability and/or property
Energy.The disposal ability of this main process equipment 104 and/or performance can be more than the disposal ability of this sensor chip 102 and/or property
Energy.
This main process equipment 104 can include the disposal ability of the processor that a ratio is associated with this sensor chip 102
Bigger processor (not shown).For example, it is possible to by a coupling 106 by this sensor chip 102 and this main process equipment 104
Communicatively couple.Such as, this coupling can include an I2C bus and/or a serial port.This sensor chip 102
Through this connection 106, a signal and/or data can be sent to this main process equipment 104 from these sensors.This main frame sets
The processor of standby 104 can be independently processed from this signal and/or data (such as, according to this motion detection algorithm) so that it is determined that be somebody's turn to do
Signal and/or data still do not have motion event to produce due to one due to a motion event.
This sensor chip 102 and this main process equipment 104 can be independently processed from the signal from sensor and/or number
According to.Make the sensor chip 102 with disposal ability run such as a kind of motion detection algorithm thus detect this signal and/or
Data are due to a motion event or owing to one does not has motion event and produce can be lowered through this connection 106
The traffic between sensor chip 102 and main process equipment 104.Such as, this main process equipment 104 receives a letter from this sensor
Number and/or data frequency can less than on sensor chip 102 processor receive data frequency.
Describe following non-limiting example thus provide context for system 100.This sensor chip 102 can include one
Individual gyroscope (is not shown).This gyroscope can send data to the processor being associated with this sensor chip 102 and wear
Cross this connection 106 and send data (such as, for parallel processing) to the processor being associated with this main process equipment 104.This gyro
The frequency that instrument sends data to main process equipment 104 can send number less than it to the processor being associated with sensor chip 102
According to frequency (such as, reducing the traffic between this sensor chip 102 and this main process equipment 104).With this sensor chip
102 processors being associated and the processor being associated with this main process equipment 104 can operate independently from a kind of motion detection
Such as, algorithm is so that it is determined that this data are due to a motion event (people moves mobile phone) or a not motion
Event (such as, noise) and produce.
When this main process equipment 104 determines that these data are not have motion event to produce due to one, this main frame is permissible
Record an offset of gyroscope.Such as, this offset of gyroscope can be applied to gyroscope signal thus reduce real rotation
Error between rate and the specific rotation of measurement.Such as, this offset of gyroscope can be used for temperature-compensating.This sensor device
The processor of 102 can also determine these data be due to one there is no motion event and produce and record one the most permissible
Offset of gyroscope for temperature-compensating.The processor of this sensor device 102 can utilize and pass normally through connection 106 (example
As, transport layer) it is sent for feeding the data of its motion detection algorithm.If the place being associated with this sensor device 102
Reason device determines that one does not has motion event to have occurred and that, and time period has pass by and/or a variations in temperature
Occurring, the processor being associated with this sensor device 102 can utilize gyro that is that it calculates and/or that read from this gyroscope
Instrument biasing updates these offset of gyroscopes.Can have similar functional with the processor that this main process equipment 104 is associated.
This is to legacy system improvement, the most in conventional systems, inquires that this gyroscope is positioned at the mobile shape of special time period
What state is, and if bias and temperature be not sent to main process equipment 104, read biasing and the temperature of this gyroscope
Degree.
Therefore, according to another non-limiting example, offset of gyroscope can have a dependency with temperature.When determining
During offset of gyroscope, this biasing and temperature can be saved and be sent to a kind of temperature compensation algorithm.Such as, over time
Passage, this temperature compensation algorithm is appreciated that the relation between this offset of gyroscope and temperature.Then, along with the change of temperature in figure
Change, this compensation can be applied to reduce overall gyro error.
For example, it is possible to run one (such as, on the hardware of this sensor device 102) on this sensor device 102
Offset of gyroscope algorithm.10008 additionally or alternatively, such as, a kind of temperature compensation algorithm can be run on main process equipment 104.
This offset of gyroscope can be needed by communicating back with this temperature compensation algorithm with the use of main process equipment 104, thus contribute to
This temperature compensation algorithm learns this temperature and offset of gyroscope relation.
Alternately, this offset of gyroscope algorithm can run on this sensor device 102.This offset of gyroscope algorithm
Can also run on this main process equipment 104.Equally run on this main process equipment 104 is this temperature compensation algorithm.
Along with this offset of gyroscope algorithm and this temperature compensation algorithm are all run on this main process equipment 104, such as, through this coupling
Communication between this sensor device 102 and this main process equipment 104 of 106 (such as, by I2C or serial port) has one
Reducing, this is owing to when this gyroscope is effective, typically gyro data is sent to this main process equipment 104, therefore
Additional data (including a not motion message and/or this offset of gyroscope) just there is no need to be sent.Even if in this biography
The offset of gyroscope algorithm run on sensor equipment 102 and this main process equipment 104 is different and/or uses different data speed
Rate, these algorithms can provide similar enough results that will be useful.
It is due to a fortune that Fig. 2, Fig. 3, Fig. 7, Figure 10 and Figure 11 illustrate for determining the signal from a sensor
Method that dynamic event still produces owing to one does not has a motion event and/or motion detection algorithm.For the simplicity explained,
These methodology are depicted and are described as a series of behavior.It is to be understood that and it is appreciated that the behavior shown and/or row
For order be not limiting as various embodiment.Such as, behavior according to various orders and/or simultaneously can occur, and other
Behavior does not herein proposes or describes.Additionally, the behavior of not all displaying can be required for coming according to disclosed theme
Realize these methodology.It addition, one skilled in the art will understand and appreciate that these methods can via a state diagram or
Event is alternately represented as a series of relevant state.Therefore, it should be further appreciated that and describe after this
These methods can be stored in goods (such as, a kind of computer-readable recording medium) thus contribute to this type of side
Method opinion carries and transfers to computer.Term " goods " is intended to include from any computer readable device, load as used herein
Addressable computer program in body or medium.For example, it is possible to by the processor being associated with this sensor chip 102 and/
Or the processor being associated with this main process equipment 104 is to perform these methods and/or algorithm.
Referring now to Fig. 2, illustrate a kind of for determining whether sensor has been subjected to a motion event or one
There is no the principle process flow diagram flow chart of the method 200 of motion event.At element 202, a processor can be from a sensor
Receive a signal and/or data.Gyroscope, acceleration can be may include that in conjunction with the example of the sensor that this algorithm uses
Meter, compass, pressure transducer, range sensor, range sensor or like this.This sensor can be used for
Identify and/or characterize any sensor of motion.Such as, these data can be quaternion algebra evidence.This processor can be with one
Sensor chip (such as, the sensor chip 102 in Fig. 1) is associated.Permissible with the processor that this sensor chip is associated
It is positioned near this sensor (such as, on an IC chip).This processor can also with a main process equipment (such as,
Main process equipment 104 in Fig. 1) it is associated.The processor being associated with this main process equipment can through a transport layer (such as,
Connection 106 in Fig. 1) receive signal and/or data from this sensor.
At element 202, this processor (processor of such as, being associated with this sensor chip and/or set with this main frame
The standby processor being associated) may determine that this signal is due to a motion event or one does not has motion event to produce
's.The processor being associated with this sensor chip and/or the processor being associated with this main process equipment can be applied independently
A kind of algorithm is so that it is determined that this signal and/or data are due to a motion event or one does not has motion event to produce
's.This processor can use such as algorithm described in Fig. 3 and/or Fig. 7 to determine this signal and/or data be due to
One motion event or one does not has motion event to produce.These methods can be based on by analyzing signal and/or number
According to type and this signal of determining and/or data are due to a motion event or does not has motion event to produce
's.Such as, by analyzing signal and the/type of data, it may be determined that this signal and/or data are a Gaussian process or one
Nongausian process.If this signal and/or data are determined as Gauss's, it can be assumed that one does not has motion event to send out
Raw.By contrast, if this signal and/or data are determined as non-gaussian, it can be assumed that a motion event has been sent out
Raw.It practice, false motion event do not have motion event more false than is less concerned by people, calculation the most described here
The fact that method utilizes.It should be understood that by these algorithms described here with other motion determine algorithm be combined thus
Strengthen the standard not having motion event further.
Referring now to Fig. 3, whether, illustrating one for determining the signal received from sensor and/or data is Gauss
The principle process flow diagram flow chart of method 300.As described above, if this signal and/or data are determined as Gauss, can
To determine that one does not has motion event to have occurred and that.By contrast, if this signal and/or data are determined as non-gaussian
, it may be determined that a motion event has occurred and that.
At element 302, a signal (such as, as described above) can be received from a sensor.This signal
The data that size is N can be included.Such as, these data can be.10008 additionally or alternatively, it is possible to use from sensor
All axles (such as, three axles) of data.This processor may determine that whether these data are the Gaussages using the moment estimated
According to.
At element 304, the first moment of the estimation for these data can be calculated.For example, it is possible to according to equation below
Formula calculates the first moment (T of this estimation1):
Wherein, T1Being first moment, N is the size of data, and xnData point in being.
At element 306, the second moment of the estimation for these data can be calculated.For example, it is possible to according to equation below
Formula calculates the second moment (T of this estimation2):
Wherein, T2Being the second moment of estimation, N is the size of data, and xnData point in being.
At element 308, the third moment of the estimation for these data can be calculated.For example, it is possible to according to equation below
Formula calculates the third moment (T of this estimation3):
Wherein, T3Being the third moment of estimation, N is the size of data, and xnData point in being.
If it is well known that a process is Gauss, can calculate all of from this first moment and this second moment
Three rank and High Order Moment.At element 310, can be by contrast according to equation 3 (T3) third moment estimated and theoretical three rank
Square determines whether this signal is Gauss.Can first moment (T based on this estimation1) and the second moment (T of this estimation2) calculating should
Theoretical third moment.Third moment (the T of this estimation3) and this theory third moment between error (V3) can be represented as:
V3=T3-3*T2*T1+2*T1 3Equation 4,
Wherein, V3It is the third moment (T of estimation3) and based on T1And T2Theoretical third moment between error, T1It it is this estimation
First moment and T2It it is the second moment of this estimation.
In a Gaussian process, this third moment T3Should be equal to by using real first order and second order moments to calculate
Moment.By using the estimation to moment and comparing the relation between them when using these estimations, this contrast is produced
Raw error should be little for a Gaussian process.Such as, V3Absolute value for Gaussian distributed process should
Should be in a threshold range.Therefore, if V3Absolute value less than this threshold value, this process is determined as Gauss, and
Data from this sensor are determined as owing to one does not has motion event to produce.By contrast, if V3Exhausted
To value more than this threshold value, this process is determined as non-gaussian, and from the data of this sensor be determined as due to
One motion event and produce.If this sensor noise is not strictly Gauss, this threshold value may have to be increased.
For the gaussian sequence (for example, as it is known that being Gauss) that known standard deviation is 1, can be according to equation
4 calculate V3.Referring now to Fig. 4, illustrate and there are 25 samples (N=25) and standard deviation is the V of 13Cumulative probability function
Drawing 400.This drawing 400 given, can arrange and obtain probability and the V not having motion event3Need wherein thus real
The now scope of this probability.Such as, for the probability of 80%, V3Must be in being about the threshold value of-0.5 and 0.5.
With V3The standard deviation that is used together of determination cumulative probability function is had an impact.Referring now to Fig. 5, illustrate tool
Having 25 samples (N=25) and standard deviation is 1 (sigma _ 1), 2 (sigma _ 2) and the V of 3 (sigma _ 3)3Accumulation general
The drawing of rate function.If this standard deviation is not 1, the shape of this cumulative probability function curve changes.Pass through standard deviation
Difference cube substantially extend V3Cumulative probability function.
For V3The sample number (N) of determination equally cumulative probability function is had an impact.Referring now to Fig. 6, illustrate tool
There are 25 (N=25), 50 (N=50) and the V of 100 (N=100) samples3The drawing 600 of cumulative probability function.As
Being shown in drawing 600, sample number (N) has the impact of square root (N) type for correcting this curve.It practice,
25 samples are found to be one good number quickly not having motion event with minority vacation motion event of acquisition.If the time
It not constraint, will more preferably use a higher sample number.
Fig. 7 shows the option contributing to realizing the more strict requirements not having big time loss.Fig. 7 shows
One for determining that whether the signal received from sensor and/or data be the principle process flow diagram flow chart of the method for Gauss.As
Described above, if this signal and/or data are determined as Gauss's, it may be determined that one does not has motion event
Occur.By contrast, if this signal and/or data are determined as non-gaussian, it may be determined that a motion event is
Occur.
At element 702, a signal (such as, as described above) can be received from a sensor.It is similar to
Element 302 above, this signal can comprise the data that size is N.Such as, these data can be.10008 additionally or alternatively,
All axles (such as, three axles) of the data from sensor can be used.This processor may determine that whether these data make
Gaussian data by well-defined moment.
At element 704, it is similar to element 304 above, the first moment of the estimation for these data can be calculated.Example
As, the first moment (T of this estimation can be calculated according to equation below1):
Wherein, T1Being the first moment of estimation, N is the size of data, and xnData point in being.
At element 706, it is similar to element 306 above, the second moment of the estimation for these data can be calculated.Example
As, the second moment (T of this estimation can be calculated according to equation below2):
Wherein, T2Being the second moment of estimation, N is the size of data, and xnData point in being.
At element 708, it is similar to element 308 above, the third moment of the estimation for these data can be calculated.Example
As, the third moment (T of this estimation can be calculated according to equation below3):
Wherein, T3Being the third moment of estimation, N is the size of data, and xnData point in being.
In order to realize more strict requirements, at element 712, the Fourth-order moment of the estimation for these data can be calculated.
For example, it is possible to calculate the Fourth-order moment (T of this estimation according to equation below4):
Wherein, T4Being the Fourth-order moment of estimation, N is the size of data, and xnData point in being.
If it is well known that a process is Gauss, can calculate all of from this first moment and this second moment
Three rank and High Order Moment (such as, including Fourth-order moment).At element 712, can be by contrast according to equation 5 (T4) estimate
Fourth-order moment and theoretical quadravalence Gaussian Moment determine whether this signal is Gauss.Can first moment (T based on this estimation1) and this estimate
Second moment (the T calculated2) calculate this theory Fourth-order moment.Fourth-order moment (the T of this estimation4) and this theory Fourth-order moment between error (V4)
Can be represented as:
V4=T4-3*T2 2+2*T1 4-4*V3*T1Equation 6,
Wherein, V4It is the Fourth-order moment (T of estimation4) and T based on estimation1、T2And V3Theoretical Fourth-order moment between error, T1
It is first moment and the T of this estimation2It is the second moment of this estimation, and V3Obtain via equation 4.Due to estimation difference,
V3It is probably non-zero.
In a Gaussian process, this Fourth-order moment T4Should be equal to by using real first order and second order moments to calculate
Moment.By using the estimation to moment and comparing the relation between them when using these estimations, this contrast is produced
Raw error should be little for a Gaussian process.Such as, V4Absolute value for Gaussian distributed process should
Should be in a threshold range.Therefore, if V4Absolute value less than this threshold value, this process is determined as Gauss, and
Data from this sensor are determined as owing to one does not has motion event to produce.By contrast, if V4Exhausted
To value more than this threshold value, this process is determined as non-gaussian, and from the data of this sensor be determined as due to
One motion event and produce.If this sensor noise is not strictly Gauss, this threshold value may have to be increased.
For the gaussian sequence (for example, as it is known that being Gauss) that known standard deviation is 1, can be according to equation
6 calculate V4.Referring now to Fig. 8, illustrate and there are 25 samples (N=25) and standard deviation is the V of 13Cumulative probability function
Drawing 800.This drawing 800 given, can arrange and obtain probability and the V not having motion event4Need wherein thus real
The now scope of this probability.
With V4The standard deviation that is used together of determination cumulative probability function is had an impact.Referring now to Fig. 9, illustrate tool
Having 25 samples (N=25) and standard deviation is 1 (sigma _ 1), 2 (sigma _ 2) and the V of 3 (sigma _ 3)4Accumulation general
The drawing of rate function.If this standard deviation is not 1, the shape of this cumulative probability function curve changes.Pass through standard deviation
The biquadratic of difference substantially extends V4Cumulative probability function.
Although not showing, for V4The sample number (N) of determination equally cumulative probability function is had an impact.It practice,
25 samples are to obtain a good number determined that quickly do not moves with minority vacation motion event.If the time is not about
Bundle, will more preferably use a higher sample number.
High Order Moment can be utilized in a motion event or a determination not having motion event.Such as institute in Fig. 3 and Fig. 7
The identical technology shown can be extended to more high math power.But, more high math power can increase complexity and lose such as Fig. 3 and
The Saving in time costs that third-order and fourthorder process demonstrated in Figure 7 provides.Additionally, this sensor (such as, gyroscope) can represent
Non-Gaussian feature for High Order Moment.But, by way of example, this five rank deviation can be obtained according to following equation:
V5=T5-15*T2 2*T1+20*T2*T1 3-6*T1 5-10*V3*T1 2Equation 7,
Wherein, T5It is five rank squares of estimation, T1It is the first moment of estimation, T2It is the second moment of estimation, and V3It is according to public affairs
Formula 4 obtains.
By a non-limiting example, for the more safety when not obtaining transition effect, can detect not
An only single motion event.For example, it is possible to three motion events of detection.Can use this second and the 3rd motion event make
It is a motion event or a determination not having motion event.Due to sample number very little (such as, N=25), this motion is obtained
The time of event is the least.
As a non-limiting example, this sensor can be a gyroscope and one do not have motion event really
Surely can aid in the biasing setting this gyroscope.Referring now to Figure 10, illustrate one for the side updating offset of gyroscope
The principle process flow diagram flow chart of method.At element 1002, receive a signal from a gyroscope.Such as, this signal can include
Size is the data of N.Such as, these data can be.10008 additionally or alternatively, it is possible to use all axles of gyro data
(such as, three axles).
At element 1004, it may be determined that (such as, according to the method for Fig. 3 or demonstrated in Figure 7) this signal is due to one
Individual do not have motion event to produce.Such as, this gyro data (such as) can be fed to the defined place of Fig. 3 or Fig. 7
In reason device.According to Fig. 3 or Fig. 7, it may be determined that these data are due to a motion event or owing to one does not has motion event
And produce.Such as, if being used for all values of each axle in suitable threshold range, it can be stated that a not motion
Event.
At element 1006, if it is determined that these data do not have motion event to produce due to one, can arrange this
The biasing of gyroscope.Such as, the biasing of this gyroscope can be configured so that the first moment (T of this estimation1).If it is known that this gyro
Whether instrument moves, it is easy to computing gyroscope biases.On a typical gyroscope, this standard deviation is typically 1 bit or 2
Bit, the most simply a few samples (such as, N=25) will experience a motion event or one does not has at this gyroscope
Provide a good biasing in the determination of motion event to estimate.When this gyroscope does not move, can be by using gyroscope
The meansigma methods of sensing data calculates this biasing (such as, T simply1)。
It practice, false motion event do not have motion event more false than is less concerned by people.This is due to one
The motion event that do not has of individual vacation can cause the wrong gyro biasing needing to be employed, but for a false motion event,
Offset correction will not occur.
If being attached to other sensors, such as accelerometer, gyroscope, pressure transducer, range sensor, scope sensing
Device or like this, then can also alternately through the method that such as Fig. 3 and/or Fig. 7 is shown run these data and
It is included in motion event/do not have motion event to determine.But, it is impossible to determine except top according to method as Figure 11 shows the
The biasing of the sensor beyond spiral shell instrument.
According to another non-limiting example, the method as described by Fig. 3 and Fig. 7 can be by during a test period
It is applied to determine that a sensor is good or bad.Referring now to Figure 11, illustrate one for whether determining sensor
The principle process flow diagram flow chart of the method the most normally worked.
At element 1102, one can be received from there being a sensor not having motion event to be subjected known to one
Signal.Such as, this signal can include the data that size is N.Such as, these data can be.10008 additionally or alternatively, permissible
Use all axles (such as, three axles) of the data from sensor.
At element 1104, it may be determined that (such as, according to the method for Fig. 3 or demonstrated in Figure 7) this signal is due to one
Individual do not have motion event to produce.For example, it is possible to these data (such as) are fed to by process defined in Fig. 3 or Fig. 7
In.According to Fig. 3 or Fig. 7, it may be determined that these data are due to a motion event or owing to one does not has motion event to produce
Raw.Such as, if being used for all values of each axle in suitable threshold range, it can be stated that a not motion thing
Part.
At element 1106, it may be determined that this sensor the most normally works.Such as, at element 1102,
Know that this sensor has one to be subjected not have motion event.At element 1104, if it is determined that this sensor is experiencing one
Motion event, this sensor can not normally work.But, at element 1104, if it is determined that this sensor is experiencing one
Individual do not have motion event, then known experiencing a sensor not having motion event and the most normally works.
If it is known that an equipment is not moved, the V as defined in Fig. 3 can be used in a test period3
Or the V as defined in Fig. 74Prove that a sensor is good or bad.For a concrete sensor, generally have
The standard deviation of one little distribution.If this distribution of standard deviation is unknown, then can be by using (T2–T1 2)3/2It is multiplied by this
The scope of a little values adjusts for the scope of a value not having motion event to receive.For this upper limit, (this upper limit is for one
Can be implemented for there is no motion event) there is a restriction remain desirable.
Referring now to Figure 12 to Figure 15, illustrate example calculation network 1200, computing environment 1300 and movement at this and set
Standby 1400,1500, these equipment can be conducive to the realization of system described above and method.Each width in Figure 12 to Figure 15
It is not used to limit, but the system and method on the contrary in order to describe upward provide an exemplary hardware environment.
Referring now to Figure 12, illustrate the non-limiting principle of an exemplary networked or distributed computing environment 1200
Figure.This distributed computing environment include calculating object 1210,1212 etc. and calculate object or equipment 1220,1222,1224,
1226,1228 etc., it can include the storage of program, method, data, FPGA etc., as application 1230,1232,1234,
1236, represented by 1238.It can be appreciated that object 1210,1212 etc. and calculate object or equipment 1220,1222,
1224,1226,1228 etc. can include different equipment, as remote controllers, PDA, audio/video devices, mobile phone,
MP3 player, notebook computer etc..
Each object 1210,1212 etc. and calculate object or equipment 1220,1222,1224,1226,1228 etc. can be via
Communication network 1240 either directly or indirectly with other objects 1210,1212 one or more etc. and calculate object or equipment
1220,1222,1224,1226,1228 etc. communicate.Although being shown as a single element in fig. 12, net
Network 1240 can include that other provide calculating object and the calculating equipment of service to the system shown in Figure 12, and/or can represent
Multiple interconnective networks, these networks are the most not shown.Each object 1210,1212 etc. or 1220,1222,1224,
1226,1228 etc. can also comprise an application possibly also with API or other objects, software, firmware and/or hardware (as should
With 1230,1232,1234,1236,1238), it is adaptable to communicate with the delay interaction models provided according to various embodiments
Or it realizes.
There are system, parts and the network configuration of multiple support distributed computing environment.For example, it is possible to by wired or nothing
The system of line, by local network or widely distributed network calculating system is linked together.Currently, many networks are by coupling
Closing the Internet, it provides an infrastructure for Distributed Calculation widely and comprises many different networks, although appointing
What network infrastructure may be used for the example communication that technology described in various embodiments occurs.
Therefore, it is possible to use substantial amounts of network topology and basic network equipment, as client/server, peer-to-peer network or
Hybrid architecture.In a client/server architecture, specifically in a networked system, client is usual
Being a computer, this computer accesses the shared Internet resources provided by another computer (such as a, server).?
In the explanation of Figure 12, as a non-limiting example, computer 1220,1222,1224,1226,1228 etc. may be considered that
Being client, and computer 1210,1212 etc. is considered server, wherein server 1210,1212 etc. provide number
According to service, as received data from client computer 1220,1222,1224,1226,1228 etc., data storage, data process,
Transfer data to client computer 1220,1222,1224,1226,1228 etc., although based on any computer of these environment
Be considered a client, a server or both have both at the same time.Any one in these calculating equipment is permissible
Being to process data, or request services or task, it can infer this delay interaction models and as described in this for one
Individual or the correlation technique of multiple embodiment.
Server is typically an addressable remote computer system in remotely-or locally network, such as the Internet or nothing
Line network infrastructure.In a first computer system, this client process can be movable, and at one second
In computer system, these server processes can be movable, is in communication with each other, thus provides distributed on a kind of communication media
Functional and allow multiple client to utilize the information gathering capability of this server.Can be on multiple calculating equipment or object
Any software object used according to service based on direction is provided individually or in a distributed manner.
Communications network/bus 1240 be the Internet network environment in, such as servers 1210,1212 etc. are permissible
Being Web server, based on this, client 1220,1222,1224,1226,1228 etc. is via any in multiple known protocols
Individual (such as HTTP (HTTP)) communicates.Server 1210,1212 etc. can also as client 1220,
1222,1224,1226,1228 etc., as being the feature of a distributed computing environment.
As a further non-limiting example, in conjunction with various embodiments described here, it is considered to use and retouch at this
That states is applicable to various any hand-held, portable and other calculating equipment and calculates the various embodiments of object, i.e.
One equipment can be asked whenever and wherever possible based on the service pointed to.Therefore, Universal Remote described in Figure 13 calculates below
Machine is an example, and can have network/bus interoperability and mutual client realizes this theme by any
These embodiments disclosed.
Although not necessarily, can partly realize any one in these embodiments via an operating system,
For the developer of the service for an equipment or object, and/or be included in combine this/these operable parts enter
In the application software of row operation.Can be by one or more computers (such as client station, server or other equipment)
Software described in the general context of the computer executable instructions (such as program module) performed.Those skilled in the art will recognize that
Arrive, can be with various computing systems configuration and actualizing network interaction.
Figure 13 illustrates the example of a suitable computing system environment 1300, can realize these in this context and implement
Example one or more, although as above explicitly indicating that, this computing system environment 1300 is only a suitable computing environment
One example, and it is not intended to suggestion using or any restriction of functional scope about these embodiments.The most should be by
Computing environment 1300 is construed to any one in the multiple parts having and illustrating in Example Operating Environment 1300 or combination
Relevant any dependence or requirement.
Seeing Figure 13, one for realizing, at this, the exemplary remote device of one or more embodiments can include in one
One general-purpose calculating appts of individual handheld computer 1310 form.Multiple parts of handheld computer 1310 can include
1320, system storage 1330 of one processing unit of (but not limited to) and a system bus 1321, this system bus
To include that the various couple system components of this system storage are to this processing unit 1320.
Computer 1310 generally includes multiple computer-readable medium and can be any can be visited by computer 1310
The obtainable medium asked.This system storage 1330 can be to include in volatibility and/or in terms of nonvolatile memory form
Calculation machine storage medium, such as read only memory (ROM) and/or random access storage device (RAM).By way of example, and unrestricted, deposit
Reservoir 1330 equally includes operating system, application program, other program modules and routine data.
Order and information can be inputted this computer 1310 by input equipment 1340 by user.Monitor or other types
Display device be also connected on system bus 1321 via an interface (such as output interface 1350).In addition to monitor 891, meter
Calculation machine can also include other peripheral output devices, and such as speaker and printer, these equipment can pass through output interface 1350
Connect.
Computer 1310 can use other remote computation one or more in a networking or distributed environment
The logic connection of machine (such as remote computer 1370) operates.This remote computer 1370 can be personal computer, server,
Router, network PC, peer device or other common network node or any other remote media consume or the equipment of transmission, and
And can include above in relation to computer 1310 describe multiple elements in any or all of.Logic depicted in figure 13
Connect and include a network 1371, such as LAN (LAN) or wide area network (WAN), it is also possible to include other network/bus.
This type of networked environment is that the minister of public works in ancient china is shown in family, office, the computer network of enterprise-wide, intranet and the Internet
It is used to.
As described above, various embodiment can be embodied in a mobile device.Figure 14 and Figure 15 illustrates one
The exemplary embodiment of individual mobile device.Referring now to Figure 14, the movement illustrating the movement that can participate in three axles sets
Standby 1400.As show in Figure 15, this mobile device 1500 can include multiple sensor, such as a gyroscope 1502,
(although not explanation, this mobile device is equally any can to sense for individual accelerometer 1504 and/or compass 1506
Inertia, pressure, distance, scope, sensor like this；This gyroscope 1502, accelerometer 1504 and compass 1506 are only
It is exemplary).These sensors can be communicatively coupled to a processing module 1508.Such as, as described above
, one or more sensors can be on an IC chip (such as sensor chip 102).This sensor chip can have
Relevant disposal ability and/or performance.Additionally, processing module 1508 can have extra disposal ability and/or performance (example
As, main process equipment 104).Sensor 1502-1506 as shown in figure 15 can experience on three axles that such as Figure 14 is shown
Motion.The data coming from sensor on these three axle can be fed in the method that Fig. 3 and Fig. 7 is shown, and base
In making from the data of these three axle, this motion/not moving determines.
Although describing various embodiment already in connection with various accompanying drawings, it should be appreciated that other can be used to be similar to
Embodiment or can to describe embodiment modify and add, for perform just as function and do not deviate with it.Cause
This, the innovation should not necessarily be limited to any single embodiment, but should be according to the width of appended claims and scope
Explain.
Claims (22)
1. a motion determination method, including:
Receiving a data signal from a sensor, wherein this data signal includes that multiple data value as user movement and is made an uproar
The function of sound；
Determining a meansigma methods of these data values, wherein the meansigma methods of these data values includes first moment；
Determine these data values square a meansigma methods, wherein these data values square meansigma methods include second moment；
Determine these data values cube a meansigma methods；
Determine only by using the third moment of the mean value calculation of this cube and only by using this first moment and this second moment meter
Difference between the third moment calculated；And
Determine whether a user movement event has occurred and that based on this difference.
2. the method for claim 1, wherein this determines whether a user movement event has occurred and that and farther includes
When this difference is in a threshold range, determines and also do not have user movement to occur.
3. method as claimed in claim 2, wherein receives this data signal and includes receiving these data letter from an accelerometer
Number.
4. method as claimed in claim 2, wherein receives this data signal and includes receiving this data signal from a compass.
5. method as claimed in claim 2, wherein receives this data signal and includes receiving this data signal from a gyroscope.
6. method as claimed in claim 5, farther includes when also not having user movement to occur, arranges a gyroscope inclined
Put.
7. method as claimed in claim 6, wherein this setting farther includes to be set to this offset of gyroscope these data
The meansigma methods of value.
8. method as claimed in claim 5, wherein this determines whether a user movement event has occurred and that and farther includes
When also not having user movement to occur, a gyroscope temperature-compensating learning period is set.
The most the method for claim 1, wherein this noise is Gaussian noise.
10. the method for claim 1, farther includes:
Determine a quadruplicate meansigma methods of these data values；
Determine second difference between these quadruplicate meansigma methodss and an intended Fourth-order moment, wherein this intended four
Rank square is that the mean value calculation from the meansigma methods of these squares He these data values obtains；And
Determine whether a user movement event has occurred and that based on this second difference.
11. the method for claim 1, farther include:
Determine a quadruplicate meansigma methods of these data values；
Determine a meansigma methods of five powers of these data values；
Determine the 3rd difference between five rank squares expected from the meansigma methods of these five powers and, wherein this intended five
Rank square is that the mean value calculation from the meansigma methods of these squares He these data values obtains；And
Determine whether a user movement event has occurred and that based on the 3rd difference.
12. 1 kinds of motion determination method, including:
A data signal is received, wherein when this sensor known is not when mobile, and this data signal includes from a sensor
Multiple data values are as motion and the function of noise；
Determining a meansigma methods of these data values, wherein the meansigma methods of these data values includes first moment；
Determine these data values square a meansigma methods, wherein these data values square meansigma methods include second moment；
Determine these data values cube a meansigma methods；
Determine only by using the third moment of the mean value calculation of this cube and only by using this first moment and this second moment meter
Difference between the third moment calculated；And
Determine that this sensor the most normally works based on this difference.
13. methods as claimed in claim 12, wherein determine this sensor the most normally work farther include as
Really this difference is in a threshold range, confirms that the most recorded one of this sensor does not has motion event.
14. methods as claimed in claim 12, wherein receive this data signal and farther include to receive from an accelerometer
This data signal.
15. methods as claimed in claim 12, wherein receive this data signal and farther include to receive this number from a compass
The number of it is believed that.
16. methods as claimed in claim 12, wherein receive this data signal and farther include to receive from a gyroscope to be somebody's turn to do
Data signal.
17. methods as claimed in claim 12, farther include:
Determine a quadruplicate meansigma methods of these data values；
Determine second difference between these quadruplicate meansigma methodss and an intended Fourth-order moment, wherein this intended four
Rank square is that the mean value calculation from the meansigma methods of these squares He these data values obtains；And
Determine that this sensor the most normally works based on this second difference.
18. 1 kinds of motion determination system, including:
One sensor chip, is configured for running in the data of this gyroscope including a gyroscope and one
A kind of first processor of motion detection algorithm；
One main process equipment being communicatively coupled to this sensor chip, including second processor, this second processor
It is configured in the data of this sensor chip, operating independently from this motion detection algorithm and being joined further
It is set to for running a kind of temperature compensation algorithm,
Wherein, this first processor is caught to receive the data signal from this gyroscope, and wherein this data signal includes
Multiple data values are as user movement and the function of noise；And
Wherein, this motion detection algorithm determines
The meansigma methods of these data values, wherein, this meansigma methods of these data values includes first moment；
These data values square a meansigma methods, wherein these data values square meansigma methods include second moment；
These data values cube a meansigma methods；
Only by using the third moment of the mean value calculation of this cube and only by using this first moment and this second moment to calculate
Difference between third moment；And
Determine whether a user movement event has occurred and that based on this difference；
Wherein, when this motion detection algorithm run on this first processor detect one there is no motion event time, this is the years old
Two processors are this gyroscope record one biasing used in this temperature compensation algorithm.
19. systems as claimed in claim 18, wherein this main process equipment is via an I2C bus or a serial port communications
It is coupled to this sensor chip to property.
20. systems as claimed in claim 18, wherein this main process equipment utilizes the data from this gyroscope to feed this fortune
Dynamic detection algorithm.
21. systems as claimed in claim 18, wherein this first processor be configured for determining a time period or
Move at the end of one variations in temperature and the most do not occur, and be configured for this gyroscope is utilized this biasing.
22. systems as claimed in claim 18, wherein this second processor is configured for this first processor simultaneously
Run this motion detection algorithm.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US13/164,136 | 2011-06-20 | ||
| US13/164,136 US8843345B2 (en) | 2011-06-20 | 2011-06-20 | Motion determination |
| PCT/US2012/035848 WO2012177323A1 (en) | 2011-06-20 | 2012-04-30 | Motion determination |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103717994A CN103717994A (en) | 2014-04-09 |
| CN103717994B true CN103717994B (en) | 2016-11-30 |
Family
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| CN1194691A (en) * | 1996-06-05 | 1998-09-30 | 卡西欧计算机株式会社 | Movement measuring device, electronic game machine including morement measuring device, and method of playing game machine |
| CN1384915A (en) * | 1999-09-15 | 2002-12-11 | 赛尔弗技术公司 | Navigation system and method for tracking position of object |
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| US5323337A (en) * | 1992-08-04 | 1994-06-21 | Loral Aerospace Corp. | Signal detector employing mean energy and variance of energy content comparison for noise detection |
| CN1194691A (en) * | 1996-06-05 | 1998-09-30 | 卡西欧计算机株式会社 | Movement measuring device, electronic game machine including morement measuring device, and method of playing game machine |
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