CN108960151A - A kind of mobile phone owner's personal identification method based on body motion information - Google Patents
A kind of mobile phone owner's personal identification method based on body motion information Download PDFInfo
- Publication number
- CN108960151A CN108960151A CN201810735530.9A CN201810735530A CN108960151A CN 108960151 A CN108960151 A CN 108960151A CN 201810735530 A CN201810735530 A CN 201810735530A CN 108960151 A CN108960151 A CN 108960151A
- Authority
- CN
- China
- Prior art keywords
- owner
- mobile phone
- motion
- scatterplot
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- General Physics & Mathematics (AREA)
- Psychiatry (AREA)
- Human Computer Interaction (AREA)
- Social Psychology (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Telephone Function (AREA)
Abstract
The present invention provides a kind of mobile phone owner's personal identification method based on body motion information, it is related to information of mobile terminal security fields, human body motion feature information is extracted and handled using 3-axis acceleration sensor in mobile phone, and establish the data base of owner itself, identification is realized by the comparison to mobile phone holder and reference data, to non-owner holder, mobile phone screen is locked immediately, and sends email alerts to mobile phone owner.Present invention can assure that owner's personal information and property safety, computer capacity is included in the direction that will have an effect, so that the precision of recognizing model of movement is higher, in addition, can be very good to avoid mobile phone towards different problems using sphere data distribution, calculated result is more reliable.
Description
Technical field
The present invention relates to information of mobile terminal security fields, especially a kind of personal identification method, by holding to mobile phone
The motion information acquisition of people and identification, whether confirmation mobile phone holder is owner, can be in the case where non-owner holds mobile phone
Ensure owner's personal information and property safety.
Background technique
Personal identification method based on body motion information is also referred to as Gait Recognition, belongs to a kind of living things feature recognition skill
Art, compared with the biometrics identification technologies such as fingerprint, iris, Gait Recognition have untouchable, non-infringements property, be difficult to it is hiding and
The advantages that camouflage, becomes the research hotspot of biometrics identification technology.In today that smart phone is popularized, people are in work and life
It increasingly be unable to do without mobile phone in work, e-payment and personal property management may be implemented by mobile phone.Due to being stored in mobile phone
A large amount of personal information brings the loss for being difficult to retrieve once mobile phone loss will will cause owner's leakage of personal information.
Condenser type chip speed sensor is configured in smart phone, this is a kind of 3-axis acceleration sensor, is passed through
The functions such as the positioning, navigation, measurement of mobile phone may be implemented in 3-axis acceleration sensor, and three axis, which refer to, tri- axial directions of X, Y, Z, X
Axis is parallel with mobile phone screen left and right directions (mobile phone narrow side), is positive to the right；Y-axis is parallel with screen up and down direction (mobile phone long side),
It is positive upwards；Z axis is parallel with screen face direction, and screen face direction is positive.3-axis acceleration sensor is referred to as acceleration
Sensor.
Human body all shows significant personal characteristics when doing regularity movement, such as leg periodic wobble, the torsion of waist
Dynamic, shaking of shoulder etc., these personal characteristics can be used as the main feature of identification body gait.The correlative study of early stage is all
Based on environmental sensor, wherein it is the most popular in such a way that image obtains gait feature, i.e., in the middle part of physical activity environment
Multiple cameras are affixed one's name to, gait information that is significant, playing a crucial role to behavior understanding is extracted from video and image data, into
And parse personal characteristic information wherein included.This body gait recognition methods has the feature letter of interaction nature, extraction
Breath is abundant and is easy to the advantages that disposing in subenvironment, but in practical applications there is also some limitations, as illumination condition,
Object's position, place size, temperature and humidity etc. will affect accuracy of identification and verification result, while also can not dispose
Application in the environment and particular surroundings (such as mobile phone) of camera.
Summary of the invention
For overcome the deficiencies in the prior art, the invention proposes a kind of mobile phone owner personal identification method, mobile phone is utilized
Middle 3-axis acceleration sensor is extracted and is handled to human body motion feature information, is realized and is carried out to the identity of mobile phone holder
Identification and certification lock mobile phone screen to non-owner holder immediately, and send email alerts to mobile phone owner, keep away
Exempt from leakage of personal information caused by losing because of mobile phone to owner and the loss in terms of property.Since the present invention is to utilize mobile phone
Sensor realize, do not need additionally to configure hardware, have the characteristics that cost of implementation is low, not affected by environment, discrimination is high.
The detailed step of the technical solution adopted by the present invention to solve the technical problems is as follows:
Step 1: establishing owner's motion model
The motion model of owner is established, owner's motion model establishment step is as follows:
Step 1.1: motion information acquisition
Using the motion information of mobile phone acceleration sensor acquisition owner, motion information acquisition is divided into two kinds of indoor and outdoors
Environment；
(1) outdoor sport information collection: owner walks, runs, jumps and cycles, and acquires exercise data, specifically adopts
Collect as follows:
Mobile phone is respectively placed in the purse on waistband, in trousers front pocket and in the pocket of front, mobile phone owner into
The following movement of row:, hurrying up 50 meters by 50 meters of walking, and reverse 50 meters of walking is run 100 meters, stride is walked 45 seconds, jump in place 45 seconds, is ridden
100 meters of vehicle；
(2) indoor sport information collection: owner carries out walking, stair activity, takes elevator and sit quietly, acquisition movement number
According to specific acquisition is as follows:
Mobile phone is respectively placed in the purse on waistband, in trousers front pocket and in the pocket of front, mobile phone owner into
4 kinds of activities of vertical ladder are gone upstairs, go downstairs, sit quietly and taken to row, and at ten more than the period, stair activity needs 20 grades for each activity
It is more than step；
The exercise data obtained during data are acquired is stored in mobile phone；
Step 1.2: owner's motion model is established
Owner's motion model is described using characteristic, characteristic include sample rate, peak value, wavelength, dynamics and
Direction enables to do same type games phase differentiation with other people, and specific modeling procedure is as follows:
(1) motion segmentation range is determined
Modulo operation is carried out to data acquired in mobile phone acceleration sensor, modulus formula is
), wherein x, y, z are respectively the accelerometer of current state in the acceleration value of X-axis, Y-axis and Z axis, and wherein M is vector field homoemorphism, X
Axis is the narrow side parallel direction of mobile phone screen, is positive to the right, and Y-axis is the broadside parallel direction of mobile phone screen, is positive upwards, Z axis
Normal to screen direction, vertical screen and is positive far from the direction of screen, thus obtains having an effect when representing user movement
The curve graph of size, curve graph horizontal axis are the serial number of sampled point, and the longitudinal axis is the acceleration value obtained, are obtained according to curvilinear characteristic
To peak value and period, peak-data is found out using sliding average algorithm denoising, then takes the peak value greater than 3 wave crests, and two
It is secondary to calculate average period, so that it is determined that the cyclic fluctuation range of movement；
(2) data normalization
The wavelength of the curve graph of exercise data each in step 1.1 is subjected to average calculating operation and completes wavelength normalization, to fortune
Acceleration information in dynamic data carries out interpolation or deletion, specially defines the abscissa of maximum and minimum in each period
For anchor point, so that it is determined that the subinterval in a cycle, determines according to the mean value for falling into sampled point quantity in subinterval to point
It is inserted into or is deleted, i.e., the number of sampled point is all the same in each period after insertion sampled point or deletion sampled point, and inserts
Access point or the position for deleting point are all located on the left of curve graph wave crest or on the left of trough；
(3) have an effect direction and variation are determined
Data point in one period of motion is standardized, standardization formula isWhereinCoordinate after respectively standardizing, is distributed in all data points all on the unit sphere curved surface of radius 1, and
Successively carry out being matched colors by red to purple gradual change chromatic colorant with RGB according to the sampling time, data distribution by (220,60,60),
(220,220,60), (60,220,60), (60,220,220), (60,60,220), (220,60,220) are evenly distributed, if being formed
All scatterplots on spherical surface are then taken scatterplot average value by dry scatterplot, and scatterplot average value is in each period according to the sampling time
Scatterplot is successively averaged by sequence with the scatterplot of identical sample time orders all in other periods, and each sampling can be obtained
Form line between the scatterplot average value of time sequencing, the centre of sphere of unit sphere and scatterplot average value on spherical surface, and with
Scatterplot circle is formed for 5 ° of rotations with centre of sphere degree on spherical surface centered on this line, the vector direction of scatterplot average value is current scattered
The direction of having an effect of point, i.e. have an effect direction of the owner within the period of motion；
(4) owner's motion state parameters value is determined
According to owner's motion model determine owner respectively in stroll, walk, hurry up, run, jump, cycle, take a lift, stop
The parameter value for ceasing motion state, forms the motor pattern of owner, the basic foundation as identification owner；
Step 2: when antecessor's identification
Step 2.1: motion information acquisition
Work as the motion information of antecessor using mobile phone acceleration sensor periodically acquisition；
Step 2.2: holder's motion model is established
From acquisition when extracting its characteristic information in antecessor's motion information, establishes and work as antecessor's motion model, really
Determine the motion state parameters of holder, step is identical as owner's motion model establishment step in step 1；
Step 2.3: holder's identification
Holder's motion state parameters value and owner's motion state parameters value are compared, first determine whether mobile phone holder
Current motion state calculates each scatterplot and corresponding sampling then again compared with the owner's motion model pre-saved
Distance between the scatterplot average value of time sequencing, if the data value of motion state parameters value or corresponding sports mode is transported at 5
The scatterplot number fallen within the scatterplot circle of corresponding sample time order in the dynamic period is greater than the 90% of total scatterplot number, then it is assumed that
When antecessor be owner, be otherwise non-owner, and enter step 2.4；
Step 2.4: mobile phone safe protection
If locking mobile phone screen immediately, while sending electricity to mobile phone owner when antecessor is identified as non-owner
Sub- mail is alarmed, comprising utilizing the geographical location for working as antecessor determined by mobile phone positioning system in warning message.
The beneficial effects of the invention are as follows developing corresponding cell phone application software based on this method and operate on smart phone,
In the case where non-owner holds mobile phone, it can be ensured that owner's personal information and property safety.It is existing wearable to use sensor
Gait Recognition can only be carried out according to size of having an effect by carrying out Gait Recognition, due to having an effect when calculating the modulo operation for size of having an effect
Bearing data is abandoned, so being easy to cause precision lower.The present invention is included in computer capacity at the direction that will have an effect, so that motor pattern
The precision of identification is higher, in addition, can be very good to avoid mobile phone towards different problems, calculated result using sphere data distribution
It is more reliable.
Detailed description of the invention
Fig. 1 is the monocyclic-start data waveform figure of certain user walking.
Fig. 2 is that sliding average algorithm moves forward and backward different wave shape figure
Fig. 3 is more all exercise data overlaid waveforms figures of single user walking.
Fig. 4 is spherome surface distribution map of the data point on unit radius in the monocycle.
Fig. 5 is different user walking mode waveform diagram.
Fig. 6 is same user's different motion mode waveform.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples.
Step 1: establishing owner's motion model
Establish the motion model of owner, so as to later compared with the motion model of holder, to the identity of holder into
Row identification and certification, determine whether the holder is owner；Owner's motion model establishment step is as follows:
Step 1.1: motion information acquisition
Using the motion information of mobile phone acceleration sensor acquisition owner, motion information acquisition is divided into two kinds of indoor and outdoors
Environment；
(1) outdoor sport information collection: owner walks, runs, jumps and cycles, and acquires exercise data, specifically adopts
Collect as follows:
Mobile phone is respectively placed in the purse on waistband, in trousers front pocket and in the pocket of front, mobile phone owner into
The following movement of row:, hurrying up 50 meters by 50 meters of walking, and reverse 50 meters of walking is run 100 meters, stride is walked 45 seconds, jump in place 45 seconds, is ridden
100 meters of vehicle；
(2) indoor sport information collection: owner carries out walking, stair activity, takes elevator and sit quietly, acquisition movement number
According to specific acquisition is as follows:
Mobile phone is respectively placed in the purse on waistband, in trousers front pocket and in the pocket of front, mobile phone owner into
4 kinds of activities of vertical ladder are gone upstairs, go downstairs, sit quietly and taken to row, and at ten more than the period, stair activity needs 20 grades for each activity
It is more than step；
The exercise data obtained during data are acquired is stored in mobile phone；
Step 1.2: owner's motion model is established
Owner's motion model is described using characteristic, characteristic include sample rate, peak value, wavelength, dynamics and
Direction enables to do same type games phase differentiation with other people, and specific modeling procedure is as follows:
(1) motion segmentation range is determined
Modulo operation is carried out to data acquired in mobile phone acceleration sensor, modulus formula is
), wherein x, y, z are respectively the accelerometer of current state in the acceleration value of X-axis, Y-axis and Z axis, and wherein M is vector field homoemorphism, X
Axis is the narrow side parallel direction of mobile phone screen, is positive to the right, and Y-axis is the broadside parallel direction of mobile phone screen, is positive upwards, Z axis
Normal to screen direction, vertical screen and is positive far from the direction of screen, thus obtains having an effect when representing user movement
The curve graph of size, specific form as shown in Figure 1, Fig. 1 shows be waveforms of the motion data, according to chronological order arrangement
Sampling point value, horizontal axis are the serial number of the sampled point of interception, and the longitudinal axis is the acceleration value obtained, due to the hand of different model
The accelerometer that machine uses is different, only considers the opposite variation of the waveform of numerical value, does not require to data unit.Fig. 1 is to use
Family monocycle walking data waveform figure, RGB are xyz 3-axis acceleration data, and yellow is the data after modulus, according to curve
Feature obtains the peak value of its dynamics and the period of having an effect, and finds out peak-data using sliding average algorithm denoising, then takes greater than 3
The peak value of a wave crest, and secondary calculating goes out average period, so that it is determined that the cyclic fluctuation range of movement；
(2) data normalization
The wavelength of the curve graph of exercise data each in step 1.1 is subjected to average calculating operation and completes wavelength normalization, to fortune
Acceleration information in dynamic data carries out interpolation or deletion, specially defines the abscissa of maximum and minimum in each period
For anchor point, so that it is determined that the subinterval in a cycle, determines according to the mean value for falling into sampled point quantity in subinterval to point
It is inserted into or is deleted, i.e., the number of sampled point is all the same in each period after insertion sampled point or deletion sampled point, and inserts
Access point or the position for deleting point are all located on the left of curve graph wave crest or on the left of trough, to ensure the wavelength phase of each section of exercise data
Together；
(3) have an effect direction and variation are determined
Data point in one period of motion is standardized, standardization formula isWhereinCoordinate after respectively standardizing, is distributed in all data points all on the unit sphere curved surface of radius 1, and
Successively carry out being matched colors by red to purple gradual change chromatic colorant with RGB according to the sampling time, data distribution by (220,60,60),
(220,220,60), (60,220,60), (60,220,220), (60,60,220), (220,60,220) are evenly distributed, if being formed
All scatterplots on spherical surface are then taken scatterplot average value by dry scatterplot, and scatterplot average value is in each period according to the sampling time
Scatterplot is successively averaged by sequence with the scatterplot of identical sample time orders all in other periods, and each sampling can be obtained
Form line between the scatterplot average value of time sequencing, the centre of sphere of unit sphere and scatterplot average value on spherical surface, and with
Scatterplot circle is formed for 5 ° of rotations with centre of sphere degree on spherical surface centered on this line, the vector direction of scatterplot average value is current scattered
The direction of having an effect of point, i.e. have an effect direction of the owner within the period of motion；
(4) owner's motion state parameters value is determined
According to owner's motion model determine owner respectively in stroll, walk, hurry up, run, jump, cycle, take a lift, stop
The parameter value for ceasing motion state, forms the motor pattern of owner, the basic foundation as identification owner；
Step 2: when antecessor's identification
Step 2.1: motion information acquisition
Work as the motion information of antecessor using mobile phone acceleration sensor periodically acquisition；
Step 2.2: holder's motion model is established
From acquisition when extracting its characteristic information in antecessor's motion information, establishes and work as antecessor's motion model, really
Determine the motion state parameters of holder, step is identical as owner's motion model establishment step in step 1；
Step 2.3: holder's identification
Holder's motion state parameters value and owner's motion state parameters value are compared, first determine whether mobile phone holder
Current motion state calculates each scatterplot and corresponding sampling then again compared with the owner's motion model pre-saved
Distance between the scatterplot average value of time sequencing, if the data value of motion state parameters value or corresponding sports mode is transported at 5
The scatterplot number fallen within the scatterplot circle of corresponding sample time order in the dynamic period is greater than the 90% of total scatterplot number, then it is assumed that
When antecessor be owner, be otherwise non-owner, and enter step 2.4；
Step 2.4: mobile phone safe protection
If locking mobile phone screen immediately when antecessor is identified as non-owner, safeguard protection is carried out to mobile phone, together
When to mobile phone owner send email alerts, contain in warning message using currently holding determined by mobile phone positioning system
The geographical location of people.
Basic conception according to the present invention is as follows:
1. mobile phone owner and holder
Mobile phone owner refers to mobile phone owner, under normal circumstances and mobile phone holder.Mobile phone holder refers to currently
Hold the people of mobile phone, it may be possible to owner, it is also possible to be non-owner.Holding mobile phone for non-owner, (such as hand-set from stolen loses feelings
Condition), it is likely that it will cause owner's leakage of personal information and property loss.
2. mobile phone holder's motion information
One mobile phone holder motion information is made of following several parts:
(1) 3-axis acceleration data.The real-time motion data read from mobile phone acceleration sensor, wherein with Mobile phone screen
Curtain left and right directions is X-axis, is positive to the right；Up and down direction is Y-axis, is positive upwards；Normal to screen direction is Z axis, vertical screen
Outwardly direction is forward direction.
(2) sample rate.Mobile phone acceleration sensor acquires the frequency of data, usually 60 time/second.
(3) owner's physical characteristic information.The physical traits such as the owner's age, gender, height, the weight that are saved in mobile phone letter
Breath.
(4) holder's motion state.Owner's stair activity for being extracted from real-time motion data, run and walk, walking,
The motion informations such as cycle, hurry up, take a lift, loosen and make a phone call, for describing the motion state of holder.
(5) mobile phone position information.The location of mobile phone when mobile phone holder moves, mobile phone can be placed on waist
In packet, trouser pocket and coat pocket.
The present invention is implemented by exploitation cell phone application, can also be implemented by the way that plug-in unit is arranged in having application.Mobile phone
APP provides a user interface and carries out system administration, and major function is as follows:
1. owner registers.Owner registers a username and password, and related personal information, including age, gender, body is arranged
High, weight and mailbox.
2. owner's motion information acquisition.After APP starting, mobile phone owner, which normally holds mobile phone, can acquire most of movement shape
State information is stored in preset corresponding motion model.For some motor patterns seldom occurred, owner can be according to user
Specified movement is done in the prompt at interface, the movement including two kinds of environment of indoor and outdoors, when collecting enough motion informations
Afterwards, system prompt owner motion model, which is established, completes.
3. working as antecessor's motion pick and modeling.After system enters normal operating condition, antecessor is worked as in acquisition in real time
Motion information and modeling, if it find that the matching degree when antecessor's motor pattern and owner's motor pattern is lower than 90%, then
Into mobile phone guard mode.
4. mobile phone protection and alarm lock mobile phone screen under mobile phone guard mode first, then extracts and work as antecessor
Location information, at regular intervals to owner preset mailbox send alarm mail.
Claims (1)
1. a kind of mobile phone owner's personal identification method based on body motion information, it is characterised in that include the following steps:
Step 1: establishing owner's motion model
The motion model of owner is established, owner's motion model establishment step is as follows:
Step 1.1: motion information acquisition
Using the motion information of mobile phone acceleration sensor acquisition owner, motion information acquisition is divided into two kinds of rings of indoor and outdoors
Border；
(1) outdoor sport information collection: owner walks, runs, jumps and cycles, and acquires exercise data, specific acquisition is such as
Under:
Mobile phone is respectively placed in the purse on waistband, and in trousers front pocket and in the pocket of front, mobile phone owner is carried out such as
Lower movement:, hurrying up 50 meters by 50 meters of walking, and reverse 50 meters of walking is run 100 meters, and stride is walked 45 seconds, jump in place 45 seconds, cycles 100
Rice；
(2) indoor sport information collection: owner carries out walking, stair activity, takes elevator and sit quietly, and acquires exercise data, tool
Body acquisition is as follows:
Mobile phone is respectively placed in the purse on waistband, in trousers front pocket and in the pocket of front, and mobile phone owner carries out
4 kinds of activities of vertical ladder are gone downstairs, sit quietly and taken to stair, and at ten more than the period, stair activity needs 20 grades of steps for each activity
More than；
The exercise data obtained during data are acquired is stored in mobile phone；
Step 1.2: owner's motion model is established
Owner's motion model is described using characteristic, and characteristic includes sample rate, peak value, wavelength, dynamics and direction,
Enable to do same type games phase differentiation with other people, specific modeling procedure is as follows:
(1) motion segmentation range is determined
Modulo operation is carried out to data acquired in mobile phone acceleration sensor, modulus formula is
Wherein x, y, z are respectively the accelerometer of current state in the acceleration value of X-axis, Y-axis and Z axis, and wherein M is vector field homoemorphism, X-axis
It for the narrow side parallel direction of mobile phone screen, is positive to the right, Y-axis is the broadside parallel direction of mobile phone screen, is positive upwards, and Z axis hangs down
Directly in screen direction, the vertical screen and direction far from screen is positive, thus obtain one when representing user movement have an effect it is big
Small curve graph, curve graph horizontal axis are the serial number of sampled point, and the longitudinal axis is the acceleration value obtained, are obtained according to curvilinear characteristic
Peak value and period find out peak-data using sliding average algorithm denoising, then take the peak value greater than 3 wave crests, and secondary
Average period is calculated, so that it is determined that the cyclic fluctuation range of movement；
(2) data normalization
The wavelength of the curve graph of exercise data each in step 1.1 is subjected to average calculating operation and completes wavelength normalization, to movement number
Acceleration information in carries out interpolation or deletion, and specially defining the abscissa of maximum and minimum in each period is anchor
Point, so that it is determined that the subinterval in a cycle, determines according to the mean value for falling into sampled point quantity in subinterval to a progress
Insertion is deleted, i.e., the number of sampled point is all the same in each period after insertion sampled point or deletion sampled point, and insertion point
Or the position of deletion point is all located on the left of curve graph wave crest or on the left of trough；
(3) have an effect direction and variation are determined
Data point in one period of motion is standardized, standardization formula isWhereinCoordinate after respectively standardizing, is distributed in all data points all on the unit sphere curved surface of radius 1, and
Successively carry out being matched colors by red to purple gradual change chromatic colorant with RGB according to the sampling time, data distribution by (220,60,60),
(220,220,60), (60,220,60), (60,220,220), (60,60,220), (220,60,220) are evenly distributed, if being formed
All scatterplots on spherical surface are then taken scatterplot average value by dry scatterplot, and scatterplot average value is in each period according to the sampling time
Scatterplot is successively averaged by sequence with the scatterplot of identical sample time orders all in other periods, and each sampling can be obtained
Form line between the scatterplot average value of time sequencing, the centre of sphere of unit sphere and scatterplot average value on spherical surface, and with
Scatterplot circle is formed for 5 ° of rotations with centre of sphere degree on spherical surface centered on this line, the vector direction of scatterplot average value is current scattered
The direction of having an effect of point, i.e. have an effect direction of the owner within the period of motion；
(4) owner's motion state parameters value is determined
According to owner's motion model determine owner respectively in stroll, walk, hurry up, run, jump, cycle, take a lift, fortune of resting
The parameter value of dynamic state, forms the motor pattern of owner, the basic foundation as identification owner；
Step 2: when antecessor's identification
Step 2.1: motion information acquisition
Work as the motion information of antecessor using mobile phone acceleration sensor periodically acquisition；
Step 2.2: holder's motion model is established
It from acquisition when extracting its characteristic information in antecessor's motion information, establishes and works as antecessor's motion model, determination is held
The motion state parameters of someone, step are identical as owner's motion model establishment step in step 1；
Step 2.3: holder's identification
Holder's motion state parameters value and owner's motion state parameters value are compared, first determine whether that mobile phone holder is current
Motion state calculate each scatterplot and corresponding sampling time then again compared with the owner's motion model pre-saved
Distance between the scatterplot average value of sequence, if the data value of motion state parameters value or corresponding sports mode is 5 movement weeks
The scatterplot number fallen within the scatterplot circle of corresponding sample time order in phase is greater than the 90% of total scatterplot number, then it is assumed that current
Hold artificial owner, is otherwise non-owner, and enter step 2.4；
Step 2.4: mobile phone safe protection
If locking mobile phone screen immediately, while sending electronics postal to mobile phone owner when antecessor is identified as non-owner
Part is alarmed, comprising utilizing the geographical location for working as antecessor determined by mobile phone positioning system in warning message.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810735530.9A CN108960151A (en) | 2018-07-06 | 2018-07-06 | A kind of mobile phone owner's personal identification method based on body motion information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810735530.9A CN108960151A (en) | 2018-07-06 | 2018-07-06 | A kind of mobile phone owner's personal identification method based on body motion information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108960151A true CN108960151A (en) | 2018-12-07 |
Family
ID=64484253
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810735530.9A Pending CN108960151A (en) | 2018-07-06 | 2018-07-06 | A kind of mobile phone owner's personal identification method based on body motion information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108960151A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110674801A (en) * | 2019-12-04 | 2020-01-10 | 展讯通信（上海）有限公司 | Method and device for identifying user motion mode based on accelerometer and electronic equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103925929A (en) * | 2013-01-14 | 2014-07-16 | 北京中交兴路信息科技有限公司 | Method and device for statistics of movement distance of moving target |
| CN105354465A (en) * | 2015-10-27 | 2016-02-24 | 上海斐讯数据通信技术有限公司 | Application security realization method, system and mobile terminal |
-
2018
- 2018-07-06 CN CN201810735530.9A patent/CN108960151A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103925929A (en) * | 2013-01-14 | 2014-07-16 | 北京中交兴路信息科技有限公司 | Method and device for statistics of movement distance of moving target |
| CN105354465A (en) * | 2015-10-27 | 2016-02-24 | 上海斐讯数据通信技术有限公司 | Application security realization method, system and mobile terminal |
Non-Patent Citations (1)
| Title |
|---|
| 李勇谋: "基于智能手机的活动识别和身份识别技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110674801A (en) * | 2019-12-04 | 2020-01-10 | 展讯通信（上海）有限公司 | Method and device for identifying user motion mode based on accelerometer and electronic equipment |
| CN110674801B (en) * | 2019-12-04 | 2020-03-20 | 展讯通信（上海）有限公司 | Method and device for identifying user motion mode based on accelerometer and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Nickel et al. | Authentication of smartphone users based on the way they walk using k-NN algorithm | |
| Coskun et al. | Phone position/placement detection using accelerometer: Impact on activity recognition | |
| Sun et al. | Accelerometer-based speed-adaptive gait authentication method for wearable IoT devices | |
| Miao et al. | Identifying typical physical activity on smartphone with varying positions and orientations | |
| Reddy et al. | Using mobile phones to determine transportation modes | |
| CN108109336B (en) | Human body falling identification method based on acceleration sensor | |
| Gu et al. | Wifi-assisted human activity recognition | |
| Heng et al. | Human activity recognition based on transformed accelerometer data from a mobile phone | |
| Tang et al. | An empirical evaluation of activities and classifiers for user identification on smartphones | |
| KR102081245B1 (en) | Determining information on portable electronic devices that users carry | |
| CN107358248B (en) | Method for improving falling detection system precision | |
| Peng et al. | Indoor floor plan construction through sensing data collected from smartphones | |
| CN107270889A (en) | A kind of indoor orientation method and alignment system based on earth magnetism collection of illustrative plates | |
| Guo et al. | OpenSesame: Unlocking smart phone through handshaking biometrics | |
| CN107432739A (en) | A kind of smart garment system for health monitoring | |
| Du et al. | Group mobility classification and structure recognition using mobile devices | |
| CN108960151A (en) | A kind of mobile phone owner's personal identification method based on body motion information | |
| CN107169334B (en) | The user authen method based on straight punch motion detection for hand wearable device | |
| Muaaz et al. | Accelerometer based gait recognition using adapted gaussian mixture models | |
| Li et al. | Collaborative recognition of queuing behavior on mobile phones | |
| Samiei-Zonouz et al. | Smartphone-centric human posture monitoring system | |
| CN107991718A (en) | A kind of mobile phone wearing mode automatic testing method based on multi-modal data analysis | |
| Hsu et al. | Two-phase activity recognition with smartphone sensors | |
| CN110929766B (en) | Self-adaptive pedestrian mobile phone attitude identification method based on Gaussian mixture clustering algorithm | |
| Ye et al. | Research of fall detection and alarm applications for the elderly |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20181207 |
|
| WD01 | Invention patent application deemed withdrawn after publication |