CN108338791A - The detection device and detection method of unstable motion data - Google Patents
The detection device and detection method of unstable motion data Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A—HUMAN NECESSITIES
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Abstract
This application discloses a kind of unstable motion data detection device and detection method, which includes：Unstable motion analog platform and human body movement data detection device；Unstable motion analog platform makes human body be in instability status for guiding human motion；Human body movement data detection device, for measuring kinematic parameter and gait parameter of the record human body on unstable motion analog platform.The present invention also provides a kind of unstable motion data detection methods, include the following steps：Unstability is generated using unstable motion analog platform guiding human body；The kinematic parameter and gait parameter of record human body are measured using human body movement data detection device.The motion state of unstable motion data detection device detection participant using the present invention.Using unstable motion analog platform, different unstable motion states are generated, the kinematic parameter of detection and record participant, the fall risk for follow-up accurate evaluation human body provide parameter foundation in real time.
Description
Technical field
This application involves unstable motions, and in particular to the detection device and detection method of unstable motion data.
Background technology
Since human body disequilibrium is likely to result in falling, tumble may be that self reason is also likely to be that outside cause is led
It causes.When human body disequilibrium, under the effect of human body instinct, a self-control process, the knot after self-control can be carried out
Unquestionably there are two types of possibilities for fruit：First, restoring balance, second is that falling over.Tumble is also one in human motion state
Kind, tumble is generally defined as：Human body falls due to unexpected, involuntary attitudes vibration on ground or other are lower
In plane.When human body experience is fallen, unstability can be passed through successively, hit ground, steady three phases.Therefore, human body fall risk
Assessment is sought to human body when unstability situation occur, a kind of assessment of human body regulating power.
Traditional tumble is assessed generally use questionnaire, physical examination, is visually observed with modes such as locomitivity tests to people
The possibility that body is fallen is assessed, and the tool of use includes：Morse tumbles scale (Morse Fall Scale, MFS), support
Maas fall risk assessment tool (St. Thomas's Risk Assessment Tool, STRATIFY), timing are stood up-are walked
Test (Timed Up and Go, TUG), Berg balance scales (Berg Balance Scale, BBS), Tinetti balance and
Gait scale etc..
Traditional methods of risk assessment is primarily present following deficiency in clinical application.1. commenting Falls in Old People risk
The accuracy estimated is insufficient：Existing analysis is found, is either directed to community-dwelling elder or inpatient, existing fall risk
Tumble person can not be effectively predicted in assessment tool.2. these assessment tools usually require multi-disciplinary professional, specifically
It checks that place, equipment, assessment result are somewhat dependent upon the experience of assessment doctor, thus significantly limits it and answer
Use range.3. the assessment to locomitivity is completed in specific inspection chamber, on the one hand there is experimenter effect
(Hawthorne effect), on the other hand, the everyday environments of experimental situation and the elderly greatly differ from each other, and influence to fall
The accuracy of prediction.4. using the methods of questionnaire and functional test since there are the subjective judgements of tester and subject, thus
As a result usually lack objectivity and accuracy.
Invention content
The brief overview about the present invention is given below, in order to provide about the basic of certain aspects of the invention
Understand.It should be appreciated that this general introduction is not the exhaustive general introduction about the present invention.It is not intended to determine the present invention's
Crucial or pith, nor is it intended to limit the scope of the present invention.Its purpose only provides certain general in simplified form
It reads, in this, as the preamble in greater detail discussed later.
The purpose of the embodiment of the present invention is that in view of the above-mentioned defects in the prior art, providing one kind can allow human body to be in not
With unstable motion state, the kinematic parameter of detection and record participant, the fall risk for accurate evaluator body provide ginseng in real time
The detection device and detection method of the unstable motion data of number foundation.
To achieve the goals above, the technical solution adopted by the present invention is that：
A kind of unstable motion data detection device, including：Unstable motion analog platform and human body movement data detection dress
It sets；
Unstable motion analog platform makes human body be in instability status for guiding human motion；
Human body movement data detection device, for measuring kinematic parameter of the record human body on unstable motion analog platform
And gait parameter.
The present invention also provides a kind of unstable motion data detection methods, include the following steps：
Unstability is generated using unstable motion analog platform guiding human body；
The kinematic parameter and gait parameter of record human body are measured using human body movement data detection device.
Compared with prior art, the beneficial effects of the invention are as follows：
The motion state of unstable motion data detection device detection participant using the present invention.Utilize unstable motion mould
Quasi- platform, generates different unstable motion states, in real time the kinematic parameter of detection and record participant, is follow-up accurate evaluation people
The fall risk of body provides parameter foundation.
Description of the drawings
Below with reference to the accompanying drawings illustrate embodiments of the invention, can be more readily understood that the present invention it is above and
Other objects, features and advantages.Component in attached drawing is intended merely to show the principle of the present invention.In the accompanying drawings, identical or class
As technical characteristic or component will be indicated using same or similar reference numeral.
Fig. 1 is nine number of axle provided in an embodiment of the present invention according to fusion algorithm structure chart；
Fig. 2 is the coordinate system relational graph of the motion calculation process of unstable motion analog platform provided in an embodiment of the present invention.
Specific implementation mode
Illustrate the embodiment of the present invention with reference to the accompanying drawings.In the attached drawing of the present invention or a kind of embodiment
The elements and features of description can mutually be tied with elements and features shown in one or more other attached drawings or embodiment
It closes.It should be noted that for purposes of clarity, unrelated to the invention, ordinary skill people is omitted in attached drawing and explanation
The expression and description of component and processing known to member.
A kind of unstable motion data detection device, including：Unstable motion analog platform and human body movement data detection dress
It sets；
Unstable motion analog platform makes human body be in instability status for guiding human motion；
Human body movement data detection device, for measuring kinematic parameter of the record human body on unstable motion analog platform
And gait parameter.
Using the special exercise of unstable motion analog platform of the present invention, human body is allowed to be in different unstable motion states, in real time
The kinematic parameter of detection and record participant, the fall risk for accurate evaluator body provide parameter foundation.
Preferably, the unstable motion analog platform includes multifreedom motion guide device；
Multifreedom motion guide device, for providing multiple freedom of motion, guiding human body moves certainly the multiple
By being moved on degree.
Preferably, the multifreedom motion guide device is multi-freedom robot.
Preferably, the multi-freedom robot is the robot of 6 degree of freedom.
The unstable motion analog platform of the present invention has 6DOF, can preferably simulate human body unstable motion, allows human body
Generate unstability body-sensing.
On the basis of the above embodiments, platform, institute are restored in the human motion position for further including single-degree-of-freedom to the present embodiment
State human motion position restore stage+module the multifreedom motion guide device output end.
The present invention is tested human body in human motion position during unstable motion analog platform simulates human body instability status
It sets and restores to do different walking movements according to requiring on platform, will produce the position displacement of opposite unstable motion analog platform.
Human motion position restores platform and generates a movement, and tested human body is made to be restored to the position before walking.Pass through human motion
The movement that platform is restored in position will drive human body passively to return to the original position of unstable motion analog platform.
Using the special exercise of unstable motion analog platform of the present invention, human body is allowed to be in different unstable motion states, in real time
The kinematic parameter of detection and record participant, the fall risk for accurate evaluator body provide parameter foundation.
On the basis of the above embodiments, the human body movement data detection device includes being fixed on human body to the present embodiment
At least one inertial sensor, by detect inertial sensor fix movements of parts of the body acceleration parameter, to obtain
The kinematic parameter and gait parameter of human body.
Specifically, the kinematic parameter includes human motion speed and human motion acceleration；
The gait parameter includes the kinetic parameter of gait and the kinematics parameters of gait.
The gait kinematics parameter includes：
(1) gait cycle；
(2) step-length；
(3) stride length；
(4) cadence；
(5) leg speed；
(6) joint angle.
Preferably, the inertial sensor is multiple, and is fixed on human body by binding strap.
Preferably, the inertial sensor is nine axis inertial sensors, by three axis accelerometer, three-axis gyroscope and three
Axis magnetometer three parts form.
Preferably, it is the platform with one or more degree of freedom that platform is restored in the human motion position.
The present invention also provides a kind of unstable motion data detection methods, include the following steps：
Unstability is generated using unstable motion analog platform guiding human body；
The kinematic parameter and gait parameter of record human body are measured using human body movement data detection device.
The present invention simulates daily unstable motion state and records human body sport parameter and gait parameter, using Instability Simulation
Platform generates a kind of unstability body-sensing by human body, when human body, which is based on instinct, generates balance play, is recorded using inertial sensor
The motion state at each position of human body and the posture of participant, the fall risk for follow-up accurate evaluation human body provide parameter
Foundation.
The present invention provides preferred embodiment：A kind of unstable motion data detection method, includes the following steps：
(1), preparation：
Human body keeps balance to restore on platform in human motion position, and human body movement data detection device is fixed on people
On body, specifically, the measuring unit of human body movement data detection device is strapped on four limbs and waist, human body does everyday actions,
For example both hands demarcate the coordinate system of measuring unit to front raise, the actions such as squat down, stand up；
Demarcate the specific method of the coordinate system of measuring unit：Human body does the everyday actions of standard, in some particular pose,
The parameter for recording each measuring unit of human body movement data detection device under these particular poses, as follow-up measurement data
Benchmark, to complete calibration measuring unit coordinate system operation.
(2) detection of data：
The location information of detection of platform human body is restored in human motion position, and starts the telecontrol equipment under human foot, drives
Human body returns to initial position；Meanwhile unstable motion analog platform is according to the parameter of daily exercise instability status, driving human body fortune
Dynamic position restores platform and special exercise occurs, and makes the posture feature of human body consistent with daily unstable motion feature；Human body can instinct
Adjustment posture, correct instability status；The measuring unit of human body movement data detection device measures and records all of human body
Kinematic parameter；
(3) processing of data
The acceleration information at the human body position that human body movement data detection device is tested, is converted to human body
Speed, position kinematic parameter and the gait parameter at position.
Preferably, the measuring unit of human body movement data detection device is added using nine axis inertial sensors by three axis
Speedometer, three-axis gyroscope and three axle magnetometer three parts composition；Wherein, the three axis accelerometer is used for measuring sensor
The acceleration of three axial directions, the three-axis gyroscope are used for measuring the angular speed of three axial directions of sensor, the three axle magnetometer
For providing the magnetic-field component of three axial directions of sensor.
Specifically, further including binding inertial sensor to human body position, the inertial sensor angle calculation uses
Expanded Kalman filtration algorithm.
Specifically algorithm includes：
(1) angle, which resolves, includes：
Pass through the appearance of accelerometer and magnetometer data merged or object can be calculated by single gyroscope
State angle.But both of which has shortcoming：The dynamic response characteristic of accelerometer is poor, and there are system driftings for gyroscope
And random drift, long time integration can cause large error.Therefore, be to obtain precision higher human body attitude angle, need pair plus
Nine number of axle of speedometer, gyroscope and magnetometer are according to being merged.
Fusion process is as shown in Figure 1, three axle speed meters 1, gyroscope 2 and magnetometer 3 carry out data filtering 4, then count respectively
Attitude angle is calculated, following steps are specifically included：
S101：Attitude angle is calculated according to gyroscope：
The spin matrix description form of quaternary number is as follows:
Wherein,Indicate that carrier coordinate system is converted to the transition matrix of world coordinate system, q=[q0, q1, q2, q3] it is one
Group quaternary number.It calculates to obtain formula by the angular acceleration of gyroscope：
q0,q1,q2,q3Indicate the sensor quaternary number of last moment, q0′,q1′,q2′,q3' indicate to pass by △ t moments
The quaternary numerical value of sensor.
Wherein,It is the output of three-axis gyroscope.It is as follows finally to calculate attitude angle:
Wherein, θ represents pitch angle, and φ represents roll angle, and ψ represents course angle, arctan be in antitrigonometric function anyway
It cuts.
S102：Attitude angle is calculated according to accelerometer and magnetometer：
Pitch angle and roll angle can be calculated by bringing the output of 3-axis acceleration into following formula:
Wherein, [ax, ay, az] indicating the output of three axis accelerometer, θ represents pitch angle, and φ represents roll angle.
Accurate course angle in order to obtain needs to carry out slope compensation to magnetometer, and the output of magnetometer is substituted into
Formula can obtain:
Wherein,For the output of magnetometer before compensation,Result after being compensated for magnetometer.θ
Pitch angle and roll angle with φ to be calculated by accelerometer data.
Finally, the course angle after magnetometer compensation can be obtained by following formula.
S103:Kalman's data fusion is carried out by Kalman filter：
Kalman filter is to be applied to the recursion filter of linear system prediction, and principle is to utilize existing upper one
The parameter of a state estimates next state precision is high, is widely used in some real-time systems.
The equation of Kalman filter includes time update equation and measurement updaue equation.The time of discrete kalman filter updates
Equation is shown below.
Formula illustrates the state estimation walked from -1 step of kth to kth and covariance estimation, wherein Xk|k-1, it is in kth -1
When step, according to Xk-1|k-1, the prior state estimated, ukIt is the input of departure process, AkIndicate state-transition matrix,
B is control input matrix.Pk|k-1It is by Xk-1|k-1Obtained error co-variance matrix, QkIt is the association side of discrete time control process
Poor matrix.
The measurement updaue equation of discrete kalman filter is shown as the following formula.
First, kalman gain K is calculated by above formulak, wherein HkIt is calculation matrix, RkIt is the covariance in measurement process
Matrix, the process gain Z for then obtaining measurementkIt brings formula into and obtains a posteriority state estimation Xk|k, finally obtain posteriority
Error covariance estimated matrix Pk|k.By time update and measurement updaue equation, Kalman filtering constantly uses newest
Estimated value predicts the estimated value of next state.
S104:Obtain attitude angle：
After the attitude angle that two ways obtains is merged by Kalman filter, more accurate appearance has been obtained
State angle information ψt, θt, φt.Recycle following formula that Eulerian angles are converted to quaternary number.
(2) position, which resolves, includes：
Quadratic integral is carried out to inertial sensor acceleration measured directly, the movement position of sensor is calculated；
By acceleration formula：
The expression formula of displacement can be acquired：
It is expressed as applied to computer program discretization：
Wherein AnFor integral area, SnFor sampled value, Sn-1For last moment sampled value, T is the sampling interval.
The movement position that sensor can be calculated twice to the integrated acceleration detected using above formula, to three directions
Acceleration carry out respectively integral resolve can then calculate the motion conditions of sensor in space.
(3) calculating of gait parameter includes：
It is strapped in the sensor of ankle, it can be according to walking movement, the variation of attitude angle generating period.Record is strapped in ankle
Inertial sensor attitude angle mechanical periodicity；The ankle often walked is calculated using move distance calculation formula to each period
Displacement, speed, frequency movement parameter.
According to the method for data processing movement velocity, the movement of ankle are calculated using the acceleration real time data of ankle
(ankle sensor gravity direction acceleration is the time interval between adjacent 2 points of zero for distance and the frequency of ankle movements
It is reciprocal).
(1) definition of gait cycle is that side heel contact ground starts to the parapodum with institute until being contacted again ground
The time of consumption.The calculating of gait cycle：The definition of gait cycle is that side heel contact ground starts to the parapodum with again
The time consumed until contact ground.Therefore, the computational methods of gait cycle are：Acquire and record the weight of ankle sensor
The acceleration parameter of force direction simultaneously carries out noise reduction process, removes acceleration of gravity, extracts the minimum value of data after these processing
And its corresponding acquisition time Ti, Ti-1Indicate that the last time obtains the acquisition time of minimum value；
Gait cycle=Ti-Ti-1。
(2) it is known as step-length to the air line distance between the heel contact of the other side from the heel contact of side.The period
Pelvis displacement distance is exactly the step-length of human motion, the computational methods of step-length：
It acquires and records the sensor parameters at left and right ankle and pelvis and carry out noise reduction process；Extraction left and right sides foot
The parameter of ankle sensor gravity direction and its corresponding time sequentially land corresponding time point by left and right heel in terms of according to this
Section is calculated, the acceleration of gravity parameter of the sensor at the period pelvis is extracted, it, can according to above-mentioned displacement calculation formula
It is to obtain step-length：
(3) it is known as the length that strides with the air line distance between landing again to the parapodum from the heel contact of side.It strides
Long computational methods：
It acquires and records the parameter of ankle sensor and carry out noise reduction process, extract ankle sensing in the gait cycle of side
It is a length of can to obtain striding for the side according to above-mentioned displacement calculation formula for acceleration of the device in walking direction：
(4) cadence refers to the step number walked in the unit interval.The computational methods of cadence：
The average distance of cadence=1/ (left side gait cycle)+1/ (right side gait cycle)；
(5) leg speed refers to the average speed of unit interval walking, it should be the average distance that pelvis moves in the unit interval.
The computational methods of leg speed：
It acquires and records the parameter of pelvis sensor and carry out noise reduction process, extract pelvis sensor in the unit interval and exist
The acceleration in walking direction, according to speed calculation formula, can obtain the side step speed is：
(6) joint angle refers to the angle of two adjacent bone portions of human body.The computational methods of joint angle：
It acquires and records the parameter for the sensor bound with adjacent bone and carry out noise reduction process, using angle calculation public affairs
Formula calculates course angle ψ, pitching angle theta, the roll angle φ of each bone：
(4) calculating of human body attitude includes：
Human action captures system and realizes real-time capture, posture using posture inertial sensor tracking human body joint motion
Sensor node setting is more, and motion capture is finer, but it is slack-off with calculating speed that cost can be caused to increase.Therefore basis
The characteristics of human synovial structure, generally selects 17 nodes, and is bundled in arm, leg, shoulder, head, back and buttocks respectively
Position, specific installation position are：One, head sensor after both arms trail to both sides, is spaced successively in both arms and back
Arrange 8 sensors, waist each sensor up and down, from bottom to top, every leg can be arranged symmetrically 3 sensors for leg.
Before capturing motion, first have to bind into the artis of line sensor and virtual role.User makes and program void
The identical posture of quasi- role, and record the initial quaternary number attitude value Q of each junction sensor at this time0, at this time virtual role correspond to
The direction of artis is expressed as O with quaternary number0.After user's performance starts, table is calculated by optimization algorithm in t moment
Show the quaternary number Q of sensor attitudet, then the quaternary number posture O of t moment virtual role corresponding jointtExpression formula is：
Ot=Qt*Q0 -1*O0
In view of nine axle sensors can only obtain orientation information in space, so the action based on inertial sensor
Capture requires actor model to have the holding of at least foot to be contacted with ground, using this, can pass through action and redirect algorithm realization
The realization of human body walking action captures.
The unstable motion analog platform of the present invention is made of the robot platform of 6 degree of freedom, the kinematic parameter of robot
It is related to human body unstable motion parameter, the motion calculation process of the unstable motion analog platform of the present invention is given below：
Referring to Fig. 2,1) centroid transformation
Human body to the judgement of inertia motion be by the vestibular organ at ear rear portion perceive specific force at head and angular speed come
It realizes.Specific force is defined as the vector sum of linear acceleration vector and gravity negative vector, i.e. f=a-g, and wherein f is felt by human body
Specific force (the m/s being subject to2)；A is the absolute linear acceleration (m/s of human body2), g is acceleration of gravity (m/s2)。
In general, all it is to take the hostage's heart conduct when kinematic parameter (speed, the acceleration) that description people experiences
Object is considered, in mass center of human body coordinate system S-XSYSZSIn, it is necessary first to by the barycenter acceleration of peopleTransform to upper mounting plate matter
Acceleration at heart P pointsThen reconvert is to moving platform coordinate system P-XPYPZPUnder accelerationHuman feeling is arrived
Angular speedIt is transformed into coordinate system P-X as long as being coordinately transformedPYPZPUnder angular speedTransformation for mula is as follows：
In formula 6,For origin S to point of origin P in S-XSYSZSIn radius vector.
When known to the movement locus of point, the movement using natural law description point is more convenient.IfFor people
Barycenter S point absolute velocities are in S-XSYSZSProjection on each axis of coordinate system； Absolutely tangentially accelerate for people's barycenter S points
Degree is in S-XSYSZSProjection on each axis of coordinate system；It is people's barycenter S point absolute angular velocities in S-XSYSZSCoordinate
It is the projection on each axis.
It is with vector representation：
The normal acceleration of hostage's heart S points is：
Then：
In equation 8 above：
It can find out
Because P, each axis coordinate systems of S are parallel to each other,
According to the definition of specific force：
As moving platform coordinate system P-XPYPZPSpin matrix relative to earth coordinates O-XYZ.
Wherein, θ=[γ β α] is Eulerian angles of the P coordinate systems relative to earth coordinates.
Formula 16 is substituted into 15 to obtain：
Have again：
The as input of motion control arithmetic.
3) determination of filter order
High-pass filter：
In order to filter the low frequency acceleration and angular speed that vehicle drive motion platform cannot simulate, and moving platform is made to return
Neutral position uses three rank high-pass filters, direction of rotation to use bivalent high-pass filter in translation direction.Due to acceleration
With angular speed in emulator coordinate system S-XSYSZSLower filtering, moving platform will not finally return to initial position, determine to sit in the earth
High-pass filtering is carried out in mark system.
By specific force f before filtering1, angular speed w1The acceleration a being converted under earth coordinates2With Euler angle rate θ2。
θ2=Tpw1 (25)
The transmission function of three rank high-pass filters is：
Export aPHFor the acceleration at moving platform barycenter P points.The displacement S of P points is obtained after quadratic integralPH：
SPH=∫ ∫ aPHdt (27)
The transmission function of bivalent high-pass filter is：
Output is θPH, integrate θPHFor：
θPH=∫ θPHdt
θPHFor the high frequency section of emulator Eulerian angles.
Low-pass filter：
Since the working space of platform is limited, the displacement long lasting for acceleration can not be simulated.But for human body,
Can experience only start accelerate when body retreated by inertia, with acceleration at the end of body forward incline, both
Belong to the stress of high frequency section, and this period of intermediate motion with uniform acceleration be mostly do not have it is feeling.Motion cueing algorithm
It is exactly to utilize the component of acceleration of gravity on an x-y plane, to simulate the lasting part for accelerating (stress), direction is known as " inclining
Oblique coordinates system method ".
First by specific force f1By second-order low-pass filter, the high frequency section that translational motion can simulate is eliminated, and is retained
It can not achieve, can cause the low frequency part of big-movement displacement, transmission function to be：
In above formula,wnGORO DAIMON high-pass filter parameter, output are fL.It is generated followed by acceleration of gravity
At this moment component on X-Y plane just needs that " tilting Coordination module " is called to calculate the inclined angle of cockpit, and to protect
Card angular speed is limited in the threshold of feelings θ of driverlimIn (30/s).Obviously, low pass inclination will cause Z-direction acceleration
It reduces, but since inclination angle is smaller, influences less.
" tilt and coordinate " principle is as follows：
By fLIt is thought of as simulating S-X by automobile cabin inclinationSYSZSThe specific force indicated in coordinate system.In no cabin
In the case that body others are translatable and rotate, have：
Mould be：Therefore, general to coordinate accurate simulation f with inclinationLIt is impossible.Although fLAmplitude
It cannot simulate, but it is possible that its action direction is simulated by tilting cabin.Assuming that：
Then modulus has simultaneously on both sides：K=g/ | fL|
Under a proportional relationship simultaneously, formula must can be descended：
It can obtain needing the inclined Eulerian angles be by above equation：
γSL=tan-1(fSL_y/fSL_z) (35)
βSL=-tan-1{(fSL_x/fSL_z)cosγs} (36)
αSL=0 (37)
Because of upper mounting plate coordinate system P-XPYPZPWith cockpit coordinate system S-XSYSZSAlways parallel, so：
θPL=θSL=[γSL,βSL,αSL]T (38)
Finally the Eulerian angles of moving platform are：
θP=θPH+θPL (39)
The present invention, using unstability data, simulates the movement of unstability during analyzing tumble feature, measures movement ginseng
Number provides foundation to establish the assessment fallen.
The present invention unstability refer to：The mankind's walks upright due to landing as supporting point just with biped, makes the mankind
Walk upright become one it is continuous changeable and take the complex behavior process carved and keep transient behavior balance again.When human body row
When the internal and external factors walked change, the transient behavior balance of walking is broken, and just produces the unstability of walking movement.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, it will be understood by those of ordinary skill in the art that：It is still
Can be with technical scheme described in the above embodiments is modified, or which part technical characteristic is equally replaced
It changes；And these modifications or replacements, various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution
Spirit and scope.
Claims (21)
1. a kind of unstable motion data detection device, which is characterized in that including：Unstable motion analog platform and human body movement data
Detection device；
Unstable motion analog platform makes human body be in instability status for guiding human motion；
Human body movement data detection device, for measuring and recording kinematic parameter and step of the human body on unstable motion analog platform
State parameter.
2. the detection device of unstable motion data according to claim 1, which is characterized in that the unstable motion simulation is flat
Platform includes multifreedom motion guide device；
Multifreedom motion guide device, for providing multiple freedom of motion, guiding human body is in the multiple freedom of motion
Upper movement.
3. the detection device of unstable motion data according to claim 2, which is characterized in that the multifreedom motion draws
It is multi-freedom robot to lead device.
4. the detection device of unstable motion data according to claim 3, which is characterized in that the multi-freedom robot
For the robot of 6 degree of freedom.
5. according to the detection device of claim 2-4 any one of them unstable motion data, which is characterized in that further include one from
Platform is restored by the human motion position spent, the human motion position is restored stage+module and guided in the multifreedom motion
The output end of device.
6. the detection device of unstable motion data according to claim 5, which is characterized in that the human body movement data inspection
It includes at least one inertial sensor for being fixed on human body to survey device, and movements of parts of the body is fixed by detecting inertial sensor
Acceleration parameter, to obtain the kinematic parameter and gait parameter of human body.
7. the detection device of unstable motion data according to claim 6, which is characterized in that
The kinematic parameter includes human motion speed and human motion acceleration；
The gait parameter includes the kinetic parameter of gait and the kinematics parameters of gait.
8. the detection device of unstable motion data according to claim 7, which is characterized in that the gait kinematics parameter
Including：
(1) gait cycle；
(2) step-length；
(3) stride length；
(4) cadence；
(5) leg speed；
(6) joint angle.
9. the detection device of unstable motion data according to claim 6, which is characterized in that the inertial sensor is more
It is a, and be fixed on human body by binding strap.
10. the detection device of unstable motion data according to claim 8, which is characterized in that the inertial sensor is
Nine axis inertial sensors are made of three axis accelerometer, three-axis gyroscope and three axle magnetometer three parts.
11. the detection device of unstable motion data according to claim 5, which is characterized in that the human motion position
Recovery platform is the platform with one or more degree of freedom.
12. a kind of unstable motion data detection method, which is characterized in that include the following steps：
Unstability is generated using unstable motion analog platform guiding human body；
The kinematic parameter and gait parameter of record human body are measured using human body movement data detection device.
13. the detection method of unstable motion data according to claim 12, which is characterized in that include the following steps：
(1), preparation：
Human body keeps balance to restore on platform in human motion position, and human body movement data detection device is fixed on human body,
Human body does everyday actions, demarcates the coordinate system of measuring unit；
(2) detection of data：
The location information of detection of platform human body is restored in human motion position, and starts the telecontrol equipment under human foot, drives human body
Return to initial position；Meanwhile unstable motion analog platform is according to the parameter of daily exercise instability status, driving human motion position
Restore platform and special exercise occurs, makes the posture feature of human body consistent with daily unstable motion feature；The adjustment of human body meeting instinct
Posture corrects instability status；Human body movement data detection device measures and all kinematic parameters of record human body；
(3) processing of data
The acceleration information at the human body position that human body movement data detection device is tested, is converted to human body position
Speed, position kinematic parameter and gait parameter.
14. the detection method of unstable motion data according to claim 12 or 13, which is characterized in that the human motion
The measuring unit of data detection device is using nine axis inertial sensors, by three axis accelerometer, three-axis gyroscope and three axis
Magnetometer three parts form；Wherein, the three axis accelerometer is used for measuring the acceleration of three axial directions of sensor, three axis
Gyroscope is used for measuring the angular speed of three axial directions of sensor, and the three axle magnetometer is used for providing the magnetic of three axial directions of sensor
Field component.
15. the detection method of unstable motion data according to claim 14, which is characterized in that further include to human body
The binding inertial sensor at position, the inertial sensor angle calculation use expanded Kalman filtration algorithm.
16. the detection method of unstable motion data according to claim 15, which is characterized in that angle, which resolves, includes：
The data of three axis accelerometer, three-axis gyroscope and three axle magnetometer three are melted using Extended Kalman filter
It closes, and carries out attitude algorithm.
17. the detection method of unstable motion data according to claim 16, which is characterized in that the attitude algorithm packet
It includes：Three axis accelerometer (1), three-axis gyroscope (2) and three axle magnetometer (3) carry out data filtering (4), according still further to following steps
Calculate attitude angle：
S101：Attitude angle is calculated according to gyroscope：
The spin matrix description form of quaternary number is as follows:
Wherein,Indicate that carrier coordinate system is converted to the transition matrix of world coordinate system, q=[q0, q1, q2, q3] it is one group of quaternary
Number；It calculates to obtain formula by the angular acceleration of gyroscope：
q0,q1,q2,q3Indicate the sensor quaternary number of last moment, q0′,q1′,q2′,q3' indicate by △ t moment sensors
Quaternary numerical value；
Wherein,It is the output of three-axis gyroscope；It is as follows finally to calculate attitude angle:
Wherein, θ represents pitch angle, and φ represents roll angle, and ψ represents course angle, and arctan is the arc tangent in antitrigonometric function；
S102：Attitude angle is calculated according to accelerometer and magnetometer：
Pitch angle and roll angle can be calculated by bringing the output of 3-axis acceleration into following formula:
Wherein, [ax, ay, az] indicating the output of three axis accelerometer, θ represents pitch angle, and φ represents roll angle；
Accurate course angle in order to obtain, needs to carry out slope compensation to magnetometer, and the output of magnetometer is substituted into above formula, can
:
Wherein,For the output of magnetometer before compensation,Result after being compensated for magnetometer；θ and φ
For the pitch angle and roll angle being calculated by accelerometer data；
Finally, the course angle after magnetometer compensation can be obtained by following formula；
S103:Kalman's data fusion is carried out by Kalman filter：
Kalman filter is to be applied to the recursion filter of linear system prediction, and principle is to utilize existing Last status
Parameter estimate that next state, precision is high, is widely used in some real-time systems；Kalman filters
The equation of wave device includes time update equation and measurement updaue equation；The time update equation of discrete kalman filter such as following formula
It is shown；
Formula illustrates the state estimation walked from -1 step of kth to kth and covariance estimation, wherein Xk|k-1, be in -1 step of kth,
According to Xk-1|k-1, the prior state estimated, ukIt is the input of departure process, AkIndicate that state-transition matrix, B are controls
Input matrix processed；Pk|k-1It is by Xk-1|k-1Obtained error co-variance matrix, QkIt is the covariance square of discrete time control process
Battle array；
The measurement updaue equation of discrete kalman filter is shown as the following formula；
First, kalman gain K is calculated by above formulak, wherein HkIt is calculation matrix, RkIt is the covariance matrix in measurement process,
Then process gain Z measurement obtainedkIt brings formula into and obtains a posteriority state estimation Xk|k, finally obtain posteriori error association
Variance evaluation matrix Pk|k；By time update and measurement updaue equation, Kalman filtering constantly uses newest estimated value pre-
Survey the estimated value of next state；
S104:Obtain attitude angle：
After the attitude angle that two ways obtains is merged by Kalman filter, more accurate attitude angle letter has been obtained
Cease ψt, θt, φt；Recycle following formula that Eulerian angles are converted to quaternary number；
18. the detection method of unstable motion data according to claim 17, which is characterized in that position, which resolves, includes：
Quadratic integral is carried out to inertial sensor acceleration measured directly, the movement position of sensor is calculated；
By acceleration formula：
The expression formula of displacement can be acquired：
It is expressed as applied to computer program discretization：
Wherein AnFor integral area, SnFor sampled value, T is sampling interval, Sn-1For last moment sampled value；
The movement position of sensor, the acceleration to three directions can be calculated twice to the integrated acceleration detected using above formula
Degree carries out integral resolving respectively can then calculate the motion conditions of sensor in space.
19. the detection method of unstable motion data according to claim 18, which is characterized in that the calculating packet of gait parameter
It includes：
Record is strapped in the attitude angle mechanical periodicity of the inertial sensor of joint；To each period, calculated using move distance
Formula calculates the displacement of the ankle often walked, speed, frequency movement parameter.
20. the detection method of unstable motion data according to claim 19, which is characterized in that including：
(1) calculating of gait cycle：The definition of gait cycle is that side heel contact ground starts to the parapodum with being contacted again
The time consumed until ground；
The computational methods of gait cycle are：It acquires and records the acceleration parameter of the gravity direction of ankle sensor and carry out noise reduction
Processing removes acceleration of gravity, extracts the minimum value of data and its corresponding acquisition time Ti, T after these are handledi-1Indicate upper one
The corresponding acquisition time of a minimum value, gait cycle=Ti-Ti-1；
(2) computational methods of step-length：
It acquires and records the sensor parameters at left and right ankle and pelvis and carry out noise reduction process；Extraction left and right sides ankle sensing
Think highly of the parameter of force direction and its corresponding time, the corresponding time point that sequentially landed according to this using left and right heel as computation interval,
The acceleration of gravity parameter for extracting the sensor at the period pelvis calculates formula according to Bit Shift, and can obtain step-length is：
(3) stride long computational methods：
It acquires and records the parameter of ankle sensor and carry out noise reduction process, ankle sensor is in step in extraction side gait cycle
It is a length of can to obtain striding for the side according to displacement calculation formula for the acceleration of line direction：
(4) computational methods of cadence：
The average distance of cadence=1/ (left side gait cycle)+1/ (right side gait cycle)；
(5) computational methods of leg speed：
It acquires and records the parameter of pelvis sensor and carry out noise reduction process, pelvis sensor is in walking side in the extraction unit interval
To acceleration, according to speed calculation formula, the leg speed that can obtain the side is：
(6) computational methods of joint angle：
It acquires and records the parameter for the sensor bound with adjacent bone and carry out noise reduction process, using angle calculation formula, meter
Calculate course angle ψ, pitching angle theta, the roll angle φ of each bone：
21. the detection method of unstable motion data according to claim 20, which is characterized in that the calculating packet of human body attitude
It includes：
Before capturing motion, first have to bind into the artis of line sensor and virtual role；
Human body makes posture identical with program virtual role, and records the initial quaternary number attitude value of each junction sensor at this time
Q0, at this time the direction of human body corresponding joint point be expressed as O with quaternary number0；
After human body performance starts, the quaternary number Q for indicating sensor attitude is calculated by optimization algorithm in t momentt, then when t
Carve the quaternary number posture O of human body corresponding jointtExpression formula is：
Ot=Qt*Q0 -1*O0
Motion capture based on inertial sensor require human body to have at least a foot holding is contacted with ground, using this, Ke Yitong
It crosses action and redirects the realization capture that algorithm realizes human body walking action；
Inertial sensor and computer constitute human action and capture system, can acquire and record the movement of human synovial in real time
Parameter.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109091151A (en) * | 2018-09-06 | 2018-12-28 | 中国人民解放军战略支援部队信息工程大学 | A kind of pedestrian's fall detection method and device based on MIMU |
| CN109443389A (en) * | 2018-11-28 | 2019-03-08 | 电子科技大学 | Act the posture fusion method based on inertial sensor single step calibration in capture systems |
| CN110245744A (en) * | 2019-05-29 | 2019-09-17 | 金华诺普视信息技术研究所有限公司 | It is a kind of that detection method is fallen down based on multilayer perceptron |
| CN111311877A (en) * | 2020-02-20 | 2020-06-19 | 吉林农业大学 | Fall detection and positioning method and device based on attitude angle |
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| CN112188864A (en) * | 2018-10-31 | 2021-01-05 | 华为技术有限公司 | Motion index evaluation method and device |
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| CN112907895A (en) * | 2021-04-15 | 2021-06-04 | 江南造船(集团)有限责任公司 | Boats and ships personnel falling perception method, device, system, medium and electronic equipment |
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050010139A1 (en) * | 2002-02-07 | 2005-01-13 | Kamiar Aminian | Body movement monitoring device |
| US20050233868A1 (en) * | 2004-04-20 | 2005-10-20 | Mills Alden M | Stability adjustable unit |
| US20080103034A1 (en) * | 2005-03-15 | 2008-05-01 | Teruo Mihara | Balance Fitness Machine |
| CN102156054A (en) * | 2011-03-18 | 2011-08-17 | 西北工业大学 | Walking capacity tester of foot robot |
| CN102707305A (en) * | 2012-06-06 | 2012-10-03 | 山东大学 | Tumble detecting and positioning system and method |
| CN104428039A (en) * | 2012-04-24 | 2015-03-18 | 托努斯运动私人有限公司 | Exercise/training machine |
| CN204394468U (en) * | 2014-12-31 | 2015-06-17 | 浙江大学 | A kind of walking keeps away barrier aptitude tests device |
| CN105698792A (en) * | 2016-01-26 | 2016-06-22 | 上海实汇机电科技有限公司 | Dynamic MEMS (micro-electromechanical systems) inertial attitude measuring system based on self-adaptive robust integration algorithm |
| CN106539587A (en) * | 2016-12-08 | 2017-03-29 | 浙江大学 | A kind of fall risk assessment and monitoring system and appraisal procedure based on sensor of doing more physical exercises |
| CN107174255A (en) * | 2017-06-15 | 2017-09-19 | 西安交通大学 | Three-dimensional gait information gathering and analysis method based on Kinect somatosensory technology |
| CN208591046U (en) * | 2018-02-09 | 2019-03-12 | 苏州衡品医疗科技有限公司 | The detection device of unstable motion data |
-
2018
- 2018-02-09 CN CN201810135426.6A patent/CN108338791A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050010139A1 (en) * | 2002-02-07 | 2005-01-13 | Kamiar Aminian | Body movement monitoring device |
| US20050233868A1 (en) * | 2004-04-20 | 2005-10-20 | Mills Alden M | Stability adjustable unit |
| US20080103034A1 (en) * | 2005-03-15 | 2008-05-01 | Teruo Mihara | Balance Fitness Machine |
| CN102156054A (en) * | 2011-03-18 | 2011-08-17 | 西北工业大学 | Walking capacity tester of foot robot |
| CN104428039A (en) * | 2012-04-24 | 2015-03-18 | 托努斯运动私人有限公司 | Exercise/training machine |
| CN102707305A (en) * | 2012-06-06 | 2012-10-03 | 山东大学 | Tumble detecting and positioning system and method |
| CN204394468U (en) * | 2014-12-31 | 2015-06-17 | 浙江大学 | A kind of walking keeps away barrier aptitude tests device |
| CN105698792A (en) * | 2016-01-26 | 2016-06-22 | 上海实汇机电科技有限公司 | Dynamic MEMS (micro-electromechanical systems) inertial attitude measuring system based on self-adaptive robust integration algorithm |
| CN106539587A (en) * | 2016-12-08 | 2017-03-29 | 浙江大学 | A kind of fall risk assessment and monitoring system and appraisal procedure based on sensor of doing more physical exercises |
| CN107174255A (en) * | 2017-06-15 | 2017-09-19 | 西安交通大学 | Three-dimensional gait information gathering and analysis method based on Kinect somatosensory technology |
| CN208591046U (en) * | 2018-02-09 | 2019-03-12 | 苏州衡品医疗科技有限公司 | The detection device of unstable motion data |
Non-Patent Citations (3)
| Title |
|---|
| 刘琨: ""人体站立平衡系统建模与平衡能力评价方法研究"", 《中国博士学位论文全文数据库(医药卫生科技辑)》, 15 January 2017 (2017-01-15), pages 059 - 16 * |
| 季冉: ""基于惯性传感器的步态测量方法研究与实现"", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》, no. 03, 15 March 2016 (2016-03-15), pages 140 - 1123 * |
| 季冉: ""基于惯性传感器的步态测量方法研究与实现"", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》, no. 03, pages 140 - 1123 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109091151A (en) * | 2018-09-06 | 2018-12-28 | 中国人民解放军战略支援部队信息工程大学 | A kind of pedestrian's fall detection method and device based on MIMU |
| CN109091151B (en) * | 2018-09-06 | 2021-07-16 | 中国人民解放军战略支援部队信息工程大学 | Pedestrian falling detection method and device based on MIMU |
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| CN111311877A (en) * | 2020-02-20 | 2020-06-19 | 吉林农业大学 | Fall detection and positioning method and device based on attitude angle |
| CN111311877B (en) * | 2020-02-20 | 2021-11-23 | 吉林农业大学 | Fall detection and positioning method and device based on attitude angle |
| CN111345783B (en) * | 2020-03-26 | 2021-02-23 | 山东大学 | Vestibular dysfunction detection system based on inertial sensor |
| CN111345783A (en) * | 2020-03-26 | 2020-06-30 | 山东大学 | Vestibular dysfunction detection system based on inertial sensor |
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| CN113712536B (en) * | 2020-05-26 | 2023-12-26 | 李昀儒 | Unbalanced early warning method and wearing device based on gait analysis |
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| CN112418110A (en) * | 2020-11-26 | 2021-02-26 | 浙江大学 | Human gait stability assessment method and device |
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