CN106527738B - A kind of multi information body feeling interaction glove system and method for virtual reality system - Google Patents
A kind of multi information body feeling interaction glove system and method for virtual reality system Download PDFInfo
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Abstract
The present invention provides a kind of multi information body feeling interaction glove system and method for virtual reality system, is related to technical field of virtual reality.Body feeling interaction glove system includes glove bulk, sensor module, signal operation amplifier module and analog-to-digital conversion module, vibrational feedback module, processor module and communication module, the calculating of hand exercise information and pose is carried out using the body feeling interaction glove system, calculation method is loaded into the processor module of above-mentioned glove system in the form of program, hand exercise and posture information are calculated in real time, receive the feedback information of virtual reality system host computer simultaneously and control vibrational feedback module to simulate tactile, realizes two-way interactive.The present invention by multi-sensor information fusion technology and the implicit interactions based on sEMG, can hand three-dimensional motion posture to operator and power accurately identified and calculated, be that user brings interactive experience on the spot in person.
Description
Technical field
The present invention relates to technical field of virtual reality more particularly to a kind of multi information body-sensing friendships for virtual reality system
Mutual glove system and method.
Background technique
The information input mode problem of virtual reality system is always a major problem studied, and for hand exercise information
This most important virtual reality input terminal, existing interactive gloves are only examined by single Inertia information or bending sensor
Hand gestures are surveyed, really accurate simulation, the size of especially unpredictable motoricity, real experiences can not be carried out to hand motion
It is unnatural；And existing interactive gloves do not have feedback interactive function, and only system on human carries out unidirectional gesture identification, this
Kind interactive mode can not bring the virtual experience really immersed.
Summary of the invention
In view of the drawbacks of the prior art, the present invention provides a kind of multi information body feeling interaction gloves for virtual reality system
System and method, input of the multi information body feeling interaction glove system as virtual reality system, passes through multi-sensor information fusion
Technology and implicit interactions based on sEMG, can hand three-dimensional motion posture to operator and power accurately identified and counted
It calculates, brings interactive experience on the spot in person for user.
On the one hand, the present invention provides a kind of multi information body feeling interaction glove system for virtual reality system, including hand
Cover ontology, sensor module, signal operation amplifier module and analog-to-digital conversion module, vibrational feedback module, processor module and
Communication module.
Glove bulk is worn on user's hand for carrying and fix other module groups, including finger section, wrist portion and
Oversleeve portion, oversleeve portion can be covered to user's fore-arm.
Sensor module, for acquiring movement and the posture information of hand, including bend sensor group, myoelectric sensor
Group, inertial sensor group and pressure sensor group；Bend sensor group is fixed on the outside of each fingerstall of glove bulk finger section
In finger portion interlayer, for acquiring digital flexion degree, including 5 flexible bending sensors；Myoelectric sensor group is fixed on gloves sheet
The forearm oversleeve portion of body, the skin surface myoelectric information (sEMG) generated for acquiring the movement of hand exercise related muscles, including 8
A myoelectric sensor；Inertial sensor group is fixed on the wrist portion of glove bulk, for acquiring the motion information at wrist, including
Three-dimensional accelerometer and three-dimensional gyroscope；Pressure sensor group is fixed on the inside of each fingerstall of glove bulk finger section, is used for
Measure finger contact force, including 5 contact pressure sensors.
Signal operation amplifier module is fixed on the outside of glove bulk forearm oversleeve portion, for adopting myoelectric sensor group
The sEMG WeChat ID of collection amplifies.
Analog-to-digital conversion module is fixed on the outside in glove bulk forearm oversleeve portion, for detect myoelectric sensor group
Digital signal is handled and is converted by the amplified analog signal of signal operation amplifier, to improve adopting for electromyography signal
Sample rate.
Vibrational feedback module, be fixed on the inside of the fingerstall of glove bulk finger section at, for by the hand of virtual reality system
Portion's tactile data feeds back to hand, including 4 vibrational feedback units in a manner of shaking.
Processor module is fixed on the outside of the wrist portion of glove bulk, for collecting and handling the letter of analog-to-digital conversion module
Number, and posture, displacement, contact force and the contactless force of hand are calculated, and receive the hand tactile data of virtual reality system
And feed back to vibrational feedback module.
Communication module is fixed on by processor module, for connecting the host computer of processor module and virtual reality system,
Realize information interchange and communication, the communication module is bluetooth communication module.
On the other hand, the present invention also provides a kind of using the above-mentioned multi information body feeling interaction hand for virtual reality system
Set system carries out the calculation method of hand exercise information and pose, and this method is loaded into above-mentioned body feeling interaction gloves in the form of program
The processor module of system, in real time calculates hand exercise and posture information, while receiving virtual reality system host computer
Feedback information and control vibrational feedback module to simulate tactile, realize two-way interactive, the specific method is as follows:
Step 1: the signal of each sensor group in real-time collecting sensor module, and carry out analog-to-digital conversion；
Step 2: design digital filter algorithm is filtered and denoises pre- place to each digital signal after analog-to-digital conversion
Reason；
Step 3: each finger camber is calculated according to the data of bend sensor group acquisition, according to the number of myoelectric sensor group
According to the activity for calculating finger related muscles, according to the three-dimensional accelerometer signal of inertial sensor group and three-dimensional gyroscope signal
Wrist linear movement and rotation angle, the i.e. three-dimensional perspective of hand gestures are calculated, according to the data acquisition hand of pressure sensor group
Abutment power；
Step 4: establishing polymyarian meat kinetic model, Fingers power is estimated, refer to that power includes contact force and non-contact
Power, specifically includes the following steps:
Step 4.1: carrying out the finger power prediction based on polymyarian meat kinetic model, establish between muscle activation degree and muscular force
Polymyarian meat kinetic model, for calculating muscular force by the related muscles activity that inputs, which is used to predict finger
Refer to power；
Step 4.2: utilizing the finger contact force of pressure sensor group detection and the finger of polymyarian meat kinetic model estimation
Refer to that power establishes state equation, Fingers power is accurately estimated by Extended Kalman filter；
Step 5: obtained finger camber, wrist linear movement and rotation angle, finger contact force and contactless force are believed
Breath is sent to communication module port, while receiving the tactile feedback information of virtual reality system host computer for controlling vibrational feedback
Module.
Further, in the step 2, the preprocessing process of sEMG signal is carried out using IIR digital band-pass filter
Denoising, mainly filters out high-frequency noise and 50Hz Hz noise, and formula (1) is the calculation formula of preprocessing process；
Wherein, L indicates bandpass filter function；E (t) is original sEMG signal；For being averaged in access time window
Value；U (t) is pretreated sEMG signal；u0For offset.
Further, wrist linear movement and the calculation method for rotating angle in the step 3 are as follows:
Definition rotational steps are p, which is the sum of the angular speed absolute value in T sampled point, are shown below:
Wherein, abs () is to ask signed magnitude arithmetic(al),For the angular speed of i axis direction at t-th of sampled point；
Two kinds of method for solving of hand gestures (three-dimensional perspective) are defined, are obtained respectively by accelerometer and gyro data calculating
:
Method 1: joint angles are calculated using acceleration transducer:
The vector sum R for determining three directional acceleration signals of x, y, z, is shown below:
Wherein, ax、ay、azThe respectively acceleration signal in three directions of x, y, z；
Determine the angle angle of all directionsi, as shown in formula (4), wherein i=x, y, z.
Method 2: joint angles are calculated using gyroscope, as shown in formula (5)；
anglei(n)=anglei(n-1)+wi·T1 (5)
Wherein, angleiIt (n) is the joint angles of current sampling point, angleiIt (n-1) is the joint angle of previous sampled point
Degree, wiFor the angular speed in the direction i of acquisition, T1The time rate of change of data is acquired for gyroscope；
As rotational steps p < p1When, for static or lower-speed state, application method 1 calculates hand three-dimensional perspective；Work as p1≤p≤
p2When, speed is moderate, and the result weighted sum of application method 1 and 2 calculates hand three-dimensional perspective；As p > p2When, fast speed uses
Method 2 calculates hand three-dimensional perspective；Wherein, p1、p2Respectively 20%, the 40% of hand exercise maximum angular rate.
Further, the step 4.1 carries out the prediction of the finger power based on polymyarian meat kinetic model, specifically includes following step
It is rapid:
Step 4.1.1: establishing activity model, for calculating the activation degree of muscle, reacts the size of muscular force, muscle
Activity is calculated by pretreated sEMG signal according to formula (6):
Wherein, a (t) indicates the activity of t moment, and u (t) is the pretreated sEMG signal of t moment；
Step 4.1.2: establishing contraction of muscle kinetic model, abbreviation Hill model, which includes generating actively to shrink
PowerActive contractile unit (CE) and the passive elastic force of generation connected in parallelFlexible element (PE)；
Tendon units power F in Hill model1 mtBy active convergent forceWith passive elastic forceSuperposition FmIt generates, such as
Shown in following formula；
Wherein, φ is the angle of meat fiber and tendon；Active convergent forceBy itself and muscle length item function fA(lm) and
Contraction of muscle speed term function fV(v), the fleshes contractility such as muscle activation degree a (t) and muscle maximumIt indicates, by dynamic elasticity
PowerBy itself and muscle length item function fP(lm) and the fleshes contractility such as muscle maximumIt indicates, as shown in formula (8)；
Be approximated as follows hypothesis: the included angle of meat fiber and tendon remains unchanged, and takes 0.2；Contraction of muscle rate is to actively
The influence of power variation can be ignored；The rigidity of tendon is bigger, i.e. tendon length is held essentially constant；
Then normalize muscle length lmIt can be calculated by following formula:
Wherein, lmIndicate muscle length,Indicate best muscle length；
Active convergent force and muscle length item function fA(lm) and passive elastic force and muscle length item function fP(lm) letter
Number form formula is established based on the data of medical research as curve matching by existing, and obtained fitting function formula is respectively such as
Shown in formula (10) and formula (11)；
Step 4.1.3: by obtained muscle activation degree a (t) and normalization muscle length lmFormula (7) is brought into formula (9),
Obtain muscle force prediction value.
Further, the method that Extended Kalman filter is accurately estimated in the step 4.2 are as follows:
Step 4.2.1: it establishes using Fingers power as the state equation of state；
If Fingers power FmtWith the derivative F of the powermtFor the state of Kalman filtering, the Fingers power of n-th sampled point and
Shown in the state of its derivative such as formula (12) and formula (13)；
Fmt(n)=Fmt(n-1)+Fmt(n-1)T+w1(n) (12)
Fmt(n)=Fmt(n-1)+w2(n) (13)
Wherein, w1It (n) is the process noise in Fingers power state description equation, w2(n) it is described for finger strength derivative state
Process noise in equation, T are the sampling time；
The state vector of n-th of sampled point is x (n), the estimated value F of Fingers powercAnd its derivative FcForming output vector is
Y (n)=[Fc Fc]T, formula (12) and formula (13) simultaneous obtain state equation as follows:
Wherein, z (n) is the observation state for referring to power, is determined by measured value, and w (n) is the process noise of model, and v (n) is to survey
It measures noise, is white Gaussian noise, i.e. w (n)=N (0, Q), v (n)=N (0, R), wherein Q and R is constant；NoteH=[1 1]；
Step 4.2.2: it calculates error matrix and updates state equation；
Step 4.2.2.1: the least mean-square error square of current sampling point is calculated by formula (15) by the value of a upper sampled point
Battle array P (n | n-1), P (n-1 | n-1) are the least mean-square error matrix of a upper sampled point；
P (n | n-1)=AP (n-1 | n-1) AT+Q(n) (15)
Wherein Q (n) indicates process noise covariance matrix, calculates covariance by w (n) and acquires；For first sampled point,
Initialization least mean-square error matrix P (1 | 1) it is unit battle array；
Step 4.2.2.2: amendment is updated to current state according to obtained least mean-square error matrix, such as formula (16)
It is shown, the least mean-square error matrix P (n | n) including calculating Error Gain K (n), correction value x (n | n) and current state；
Wherein, x (n | n-1) indicates the current state value calculated under the conditions of preceding state is；R (n) is measurement noise
Covariance matrix is acquired by v (n)；
Step 4.2.2.3: by the state of formula (16) newer (14) state equation, the finger strength of each sampled point is obtained
The output of estimated value, i.e. state equation.
As shown from the above technical solution, the beneficial effects of the present invention are: it is provided by the invention a kind of to be used for virtual reality
The multi information body feeling interaction glove system and method for system, by multi-sensor information fusion technology, to the hand three of operator
Dimension athletic posture and power are accurately identified and are calculated, and are all greatly improved on accuracy of identification and perception range, not only may be used
To identify hand 3 d pose, accurate finger strength estimation can also be carried out；It is virtual existing using the implicit interactions based on sEMG
Real system provides hand interactive information input mode the most natural, uses light gloves as virtual reality input equipment,
Can reduce operation caused by tradition is mismatched based on handle or operating stick input equipment and reality environment operation it is unnatural,
The shortcomings that needing short-term regulation to learn；Using two-way interactive mode, body-sensing gloves are used as input in addition to perception hand exercise information,
Its multi-modal vibrational feedback can simulate touch feedback output, realize the friendship of a kind of people and virtual reality system two-way exchange feedback
Mutual mode can more really simulate operation by human hand and tactile in virtual reality, bring the operating experience of more immersion.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of virtual reality interactive system；
Fig. 2 is the reverse side of the multi information body feeling interaction glove system provided in an embodiment of the present invention for virtual reality system
Structural schematic diagram；
Fig. 3 is the front of the multi information body feeling interaction glove system provided in an embodiment of the present invention for virtual reality system
Structural schematic diagram；
Fig. 4 is the characteristic schematic diagram of flexible bending sensor provided in an embodiment of the present invention；
Fig. 5 is the external circuitry schematic diagram of flexible bending sensor provided in an embodiment of the present invention；
Fig. 6 is the characteristic schematic diagram of contact pressure sensor FSR provided in an embodiment of the present invention；
Fig. 7 is processor module provided in an embodiment of the present invention and its external circuit structural schematic diagram；
Fig. 8 is the circuit of the multi information body feeling interaction glove system provided in an embodiment of the present invention for virtual reality system
Connection schematic diagram；
Fig. 9 is the calculation method schematic diagram of hand exercise information provided in an embodiment of the present invention and pose；
Figure 10 is the contrast curve chart of pretreated sEMG signal and original electromyography signal provided in an embodiment of the present invention；
Figure 11 is contraction of muscle kinetic model schematic diagram provided in an embodiment of the present invention；
Figure 12 is active convergent force provided in an embodiment of the present invention and muscle length item function and passive elastic force and muscle
The real curve and matched curve schematic diagram of length item function.
In figure: 1, glove bulk；101, finger section；102, wrist portion；103, oversleeve portion；2, bend sensor group；201~
205, flexible bending sensor；3, myoelectric sensor group；301~308, myoelectric sensor；4, inertial sensor group；5, pressure passes
Sensor group；501~505, pressure sensor；6, signal operation amplifier module；7, analog-to-digital conversion module；8, vibrational feedback mould
Block；801~804, vibrational feedback unit；9, processor module；10, communication module.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
Virtual reality interactive system is as shown in Figure 1, include host computer, Binocular displays terminal and the body-sensing of virtual reality system
Interaction gloves, the host computer of virtual reality system refer to the core processor for realizing virtual reality system.
The present embodiment provides a kind of multi information body feeling interaction glove system for virtual reality system, the body feeling interaction hands
Set system is the input layer of above-mentioned virtual reality interactive system, for the exercise data by multi-sensor collection arm and hand
And it is transmitted to virtual reality system host computer, host computer simulates the real motion of human hand in reality environment, and will be empty
The tactile impressions information of hand is sent to body feeling interaction glove system in near-ring border, and feedback user is in virtual environment in the form of vibration
Sense of touch, body feeling interaction glove system receives the tactile feedback information of host computer, and the vibrational feedback unit for controlling glove system is defeated
Sense of touch is shaken out.At virtual reality system host computer can be inputted according to the multi-sensor data of body feeling interaction glove system
Reason and analysis, calculate hand and arm 3 d pose and amount of exercise, including each joint angles and torque, and by virtual reality
Picture is transferred to Binocular displays terminal and shows.Body feeling interaction glove system and virtual reality system are carried out wirelessly in a manner of bluetooth
Communication.
A kind of structure such as Fig. 2 of multi information body feeling interaction glove system for virtual reality system provided in this embodiment
With shown in Fig. 3, Fig. 2 is gloves reverse structure schematic, and Fig. 3 is gloves positive structure schematic.The body feeling interaction glove system
Including glove bulk 1, sensor module, signal operation amplifier module 6, analog-to-digital conversion module 7, vibrational feedback mould/8, processing
Device module 9 and communication module 10.
Glove bulk 1 is worn on user's hand, including finger section 101, wrist for carrying and fixing other module groups
Portion 102 and oversleeve portion 103, oversleeve portion 103 can be covered to the fore-arm of user.
Sensor module, for acquiring movement and the posture information of hand, including bend sensor group 2, myoelectric sensor
Group 3, inertial sensor group 4 and pressure sensor group 5.
Bend sensor group 2 includes 5 flexible bending sensors 201~205, is individually fixed in glove bulk finger section
Refer in portion's interlayer on the outside of each fingerstall, for acquiring the curvature of each finger, to obtain finger gesture.It is soft in the present embodiment
Property bending sensor 201~205 is all made of flex45 model, and characteristic and external circuits are as shown in Figure 4 and Figure 5, wherein R1 table
Show flex45 flexible bending sensor, wherein output voltage VoutFor
Myoelectric sensor group 3 is fixed on the forearm oversleeve portion 103 of glove bulk 1, including 8 myoelectric sensors 301~
308, the obverse and reverse of glove bulk 1 is respectively equipped with four myoelectric sensors, is evenly arranged in forearm oversleeve portion, each myoelectricity
Sensor includes contact electromyographic electrode and operational amplification circuit, the skin generated for acquiring the movement of hand exercise related muscles
Surface myoelectric information (sEMG).
Inertial sensor group 4 is fixed on the wrist portion of glove bulk, including an inertial sensor, for acquiring at wrist
Motion information, in the present embodiment, inertial sensor uses the ADIS16460 of ANALOG company, is the minitype inertial of 14 pins
Measuring unit (IMU), built-in 3 dimension accelerometer and 3 dimension gyroscopes, can detect the motion state (gravitational acceleration component of hand
And angular velocity of satellite motion).
Pressure sensor group 5 is fixed on the inside of each fingerstall of glove bulk finger section, including 5 contact pressure sensings
Device 501~505, for measuring finger contact force, characteristic and the external circuits of contact pressure sensor FSR as shown in fig. 6, its
External circuits are identical as the external circuits of flex45 flexible bending sensor.
Signal operation amplifier module 6 is fixed on 103 outside of oversleeve portion of glove bulk 1, is used for myoelectric sensor group
The sEMG WeChat ID of 3 acquisitions amplifies.
Analog-to-digital conversion module 7 is fixed on the outside in the oversleeve portion 103 of glove bulk 1, for examining myoelectric sensor group 3
Survey is handled and is converted into digital signal by the amplified analog signal of signal operation amplifier, to improve electromyography signal
Sample rate.In the present embodiment, D/A converter module 7 forms 8 groups of high-precision D/A converter modules using ADS1256 chip, should
It includes power circuit, peripheral filter circuits and the connection type with Arm9CPU in circuit that module, which connects,.
Vibrational feedback module 8 is fixed at the fingerstall inside of 1 finger section 101 of glove bulk, including 4 vibrational feedback lists
Member 801~804, for the hand tactile data of virtual reality system to be fed back to hand in a manner of shaking.
Processor module 9 is fixed on 102 outside of wrist portion of glove bulk 1, for collecting and handling analog-to-digital conversion module
7 signal, and posture, displacement, contact force and the contactless force of hand are calculated, and receive the hand tactile of virtual reality system
Information simultaneously feeds back to vibrational feedback module 8.In the present embodiment, processor module 9 uses the Samsung based on Arm9 master control core
S3C2440CPU, as shown in Figure 7.
Communication module 10 is fixed on by processor module 9, is bluetooth communication, for connecting processor module 9 and void
The host computer of quasi- reality system, realizes information interchange and communication.
The circuit connection diagram of multi information body feeling interaction glove system provided in this embodiment for virtual reality system is such as
Shown in Fig. 8, since the sample frequency to electromyography signal is more demanding (>=200Hz), and the simulation IO of excessive use S3C2440 is difficult
To reach sampling precision, therefore 8 channel analog signals of myoelectric sensor are amplified by operational amplifier, and are turned by high-precision AD
Mold changing block ADS1256 is converted to digital signal.A/D module data port is connected with Digital I/O area, and is connected with cpu clock area, receives
Clock signal.Other sensors data pass through simulation I O read.Wherein pressure sensor occupies 4 analog input ends of CPU
Mouthful, flexible bending sensor occupies 5, and vibrational feedback needs to connect 6 simulation input ports, is both connected to the simulation IO of CPU
Area.CPU will carry out data transmitting with bluetooth module in a manner of serial communication, and connectivity port also is located at Digital I/O area, realize with
Wireless communication between external host computer.
It is a kind of that hand exercise letter is carried out using the above-mentioned multi information body feeling interaction glove system for virtual reality system
The calculation method of breath and pose, as shown in figure 9, this method is loaded into the processor module 9 of above-mentioned glove system in the form of program,
Hand exercise and posture information are calculated in real time, while receiving the feedback information of virtual reality system host computer and controlling shake
Feedback module 8 is moved to simulate tactile, realizes two-way interactive, the specific method is as follows.
Step 1: the signal of each sensor group in real-time collecting sensor module, and carry out analog-to-digital conversion.
Step 2: design digital filter algorithm is filtered each digital signal and noise suppression preprocessing.
The pretreatment of sEMG signal, including two links of filtering and noise reduction and full-wave rectification, preprocessing process is using IIR number
Bandpass filter is denoised, and high-frequency noise and 50Hz Hz noise are mainly filtered out.Formula (1) is that the calculating of preprocessing process is public
Formula.
Wherein, L indicates bandpass filter function；E (t) is original sEMG signal；For being averaged in access time window
Value；U (t) is pretreated sEMG signal；u0For offset.
Pretreated sEMG signal and the comparison of original electromyography signal are as shown in Figure 10.
SEMG signal is acquired by myoelectric sensor, by the action potential sequence of multiple active movement unit grantings along flesh fibre
Dimension is propagated, and after the volume conductor filtering constituted via fat/skin, comprehensive folded on the time and space that skin surface is presented
It is adding as a result, have more natural control mode, be ahead of actual motion generation, movement anticipation can be carried out, contain muscle
The abundant informations such as power, amount of articulation.Based on these features, estimate that finger strength can achieve good real-time estimation by sEMG
Effect.
Other signals are all made of existing digital filter Denoising Algorithm and are pre-processed.
Step 3: each finger camber is calculated according to the data of bend sensor group acquisition, according to the number of myoelectric sensor group
According to the activity for calculating finger related muscles, wrist linear movement and rotation angle are calculated according to the data of inertial sensor group,
According to the data acquisition finger contact force of pressure sensor group.
Finger camber is directly detected by the flexible sensor of body-sensing data glove.
The calculating of hand track is solved by the three-dimensional accelerometer signal of inertial sensor and three-dimensional gyroscope signal
Three-dimensional perspective locating for hand.Defining rotational steps first is P, which is the angular speed absolute value in T sampled point
Sum, be shown below:
Wherein, abs () is to ask signed magnitude arithmetic(al),For the angular speed of i axis direction at t-th of sampled point.Define hand appearance
Two kinds of method for solving of state (three-dimensional perspective) are calculated by accelerometer and gyro data obtain respectively:
Method 1: calculating joint angles using acceleration transducer: first finding out the vector sum R of three directional acceleration signals,
Such as following formula:
Wherein, ax、ay、azThen the acceleration signal in respectively three directions can find out the angle of all directions
anglei, as shown in formula (4), wherein i=x, y, z.
Method 2: joint angles are calculated using gyroscope, such as formula (5)；
anglei(n)=anglei(n-1)+wi·T (5)
Wherein, angleiIt (n) is the joint angles of current sampling point, anglei(n-1) the previous sampled point joint to calculate
Angle value, wiFor the angular speed in the direction i of acquisition, T is the time rate of change that gyroscope acquires data.
As rotational steps p < p1When, it is specified that application method 1 calculates for static or low speed；Work as p1≤p≤p2When, it is specified that speed
Spend moderate, the result weighted sum of application method 1 and 2 must calculate；As p > p2When, it is specified that fast speed, application method 2 calculates.
Wherein, p1、p2Respectively 20%, the 40% of hand exercise most angular speed.
In the present embodiment, acceleration transducer is accurate to static and lower-speed state angle calculation in method 1, but to speed
, there is substantially deviation in the case where degree mutation, and gyroscope is accurate to the angle calculation under rotary motion in method 2, but has zero under static state
Point offset and slight jump.Therefore the present embodiment acquires hand gestures in such a way that two methods combine.
Step 4: establishing polymyarian meat kinetic model, Fingers power is estimated, refer to that power includes contact force and non-contact
Power specifically includes following steps.
Step 4.1: the polymyarian meat kinetic model between muscle activation degree and muscular force is established, for the correlation by inputting
Muscle activation degree calculates muscular force, which is used to estimate the finger power of finger, and the specific method is as follows for model foundation.
Step 4.1.1: activity model is established.Activity model can be used for calculating the activation degree of muscle, activate journey
Degree is the important measurement for reacting muscular force size, has directly reacted the activation level of muscle.In the present embodiment, muscle activation degree is situated between
Between 0 to 1.Muscle activation degree is calculated by pretreated sEMG signal according to formula (6).
Wherein, a (t) indicates the activity of t moment, and u (t) is the pretreated sEMG signal of t moment.
Step 4.1.2: establishing contraction of muscle kinetic model, and the model abbreviation Hill model, is by a large amount of anatomy
The simulation mechanics of muscle model that experiment and medical data analysis are established, by the macroscopical mechanism model body of microcosmic muscle fibre variation
It is existing, reflect the forming process of muscular force.The muscle model structure of Hill model is as shown in figure 11.Including generating active convergent forceActive contractile unit (CE) and the passive elastic force of generation connected in parallelFlexible element (PE).It is specifically related in figure
Parameter listed by table 1.
1 Hill Parameters in Mathematical Model of table
Tendon units power F in Hill model1 mtBy active convergent forceWith passive elastic forceSuperposition FmIt generates, it can
With description are as follows:
Wherein, φ is the angle of meat fiber and tendon.Active convergent forceBy itself and muscle length item function fA(lm) and
Contraction of muscle speed term function fV(v), the fleshes contractility such as muscle activation degree a (t) and muscle maximumIt indicates, by dynamic elasticity
PowerBy itself and muscle length item function fP(lm) and the fleshes contractility such as muscle maximumIt indicates, as shown in formula (8).
Be approximated as follows hypothesis: 1) included angle of meat fiber and tendon remains unchanged, and takes 0.2；2) contraction of muscle rate pair
The influence of active force variation can be ignored, i.e. fV(v)≈1；3) rigidity of tendon is bigger, i.e. tendon length is held essentially constant, that is, returns
One changes muscle length lmIt can be calculated by following formula:
Make this hypothesis be due to Hill model excessively it is complicated it is small be conducive to modeling, and parameter is difficult to obtain, after approximating assumption
It can significantly facilitate under the premise of keeping certain calculation accuracy in line computation.
Active convergent force and muscle length item function fA(lm) and passive elastic force and muscle length item function fP(lm) letter
Number form formula is established based on the data of medical research as curve matching by existing.Figure 12 is fA(lm) and fP(lm) before fitting
Data afterwards, fA(lm) be fitted by second order Gauss function, fP(lm) be fitted by exponential function, it is obtained quasi-
Conjunction functional expression is respectively as shown in formula (10) and formula (11).
Step 4.1.3: by the muscle activation degree a (t) being calculated and normalization muscle length lmFormula (7) is brought into formula
(9), muscle force prediction value can be obtained.
Step 4.2: utilizing the finger contact force of pressure sensor group detection and the finger of polymyarian meat kinetic model estimation
Refer to that power establishes state equation, the finger power of finger is accurately estimated by Extended Kalman filter, the specific method is as follows.
Step 4.2.1: it establishes using Fingers power as the state equation of state.If Fingers power Fmt(Fingers power is multiple
The sum of tendon units power) and the power derivative FmtFor the state of Kalman filtering, the Fingers power and its derivative of n-th of sampled point
State such as formula (12) and formula (13) shown in, wherein w1It (n) is the process noise in Fingers power state description equation, w2(n)
For the process noise in finger strength derivative state descriptive equation, T is the sampling time.
Fmt(n)=Fmt(n-1)+Fmt(n-1)T+w1(n) (12)
Fmt(n)=Fmt(n-1)+w2(n) (13)
The state vector of n-th of sampled point is denoted as x (n), remembers the estimated value F of Fingers powercAnd its derivative FcComposition output arrow
Measure y (n)=[Fc Fc]T.Formula (12) and formula (13) simultaneous obtain state equation as follows:
Wherein, z (n) is the observation state for referring to power, is determined by measured value, and w (n) is the process noise of model, and v (n) is to survey
It measures noise, is white Gaussian noise, i.e. w (n)=N (0, Q), v (n)=N (0, R), wherein Q and R is constant.NoteH=[1 1].
Step 4.2.2: it calculates error matrix and updates state equation.It was calculated and is worked as by formula (15) by last sampling point value first
The least mean-square error matrix P (n | n-1) of preceding sampled point, P (n-1 | n-1) are the least mean-square error square of a upper sampled point
Battle array.For first sampled point, and initialization P (1 | 1) it is unit battle array.
P (n | n-1)=AP (n-1 | n-1) AT+Q(n) (15)
Wherein Q (n) indicates process noise covariance matrix, calculates covariance by w (n) and acquires；Then, it is missed by lowest mean square
Poor matrix is updated amendment to current state, including calculating Error Gain K (n), correction value x (n | n) and current state is most
Small Square Error matrix P (n | n), as shown in formula (16).
Wherein, x (n | n-1) indicates the current state value calculated under the conditions of preceding state is；R (n) is measurement noise
Covariance matrix is acquired by v (n).
By the state of formula (16) newer (14) state equation, the finger strength estimated value of each sampled point can be found out,
I.e. state equation exports.
Step 5: obtained finger camber, wrist linear movement and rotation angle, finger contact force and contactless force are believed
Breath is sent to communication module port, while receiving the tactile feedback information of virtual reality system host computer, anti-for controlling vibration
Present module.
A kind of multi information body feeling interaction glove system and method for virtual reality system provided by the invention, by more
Sensor data fusion technology is accurately identified and is calculated to the hand three-dimensional motion posture and power of operator, identified
It is all greatly improved on precision and perception range, can not only identify hand 3 d pose, accurate finger strength can also be carried out
Estimation；Using the implicit interactions based on sEMG, the most natural hand interactive information input side is provided for virtual reality system
Formula uses light gloves as virtual reality input equipment, it is possible to reduce tradition is based on handle or operating stick input equipment and void
Operation is unnatural caused by quasi- actual environment operation mismatches, the shortcomings that needing short-term regulation to learn；Using two-way interactive mode,
For body-sensing gloves in addition to perception hand exercise information is as input, multi-modal vibrational feedback can simulate touch feedback output, real
The interactive mode of existing a kind of people and virtual reality system two-way exchange feedback, can more really simulate the hand in virtual reality
Portion's operation and tactile, bring the operating experience of more immersion.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify to technical solution documented by previous embodiment, or some or all of the technical features are equal
Replacement；And these are modified or replaceed, the scope of the claims in the present invention that it does not separate the essence of the corresponding technical solution.
Claims (4)
1. a kind of multi information body feeling interaction glove system for virtual reality system, it is characterised in that: the system includes gloves
Ontology (1), sensor module, signal operation amplifier module (6) and analog-to-digital conversion module (7), vibrational feedback module (8), place
Manage device module (9) and communication module (10)；
The glove bulk (1) is worn on user's hand for carrying and fix other module groups, including finger section (101),
Wrist portion (102) and oversleeve portion (103), oversleeve portion (103) can be covered to the fore-arm of user；
The sensor module, for acquiring movement and the posture information of hand, including bend sensor group (2), myoelectricity sensing
Device group (3), inertial sensor group (4) and pressure sensor group (5)；The bend sensor group (2) is fixed on glove bulk (1)
Refer in portion's interlayer on the outside of each fingerstall of finger section, for acquiring digital flexion degree, including 5 flexible bending sensors；It is described
Myoelectric sensor group (3) is fixed on the forearm oversleeve portion (103) of glove bulk (1), for acquiring hand exercise related muscles fortune
The raw skin surface myoelectric information (sEMG) of movable property, including 8 myoelectric sensors；The inertial sensor group (4) is fixed on hand
The wrist portion (102) for covering ontology (1), for acquiring the motion information at wrist, including three-dimensional accelerometer and three-dimensional gyroscope；
The pressure sensor group (5) is fixed on the inside of each fingerstall of glove bulk (1) finger section (101), is connect for measuring finger
Touch, including 5 contact pressure sensors；
The signal operation amplifier module (6) is fixed on the outside of glove bulk (1) forearm oversleeve portion (103), is used for myoelectricity
The sEMG WeChat ID of sensor group (3) acquisition amplifies；
The analog-to-digital conversion module (7), is fixed on the outside in glove bulk (1) forearm oversleeve portion (103), for sensing myoelectricity
Device group (3) detection is handled and is converted into digital signal by the amplified analog signal of signal operation amplifier, to mention
The sample rate of high electromyography signal；
The vibrational feedback module (8) is fixed at the fingerstall inside of glove bulk (1) finger section (101), for will virtually show
The hand tactile data of real system feeds back to hand, including 4 vibrational feedback units in a manner of shaking；
The processor module (9) is fixed on the outside of the wrist portion (102) of glove bulk (1), is turned for collecting and handling modulus
The signal of block (7) is changed the mold, and calculates posture, displacement, contact force and the contactless force of hand, and receive virtual reality system
Hand tactile data simultaneously feeds back to vibrational feedback module (8)；Wherein, it is calculated according to the data of bend sensor group (2) acquisition each
A finger camber calculates the activity of finger related muscles according to the data of myoelectric sensor group (3), according to inertial sensor group
(4) three-dimensional accelerometer signal and three-dimensional gyroscope signal calculate wrist linear movement and rotation angle, i.e. hand gestures
Three-dimensional perspective, according to the data acquisition finger contact force of pressure sensor group (5)；The calculating of wrist linear movement and rotation angle
Method are as follows:
Definition rotational steps are p, which is the sum of the angular speed absolute value in T sampled point, are shown below:
Wherein, abs () is to ask signed magnitude arithmetic(al),For the angular speed of i axis direction at t-th of sampled point；
Two kinds of method for solving of the three-dimensional perspective of hand gestures are defined, is calculated obtain by accelerometer and gyro data respectively:
Method 1: joint angles are calculated using acceleration transducer:
The vector sum R for determining three directional acceleration signals of x, y, z, is shown below:
Wherein, ax、ay、azThe respectively acceleration signal in three directions of x, y, z；
Determine the angle angle of all directionsi, as shown in formula (4), wherein i=x, y, z；
Method 2: joint angles are calculated using gyroscope, as shown in formula (5)；
anglei(n)=anglei(n-1)+wi·T1 (5)
Wherein, angleiIt (n) is the joint angles of current sampling point, angleiIt (n-1) is the joint angles of previous sampled point, wi
For the angular speed in the direction i of acquisition, T1The time rate of change of data is acquired for gyroscope；
As rotational steps p < p1When, for static or lower-speed state, application method 1 calculates hand three-dimensional perspective；Work as p1≤p≤p2
When, speed is moderate, and the result weighted sum of application method 1 and 2 calculates hand three-dimensional perspective；As p > p2When, fast speed uses
Method 2 calculates hand three-dimensional perspective；Wherein, p1、p2Respectively 20%, the 40% of hand exercise maximum angular rate；
The communication module (10) is fixed on processor module (9) side, for connecting processor module (9) and virtual reality system
The host computer of system, realizes information interchange and communication, and the communication module (10) is bluetooth communication module.
2. a kind of carry out hand using the multi information body feeling interaction glove system described in claim 1 for virtual reality system
The calculation method of motion information and pose, it is characterised in that: this method is loaded into the processing of above-mentioned glove system in the form of program
Device module (9), in real time calculates hand exercise and posture information, while receiving the feedback letter of virtual reality system host computer
It ceases and controls vibrational feedback module (8) to simulate tactile, realize two-way interactive, the specific method is as follows:
Step 1: the signal of each sensor group in real-time collecting sensor module, and carry out analog-to-digital conversion；
Step 2: design digital filter algorithm is filtered each digital signal after analog-to-digital conversion and noise suppression preprocessing；Its
In, the preprocessing process of sEMG signal is denoised using IIR digital band-pass filter, mainly filters out high-frequency noise and 50Hz
Hz noise, formula (1) are the calculation formula of preprocessing process；
Wherein, L indicates bandpass filter function；E (t) is original sEMG signal；For the average value in access time window；u
It (t) is pretreated sEMG signal；u0For offset
Step 3: each finger camber is calculated according to the data of bend sensor group (2) acquisition, according to myoelectric sensor group (3)
Data calculate the activity of finger related muscles, according to the three-dimensional accelerometer signal of inertial sensor group (4) and three-dimensional gyro
Instrument signal calculates wrist linear movement and rotation angle, the i.e. three-dimensional perspective of hand gestures, according to the number of pressure sensor group (5)
According to acquisition finger contact force；Wherein, the calculation method of wrist linear movement and rotation angle are as follows:
Definition rotational steps are p, which is the sum of the angular speed absolute value in T sampled point, are shown below:
Wherein, abs () is to ask signed magnitude arithmetic(al),For the angular speed of i axis direction at t-th of sampled point；
Two kinds of method for solving of the three-dimensional perspective of hand gestures are defined, is calculated obtain by accelerometer and gyro data respectively:
Method 1: joint angles are calculated using acceleration transducer:
The vector sum R for determining three directional acceleration signals of x, y, z, is shown below:
Wherein, ax、ay、azThe respectively acceleration signal in three directions of x, y, z；
Determine the angle angle of all directionsi, as shown in formula (4), wherein i=x, y, z；
Method 2: joint angles are calculated using gyroscope, as shown in formula (5)；
anglei(n)=anglei(n-1)+wi·T1 (5)
Wherein, angleiIt (n) is the joint angles of current sampling point, angleiIt (n-1) is the joint angles of previous sampled point, wi
For the angular speed in the direction i of acquisition, T1The time rate of change of data is acquired for gyroscope；
As rotational steps p < p1When, for static or lower-speed state, application method 1 calculates hand three-dimensional perspective；Work as p1≤p≤p2
When, speed is moderate, and the result weighted sum of application method 1 and 2 calculates hand three-dimensional perspective；As p > p2When, fast speed uses
Method 2 calculates hand three-dimensional perspective；Wherein, p1、p2Respectively 20%, the 40% of hand exercise maximum angular rate；
Step 4: establishing polymyarian meat kinetic model, Fingers power is estimated, refer to that power includes contact force and contactless force, tool
Body the following steps are included:
Step 4.1: carrying out the finger power prediction based on polymyarian meat kinetic model, establish more between muscle activation degree and muscular force
Muscular motivation model calculates muscular force for the related muscles activity by inputting, which is used to predict Fingers power；
Step 4.2: utilizing the finger contact force of pressure sensor group (5) detection and the Fingers of polymyarian meat kinetic model estimation
Power establishes state equation, and Fingers power is accurately estimated by Extended Kalman filter；
Step 5: obtained finger camber, wrist linear movement and rotation angle, finger contact force and contactless force information are sent out
It send to communication module port, while receiving the tactile feedback information of virtual reality system host computer for controlling vibrational feedback module
(8)。
3. the calculation method of a kind of hand exercise information and pose according to claim 2, it is characterised in that: the step
4.1 carry out the finger power prediction based on polymyarian meat kinetic model, specifically includes the following steps:
Step 4.1.1: establishing activity model, for calculating the activation degree of muscle, reacts the size of muscular force, muscle activation
Degree is calculated by pretreated sEMG signal according to formula (6)；
Wherein, a (t) indicates the activity of t moment, and u (t) is the pretreated sEMG signal of t moment；
Step 4.1.2: establishing contraction of muscle kinetic model, abbreviation Hill model, which includes generating active convergent force
Active contractile unit (CE) and the passive elastic force of generation connected in parallelFlexible element (PE)；
Tendon units power F in Hill model1 mtBy active convergent forceWith passive elastic forceSuperposition FmIt generates, such as following formula
It is shown；
Wherein, φ is the angle of meat fiber and tendon；Active convergent forceBy itself and muscle length item function fA(lm) and muscle
Contraction speed item function fV(v), the fleshes contractility such as muscle activation degree a (t) and muscle maximumIt indicates, passive elastic force
By itself and muscle length item function fP(lm) and the fleshes contractility such as muscle maximumIt indicates, as shown in formula (8)；
Be approximated as follows hypothesis: the included angle of meat fiber and tendon remains unchanged, and takes 0.2；Contraction of muscle rate becomes active force
The influence of change can be ignored；The rigidity of tendon is bigger, i.e. tendon length is held essentially constant；
Then normalize muscle length lmIt can be calculated by following formula:
Wherein, lmIndicate muscle length,Indicate best muscle length；
Active convergent force and muscle length item function fA(lm) and passive elastic force and muscle length item function fP(lm) function shape
Formula is established based on the data of medical research as curve matching by existing, and obtained fitting function formula is respectively such as formula
(10) and shown in formula (11)；
Step 4.1.3: by obtained muscle activation degree s (t) and normalization muscle length lmFormula (7) is brought into formula (9), is obtained
Muscle force prediction value.
4. the calculation method of a kind of hand exercise information and pose according to claim 3, it is characterised in that: the step
The method that Extended Kalman filter is accurately estimated in 4.2 are as follows:
Step 4.2.1: it establishes using Fingers power as the state equation of state；
If Fingers power FmtWith the derivative F of the powermtFor the state of Kalman filtering, the Fingers power of n-th sampled point and its lead
Shown in several states such as formula (12) and formula (13)；
Fmt(n)=Fmt(n-1)+Fmt(n-1)T+w1(n) (12)
Fmt(n)=Fmt(n-1)+w2(n) (13)
Wherein, w1It (n) is the process noise in Fingers power state description equation, w2It (n) is finger strength derivative state descriptive equation
In process noise, T is the sampling time；
The state vector of n-th of sampled point is x (n), the estimated value F of Fingers powercAnd its derivative FcComposition output vector is y (n)
=[Fc Fc]T, formula (12) and formula (13) simultaneous obtain state equation as follows:
Wherein, z (n) is the observation state for referring to power, is determined by measured value, and w (n) is the process noise of model, and v (n) is that measurement is made an uproar
Sound is white Gaussian noise, i.e. w (n)=N (0, Q), v (n)=N (0, R), wherein Q and R is constant；NoteH
=[1 1]；
Step 4.2.2: it calculates error matrix and updates state equation；
Step 4.2.2.1: the least mean-square error matrix P (n of current sampling point is calculated by formula (15) by the value of a upper sampled point
| n-1), P (n-1 | n-1) is the least mean-square error matrix of a upper sampled point；
P (n | n-1)=AP (n-1 | n-1) AT+Q(n) (15)
Wherein Q (n) indicates process noise covariance matrix, calculates covariance by w (n) and acquires；For first sampled point, initially
Change least mean-square error matrix P (1 | 1) it is unit battle array；
Step 4.2.2.2: being updated amendment to current state according to obtained least mean-square error matrix, such as formula (16) institute
Show, the least mean-square error matrix P (n | n) including calculating Error Gain K (n), correction value x (n | n) and current state；
Wherein, x (n | n-1) indicates the current state value calculated under the conditions of preceding state is；R (n) is measurement noise association side
Poor matrix, is acquired by v (n)；
Step 4.2.2.3: by the state of formula (16) newer (14) state equation, the finger strength estimation of each sampled point is obtained
Value, i.e. state equation export.
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