CN109102572A - Power transformation emulates virtual hand bone ratio in VR system and estimates method - Google Patents
Power transformation emulates virtual hand bone ratio in VR system and estimates method Download PDFInfo
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- 238000012937 correction Methods 0.000 claims abstract description 6
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Abstract
The present invention provides virtual hand bone ratio presumption method in a kind of power transformation emulation VR system, it carries out the analysis of hand skeletal structure by kinematics, simplify human hands skeleton model, based on inertial sensor real-time attitude angular data, in conjunction with bone length of the hand bone length statistical value as virtual hand in anatomy, two kinds of particular poses are demarcated respectively, calculate human hands bone ratio, it is different according to the hand structure of user, adaptively change the bone ratio of virtual hand, realize fine reproduction of the virtual hand to user's hand motion, virtual hand is driven to realize power transformation emulation training, and the virtual hand in power transformation simulated virtual environment is driven to complete corresponding movement.This method realizes the correction of sensing data initial deviation, while reducing the influence due to user's individual hand proportional difference and causing the error in hand fine movement tracing process.
Description
Technical field
The present invention relates to power transformation simulation technical fields, and in particular to virtual hand bone ratio in a kind of power transformation emulation VR system
Presumption method.
Background technique
Substation is the important component of electric system, with the raising of its degree of automation, to operation maintenance personnel
Higher requirements are also raised.Virtual reality technology is transmitted as a kind of information with the means exchanged
User provides the visual informations such as structure, operating instruction, Novel presentation of equipment, virtual reality software and hardware technology it is rapid
Development be it in terms of training using providing primary condition and powerful guarantee.Using virtual reality technology and Computer Simulation
The suitable visualization virtual environment of technology building, is trained instead of physical prototyping, can effectively overcome and be carried out using physical device
Limitation brought by training in time, place and safety such as avoids training at high cost and model machine easy to damage at the drawbacks.With void
The development of quasi- reality (VR) technology, uses VR technology to construct virtual environment for substation simulation system, will greatly improve substation
The sense of reality and feeling of immersion of scene bring leap for substation simulation training.It is constructed by virtual reality technology true to nature
Substation's three-dimensional scenic and production and construction environment, by avatar under virtual environment execution carry out task training,
The difficulty and risk of work of transformer substation are fully demonstrated by feeling of immersion environment, so as to improve trainee in the correct behaviour in complicated scene
The ability of control strengthens trainee and manipulates technical ability, promote the professional standards and psychological fitness of trainee, it is multiple to improve reply
Efficiency when miscellaneous dangerous work, shortening are on duty the laundering period.
In immersed system of virtual reality, the identification and tracking of human action are the key problems of human-computer interaction.Movement is caught
The system of catching is a kind of for accurately measuring moving object in the high-tech equipment of three-dimensional space motion situation.Motion-captured (
Motion capture) it is also referred to as motion capture at home, it is one and is remembered by tracking the movement of some key points in the time domain
Biological motion is recorded, available mathematical expression is then converted into and synthesizes the process of an individual 3D movement.University Of Tianjin
Liu Xin be based on data glove and propose a kind of dynamic calibration algorithm, studied, enabled dynamic based on joint constraint and direct kinematics
Make to capture and can preferably reflect true movement；The Wang Wei of Shanghai Communications University measures entire palm using micro-inertia sensor
Eulerian angles, carry out resolving initial attitude with MAGR algorithm；The Liu Bo of Beijing Institute of Technology is based on MEMS sensor in human body
Arm bone, thigh bone and head skeleton carry out posture calibration, the standard of realization human body movement data to human skeleton model
True attitude orientation；Tannous Halim et al., which is realized, improves human motion attitude measurement accuracy based on Kalman filter method
Method；Eric Foxlin et al. using the sensor being made of gyroscope and accelerometer as Attitude Tracking acquisition unit,
The method for describing a kind of pair of human body head, neck movement tracking measurement.
At the motion-captured aspect of virtual hand, most of virtual hand calibration algorithm is all using based on anatomical standard at present
Skeleton model handles bone ratio, and leading to the hand in presented motion capture result is one " standard hand ", rather than
" the true hand " of user.The size distortion of hand model then will lead to virtual hand and miss when restoring hand fine movement
Difference.Currently based in the hand motion capture system of MEMS sensor, after sensor obtains joint attitude angle, using in anatomy
Hand bone length statistical value calculates articulations digitorum manus position as the bone length of virtual hand.Due to user's individual hand ratio
The influence of difference causes the error in hand fine movement reduction process.If being directed to each finger and palm joint of user's individual
Define length, during actual use but it is comparatively laborious.
Summary of the invention
The object of the present invention is to provide virtual hand bone ratios in a kind of power transformation emulation VR system to estimate method, solves existing
Have in technology causes hand fine movement to lead to the problem of error due to user's individual hand proportional difference.
The technical scheme is that virtual hand bone ratio estimates method, base in power transformation emulation VR system of the invention
Implement in MEMS inertial sensor hand exercise tracking system, above-mentioned MEMS inertial sensor hand exercise tracking system includes
MEMS inertial sensor, data acquisition module, DSP processing board and computer；MEMS inertial sensor includes magnetometer, acceleration
Meter and 9 axis movement sensors, method the following steps are included:
1. obtaining the location information of human hands skeletal joint point using MEMS inertial sensor hand exercise tracking system and adding
Velocity information, specifically includes the following steps:
Step 1: using the reliability and accuracy of standard posture tooling device verifying MEMS inertial sensor detection data；
Step 2: analyzing by kinematic hand skeletal structure, simplified human hands structural model is set up；
Step 3: according to the human hands structure simplified model set up, the wearable sensors on the joint that hand 11 set, benefit
The relative motion attitude data acquisition of artis is carried out with magnetometer, accelerometer and 9 axis movement sensors；
Step 4: the data acquisition module of MEMS inertial sensor hand exercise tracking system is by wireless communication by the pass of acquisition
Section attitude data is sent to DSP processing board, and DSP processing board issues computer after processing and carries out data acquisition, realizes hand master
Want the attitude measurement in joint；
2. realizing and being corrected to the initial error of wearable sensors, and to hand major skeletal based on the calibration of two kinds of particular poses
Ratio is calculated, the specific steps are as follows:
Step 1: carrying out the correction of initial error using particular pose one:
The hand of wearable sensors shows hand particular pose one, and above-mentioned hand particular pose one is that hand is horizontal, and the five fingers are simultaneously
Hold together, finger stretches naturally, and palm turned downwards；Calibration algorithm based on particular pose one obtains error deviation matrix, the fortune that will acquire
Dynamic data carry out space conversion, keep all exercise datas unified under hand bone coordinate system；
Step 2: carrying out the calibration of finger length using particular pose two:
The hand of wearable sensors shows hand particular pose two, and above-mentioned hand particular pose two is that hand is horizontal, and the five fingers are certainly
So expansion is maximum, and finger stretches naturally, and palm turned downwards；In particular pose two, index finger metacarpophalangeal joints and middle finger metacarpophalangeal joints
Spin matrix is collected by inertial sensor, the angle between index finger metacarpophalangeal joints and middle finger metacarpophalangeal jointsPass through spin moment
Battle array is calculated；At this point,
Distance of the index finger metacarpophalangeal joints to straight line where middle fingerIt indicates are as follows:
In formula,For palm wrist joint to the distance of index finger metacarpophalangeal joints；
Distance of the index finger metacarpophalangeal joints to middle finger metacarpophalangeal jointsIt indicates are as follows:
In formula,For palm wrist joint to the distance of middle finger metacarpophalangeal joints；
Hand metacarpal bone proportionate relationship is combined with the reckoning of distance between finger, virtual hand is made more to meet Aesthetic View shape by third step
Shape: long=0.673 × index finger metacarpal bone of thumb metacarpal long=0.701 × long finger metacarpals length=0.778 × third finger metacarpal bone length=0.847 ×
Pinkie metacarpal bone is long；
3. using the physiology and kinematic constraint condition of hand skeleton pattern again to the complete hand skeleton pattern finally got
It tests, verifies the reliability of exercise data.
Further embodiment is: the simplification human hands structural model set up in the second step of above-mentioned steps 1. is 1 hand
Each finger only choose two segments, choose hand 10 articulations digitorum manus and 1 palm wrist joint totally 11 joints establish hand
Hierarchy Model；Above-mentioned 10 articulations digitorum manus be the metacarpophalangeal joints of each finger, thumb distal interphalangeal joint and remaining
The proximal phalangeal joints of finger.
The present invention has the effect of positive: the present invention is directed in MEMS inertial sensor hand motion capture system, due to
The influence of user's individual hand proportional difference causes there are problems that error in hand fine movement reduction process, devises
A kind of bone ratio based on particular pose estimates method, according to the anatomical structure of human hands and kinematics analysis,
While realizing the correction of sensing data initial deviation, using two kinds of specific hand gestures calibration algorithms, in conjunction with bone ratio
Presumption, it is different according to the hand structure of user, adaptively change the bone ratio of virtual hand, further according to sensor attitude number
According to fine reproduction of the realization virtual hand to user's hand motion can effectively solve the problem that in the prior art due to user's individual
Hand proportional difference and cause hand fine movement to lead to the problem of error.
Detailed description of the invention
Fig. 1 is the schematic diagram of hand particular pose one of the present invention；
Fig. 2 is the schematic diagram of hand particular pose two of the present invention.
Specific embodiment
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
(embodiment 1)
Virtual hand bone ratio estimates method in the power transformation emulation VR system of the present embodiment, is transported based on MEMS inertial sensor hand
Motion tracking system implement, MEMS inertial sensor hand exercise tracking system include MEMS inertial sensor, data acquisition module,
DSP processing board and computer；MEMS inertial sensor includes magnetometer, accelerometer and 9 axis movement sensors, and MEMS inertia passes
Sensor hand exercise tracking system is the prior art, is not detailed.Method specific implementation of the invention the following steps are included:
1. obtaining the location information of human hands skeletal joint point using MEMS inertial sensor hand exercise tracking system and adding
Velocity information, specifically includes the following steps:
Step 1: using the reliability and accuracy of standard posture tooling device verifying MEMS inertial sensor detection data.
Standard posture tooling device is mainly formed by 12 pieces of different module designs of posture, each module is according to setting
Angle Rotation Design, have pitch angle and 0 ° of roll angle respectively, 30 °, 45 °, 60 °, the angle of yaw then passes through the entire mark of rotation
Quasi- posture tooling device control.Standard posture tooling device is the prior art, is not detailed.Utilize standard posture tooling device pair
The MEMS inertial sensor of all uses carries out test of many times respectively, by analyzing calibration pitch angular data, tests
Demonstrate,prove the reliability and accuracy of MEMS inertial sensor detection data.
Step 2: analyzing by kinematic hand skeletal structure, simplified human hands structural model is set up.
The bone of manpower mainly includes metacarpal bone and phalanges known to kinematics, in proper motion, proximal phalangeal joints
There are approximate linear relationships for the bending angle of bending angle and distal interphalangeal joint, thus can simplify manpower structure,
Set up simplified human hands structural model: each finger only chooses two segments, 11 joints of hand is chosen, wherein including
10 articulations digitorum manus (metacarpophalangeal joints of specially each finger totally 5, the nearside of the distal interphalangeal joint of thumb and remaining finger
Interphalangeal joint) 1 palm wrist joint as research object establish the hierarchy Model of hand.
Step 3: wearing sensing on the joint that hand 11 set according to the human hands structure simplified model set up
Device is acquired using the relative motion attitude data that magnetometer, accelerometer and 9 axis movement sensors carry out artis.This step
In, sensor uses the MPU9150 motion sensor of InvenSense company.
Step 4: the data acquisition module of MEMS inertial sensor hand exercise tracking system by wireless communication will acquisition
Joint attitude data be sent to DSP processing board, DSP processing board issues computer after processing and carries out data acquisition, realizes hand
The attitude measurement of portion's major joint.
2. realizing the correction to the initial error of wearable sensors, and to hand master based on the calibration of two kinds of particular poses
Bone ratio is wanted to be calculated, the specific steps are as follows:
Step 1: carrying out the correction of initial error using particular pose one.
The hand of wearable sensors shows hand particular pose one as shown in Figure 1, and hand particular pose one is that hand is in water
Flat-shaped, the five fingers close up, and finger stretches naturally, the state that palm turned downwards.CP indicates that the palm wrist joint of 1 hand, MP1 indicate in Fig. 1
The metacarpophalangeal joints of middle finger, MP2 indicate that the metacarpophalangeal joints of index finger, PIP1 indicate that the proximal phalangeal joints of middle finger, PIP2 indicate
Indicate the proximal phalangeal joints of index finger.Calibration algorithm based on particular pose one obtains error deviation matrix, the movement that will acquire
Data carry out space conversion, keep all exercise datas unified under hand bone coordinate system.Sensing data is in the first posture
Difference between spin matrix and benchmark yaw matrix is error deviation matrix, indicates movement initial runtime, sensor appearance
The yaw of sensor is made to be grouped into about definite value under state data projection to the plane of agreement, corresponding rolling and pitching are sensor
Deviation between coordinate system and hand bone coordinate system.
Step 2: carrying out the calibration of finger length using particular pose two.
The hand of wearable sensors shows hand particular pose two as shown in Figure 2, and hand particular pose two is that hand is in water
Flat-shaped, maximum is unfolded in the five fingers naturally, and finger stretches naturally, the state that palm turned downwards.CP indicates the palm wrist joint of 1 hand in Fig. 1,
MP1 indicates that the metacarpophalangeal joints of middle finger, MP2 indicate that the metacarpophalangeal joints of index finger, PIP1 indicate the proximal phalangeal joints of middle finger,
PIP2 indicates the proximal phalangeal joints of index finger.When using posture two, when maximum is unfolded in the five fingers naturally, middle finger position is not
It moves, the nearly finger joint bone and metacarpal bone approximation straight line of remaining finger, the spin matrix of index finger and middle finger metacarpophalangeal joints MP can be by being used to
Property sensor collects, angle between themIt being calculated by spin matrix.Make line segment MP2H perpendicular to line
Section MP1 PIP1, then can obtain the angle being made of MP2, CP and MP1 is；
The distance of the length of line segment HMP2 namely index finger metacarpophalangeal joints to straight line where middle fingerIt indicates are as follows:
In formula,For line segment CPMP2 length namely palm wrist joint to index finger metacarpophalangeal joints distance；
Then according to trigonometric function principle, the distance of the length of line segment MP2MP1 namely index finger metacarpophalangeal joints to middle finger metacarpophalangeal joints
It indicates are as follows:
In formula,For line segment CPMP1 length namely palm wrist joint to index finger metacarpophalangeal joints distance.
Hand metacarpal bone proportionate relationship is combined with the reckoning of distance between finger, virtual hand is made more to meet aesthetics by third step
Sight shape: long by=0.778 × nameless metacarpal bone length of long=0.701 × long finger metacarpals of long=0.673 × index finger metacarpal bone of thumb metacarpal=
0.847 × pinkie metacarpal bone is long.
3. using the physiology and kinematic constraint of hand skeleton pattern again to the complete hand skeleton pattern finally got
Condition is tested, and the reliability of exercise data is verified.
Movement of the change in shape of manpower by hand joint with bone between movable joint is realized, as human hand shape changes,
The five fingers constantly change, but actually manpower not only has joint and bone, also have a large amount of soft tissue structure, if but
These are all taken into account, the complexity of modeling can be greatly increased, therefore hand is reduced to rigid motion model and is analyzed
Research.The movement of manpower is mainly the movement in joint, is analyzed it, the joint motions of manpower are broadly divided into two kinds of forms:
First, stretching, extension and bending: for every finger, the gradually smaller movement of angle between finger-joint is bending, phase
Instead, the movement that angle becomes larger is stretching, extension.
Second, outreach and interior receipts: assuming that the central axis of middle finger is imaginary reference line, the fortune that remaining hand directing line is drawn close
It moves as interior receipts, conversely, the movement far from reference axis is outreach.
Judge whether each artis meets the joint motions of manpower and be broadly divided into two kinds of forms, if not meeting, then it is assumed that
In this frame, the exercise data inaccuracy of this artis will not be used.
Virtual hand bone ratio estimates method in the power transformation emulation VR system of the present embodiment, is realizing that sensing data is initial
While deviation corrects, hand bone precise proportions can be estimated, to realize the precise restoration of individual hand motion.By testing
Card, this method realize the human hands bone ratio presumption of low-cost and high-precision in power transformation simulated virtual environment.
Above embodiments are the explanations to a specific embodiment of the invention, rather than limitation of the present invention, related technology
The technical staff in field without departing from the spirit and scope of the present invention, can also make various transformation and variation and obtain
To corresponding equivalent technical solution, therefore all equivalent technical solutions should be included into patent protection model of the invention
It encloses.
Claims (2)
1. virtual hand bone ratio estimates method in a kind of power transformation emulation VR system, based on MEMS inertial sensor hand exercise with
Track system implement, the MEMS inertial sensor hand exercise tracking system include MEMS inertial sensor, data acquisition module,
DSP processing board and computer；MEMS inertial sensor includes that magnetometer, accelerometer and 9 axis movement sensors, feature exist
In: it the described method comprises the following steps:
1. obtaining the location information of human hands skeletal joint point using MEMS inertial sensor hand exercise tracking system and adding
Velocity information, specifically includes the following steps:
Step 1: using the reliability and accuracy of standard posture tooling device verifying MEMS inertial sensor detection data；
Step 2: analyzing by kinematic hand skeletal structure, simplified human hands structural model is set up；
Step 3: according to the human hands structure simplified model set up, the wearable sensors on the joint that hand 11 set, benefit
The relative motion attitude data acquisition of artis is carried out with magnetometer, accelerometer and 9 axis movement sensors；
Step 4: the data acquisition module of MEMS inertial sensor hand exercise tracking system is by wireless communication by the pass of acquisition
Section attitude data is sent to DSP processing board, and DSP processing board issues computer after processing and carries out data acquisition, realizes hand master
Want the attitude measurement in joint；
2. realizing and being corrected to the initial error of wearable sensors, and to hand major skeletal based on the calibration of two kinds of particular poses
Ratio is calculated, the specific steps are as follows:
Step 1: carrying out the correction of initial error using particular pose one:
The hand of wearable sensors shows hand particular pose one, and the hand particular pose one is that hand is horizontal, and the five fingers are simultaneously
Hold together, finger stretches naturally, and palm turned downwards；Calibration algorithm based on particular pose one obtains error deviation matrix, the fortune that will acquire
Dynamic data carry out space conversion, keep all exercise datas unified under hand bone coordinate system；
Step 2: carrying out the calibration of finger length using particular pose two:
The hand of wearable sensors shows hand particular pose two, and the hand particular pose two is that hand is horizontal, and the five fingers are certainly
So expansion is maximum, and finger stretches naturally, and palm turned downwards；In particular pose two, index finger metacarpophalangeal joints and middle finger metacarpophalangeal joints
Spin matrix is collected by inertial sensor, the angle between index finger metacarpophalangeal joints and middle finger metacarpophalangeal jointsPass through spin moment
Battle array is calculated；At this point,
Distance of the index finger metacarpophalangeal joints to straight line where middle fingerIt indicates are as follows:
In formula,For palm wrist joint to the distance of index finger metacarpophalangeal joints；
Distance of the index finger metacarpophalangeal joints to middle finger metacarpophalangeal jointsIt indicates are as follows:
In formula,For palm wrist joint to the distance of middle finger metacarpophalangeal joints；
Hand metacarpal bone proportionate relationship is combined with the reckoning of distance between finger, virtual hand is made more to meet Aesthetic View shape by third step
Shape: long=0.673 × index finger metacarpal bone of thumb metacarpal long=0.701 × long finger metacarpals length=0.778 × third finger metacarpal bone length=0.847 ×
Pinkie metacarpal bone is long；
3. using the physiology and kinematic constraint condition of hand skeleton pattern again to the complete hand skeleton pattern finally got
It tests, verifies the reliability of exercise data.
2. virtual hand bone ratio estimates method in power transformation emulation VR system according to claim 1, it is characterised in that: institute
It states each finger that the simplification human hands structural model set up in the second step of step 1. is 1 hand and only chooses two segments,
Choose 10 articulations digitorum manus of hand and the hierarchy Model of 1 palm wrist joint hand that totally 11 joints are established；10 fingers
Joint is metacarpophalangeal joints, the distal interphalangeal joint of thumb and the proximal phalangeal joints of remaining finger of each finger.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112486331A (en) * | 2020-12-18 | 2021-03-12 | 清华大学 | IMU-based three-dimensional space handwriting input method and device |
| CN113190112A (en) * | 2021-04-08 | 2021-07-30 | 深圳市瑞立视多媒体科技有限公司 | Method for driving target model by extensible data glove and related device |
-
2018
- 2018-07-27 CN CN201810839943.1A patent/CN109102572A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112486331A (en) * | 2020-12-18 | 2021-03-12 | 清华大学 | IMU-based three-dimensional space handwriting input method and device |
| CN113190112A (en) * | 2021-04-08 | 2021-07-30 | 深圳市瑞立视多媒体科技有限公司 | Method for driving target model by extensible data glove and related device |
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