CN106175802B - One kind is in body Bones and joints stress distribution detection method - Google Patents
One kind is in body Bones and joints stress distribution detection method Download PDFInfo
- Publication number
- CN106175802B CN106175802B CN201610766816.4A CN201610766816A CN106175802B CN 106175802 B CN106175802 B CN 106175802B CN 201610766816 A CN201610766816 A CN 201610766816A CN 106175802 B CN106175802 B CN 106175802B
- Authority
- CN
- China
- Prior art keywords
- joints
- bones
- bone
- muscle
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/22—Ergometry; Measuring muscular strength or the force of a muscular blow
- A61B5/221—Ergometry, e.g. by using bicycle type apparatus
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4528—Joints
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Veterinary Medicine (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Dentistry (AREA)
- Rheumatology (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Orthopedic Medicine & Surgery (AREA)
- Physical Education & Sports Medicine (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Physiology (AREA)
- Psychiatry (AREA)
- Signal Processing (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Instructional Devices (AREA)
Abstract
The invention discloses one kind in body Bones and joints stress distribution detection method, specifically includes the change in electric according to the muscle for controlling Bones and joints movement when body bone joint is in different location, constructs multichannel myoelectric information Fusion Model；Using in each bone of body Bones and joints and articular surface and joint connection relationship, Bones and joints iconography three-dimensional finite element model is constructed；Utilize the mapping relations for reflecting human body control Bones and joints motion characteristics parameter and " muscular force-joint moment " in myoelectric information, the dynamic muscular strength model such as building；When body bone joint is in different location, the biomethanics variation of bone, is constructed Bones and joints biomechanical model, is detected with neuro-muscular-Bones and joints power conduction pattern to the stress transfer and distribution situation in body Bones and joints.The present invention provides a kind of completely new approach in the detection of body Bones and joints stress distribution for human body.
Description
Technical field
The present invention relates to field of biomedicine technology, and in particular to one kind is in body Bones and joints stress distribution detection method.
Background content
It to the stress distribution detection of Bones and joints is carried out on " sample " in the past." sample " absence of vital signs, and
And the Bones and joints muscle of sample is inactive, is unable to the stress distribution of true reappearance " in body " Bones and joints.Moreover, carrying out bone to sample
The placement pressure-sensitive film piece between Bones and joints is usually taken in stress distribution detection in joint, is measured by pressure-sensitive film piece, can be right
Bones and joints cause wound.
Summary of the invention
In view of the deficiencies of the prior art, the present invention is intended to provide one kind passes through in body Bones and joints stress distribution detection method
Obtained constructed by this method in body Bones and joints stress distribution detection model, be to use non-intruding mode, as object, to be in body
The detection of Bones and joints stress distribution provides a kind of completely new approach.
To achieve the goals above, the present invention adopts the following technical scheme:
One kind includes the following steps: in body Bones and joints stress distribution detection method
S1 detection human body controls the electromyography signal variation of the muscle of Bones and joints movement when body bone joint is in different location,
Construct multichannel myoelectric information Fusion Model, to influence skeleton motion related muscles nervous physiology change information carry out obtain and
Pattern-recognition；
At the same time, using in each bone of body Bones and joints and articular surface and joint connection relationship, Bones and joints image is constructed
Three-dimensional finite element model is learned, the three-dimensional coordinate parameter of the bone and its adjacent bone in body Bones and joints different location is obtained；
S2 controls the mapping of Bones and joints motion characteristics parameter and " muscular force-joint moment " according to reflection in myoelectric information
Relationship, the dynamic muscular strength model such as building calculate the muscular force load to Bones and joints and joint moment；
The biomethanics of S3 bone when body bone joint is in different location changes, and constructs Bones and joints biomechanical model,
The stress transfer and distribution situation in body Bones and joints are detected with neuro-muscular-Bones and joints power conduction pattern.
It should be noted that detection human body controls Bones and joints movement when body bone joint is in different location in step S1
Muscle electromyography signal variation, construct multichannel myoelectric information Fusion Model, to influence skeleton motion related muscles nerve
Physiological change information carries out acquisition and pattern-recognition specifically:
Acquire surface electromyogram signal of the human body when body Bones and joints are respectively at various criterion motion bit；To the surface of acquisition
Electromyography signal constructs electromyography signal characteristic vector space (x with support vector machinesi,yi), wherein xiFor sample input quantity, yiTable
Show and xiThe desired value of corresponding support vector machines；Q rank multinomial core letter is used in Optimal Separating Hyperplane H according to Mercer theorem
Number k (x, y)=[(x, y)+1]qTo classify to electromyography signal.
Explanation is needed further exist for, the surface electromyogram signal includes main muscle activation timing, time-histories, between different muscle
Coodination modes, amplitude.
Explanation is needed further exist for, surface flesh of the human body when body Bones and joints are respectively at various criterion motion bit is acquired
Electric signal specific method is to paste surface electrode three pieces along muscle group direction on every piece of muscle, and centre is reference electrode, is divided into 2cm.
It needs further exist for illustrating, in step S1, more using q rank in Optimal Separating Hyperplane H according to Mercer theorem
Item formula kernel function k (x, y)=[(x, y)+1]qIn classifying to electromyography signal, introduces relaxation factor ζ and construct penalty
Come improve electromyography signal Feature extraction and recognition precision.
It should be noted that in step S1, using in each bone of body Bones and joints and articular surface and joint connection relationship, structure
Bones and joints iconography three-dimensional finite element model is built, the three-dimensional of bone and its adjacent bone in body Bones and joints different location is obtained and sits
The method for marking parameter specifically:
Human body is acquired in body bone using three dimensional CT layer scanning technology using the bone overall model of Bones and joints as reference coordinates
Joint is respectively at CT image when various criterion motion bit；Further its bony structure of processing reproduction is carried out using to CT image
Three-dimensional configuration, and Bones and joints mathematical model of grid optimization is constructed with this, by this model construction cortex bone, cancellous bone, marrow
Chamber, ligament group, articular cartilage and triangular fiber cartilage body Model, and then construct Bones and joints three-dimensional finite element model.
It should be noted that in step S2, according to reflection control Bones and joints motion characteristics parameter and " flesh in myoelectric information
The mapping relations of meat power-joint moment ", the dynamic muscular strength model such as building calculate the muscular force load to Bones and joints and joint moment
Specifically:
Construct electromyography signal amplitude u and muscular contraction force FA mPositive correlation exponential function relation formula a (u)=(eωuR-1-1)/(eω- 1), the maximum value that wherein R is electromyography signal amplitude u, ω are the muscle activity degree factor, -5 < ω < 0；
With muscle model is constructed based on three element Hill models of series connection elasticity member-elastic member-contraction member in parallel, so
Muscular force-length curve function f (L is solved to constructed muscle model respectively afterwardsm), and muscle is solved to muscle model
Force-velocity curve function fv(vm), in conjunction with maximum isometric muscle force FO m, solve the first muscular force F of elasticity in parallelP m=f (Lm)FO m
With the first muscular force F of contractionA m=a (u) fv(vm)f(Lm)FO m, and then pass through the first muscular force F of elasticity in parallelP mWith contraction member FA mDirectly
Summation obtains muscle muscular strength Fm；
Finally in t moment when bone joint is when a certain specific location, for Fn mFor muscle muscular strength and with rnIt (t) is pass
Save Bones and joints torque G (t)=F of torque arm length1 m(t)r1(t)+F2 m(t)r2(t)+…+Fn m(t)rn(t), wherein n is to close with bone
Save relevant muscle total number.
It should be noted that the biomethanics of bone changes when body bone joint is in different location, building Bones and joints are raw
Object mechanical model, with neuro-muscular-Bones and joints power conduction pattern to stress transfer and the distribution situation progress in body Bones and joints
The method of detection are as follows:
The Bones and joints iconography three-dimensional finite element model that the dynamic muscular strength model such as what construction step S2 was obtained and step S1 are obtained
Relationship map function T, carry out the matrix conversion of two model coordinate systems, to after coordinate conversion etc. dynamic muscular strength model carry out biology
Mechanics finite element processing, to accurately be loaded into the building of Bones and joints three-dimensional finite element model using Bones and joints torque G (t) as load
Bones and joints biomechanical model out finally goes out bone when Bones and joints are respectively at different motion position using biomethanics FEM calculation
Stress suffered by bone and its adjacent bone and stress distribution.
It needs further exist for illustrating, in step S3, in the matrix conversion for carrying out two model coordinate systems, by introducing most
Small square law come reduce coordinate conversion error.
The beneficial effects of the present invention are:
(1) relative to it is previous be mostly that the stress distribution to Bones and joints carried out on " sample " detects, utilize this hair
Obtained constructed by bright method in body Bones and joints stress distribution detection model, be to use non-intruding mode, as object, to be in body
The detection of Bones and joints stress distribution provides a kind of completely new approach；
(2) present invention proposes to wait muscular strengths mould in electromyography signal-muscular force-joint moment relational model of body Bones and joints
Type realizes the muscular force load to Bones and joints and pass with reflection human body control Bones and joints motion characteristics parameter in myoelectric information
It saves torque to calculate, obtains muscular force and joint moment so as to directly be used in the electromyography signal of body, and electromyography signal can
Directly from the surface myoelectric acquisition in body, very convenient, hurtless measure, and it can be repeated several times acquisition.
(3) using Bones and joints biomechanical model come to bone load transmission and joint contact stress distribution situation into
Row detection, can obtain Bones and joints stress distribution situation, can be conducive to further carry out stress intensity and distribution characteristics situation
It analyses and evaluates.
Detailed description of the invention
Fig. 1 is overview flow chart of the invention.
Specific embodiment
Below with reference to attached drawing, the invention will be further described, it should be noted that the present embodiment is with this technology side
Premised on case, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to this reality
Apply example.
As shown in Figure 1, one kind includes the following steps: in body Bones and joints stress distribution detection method
S1 detection human body controls the electromyography signal variation of the muscle of Bones and joints movement when body bone joint is in different location,
Construct multichannel myoelectric information Fusion Model, to influence skeleton motion related muscles nervous physiology change information carry out obtain and
Pattern-recognition:
Acquire human body main muscle activation timing, time-histories, different fleshes when body Bones and joints are respectively at various criterion motion bit
The surface electromyogram signals such as coodination modes, amplitude (paste surface electrode three pieces along muscle group direction on every piece of muscle, centre is ginseng between meat
Electrode is examined, is divided into 2cm).Electromyography signal characteristic vector space is constructed with support vector machines to the surface electromyogram signal of acquisition
(xi,yi)；Q rank multinomial kernel function k (x, y)=[(x, y)+1] is used in Optimal Separating Hyperplane H according to Mercer theoremqNext pair
Electromyography signal is classified, and introduces relaxation factor ζ building penalty to improve the Feature extraction and recognition of electromyography signal
Precision.
At the same time, using in each bone of body Bones and joints and articular surface and joint connection relationship, Bones and joints image is constructed
Three-dimensional finite element model is learned, the three-dimensional coordinate parameter of the bone and its adjacent bone in body Bones and joints different location is obtained:
Utilize three dimensional CT layer scanning technology, acquisition human body in body bone by reference coordinates of the bone overall model of Bones and joints
Joint is respectively at CT image when various criterion motion bit；Further CT image is handled again using medical image reconstruction
The three-dimensional configuration of its existing bony structure, and the Bones and joints number mould of image procossing reverse-engineering building grid optimization is utilized with this
It is soft to be constructed cortex bone, cancellous bone, ossis, ligament group, articular cartilage and triangle fiber by type for this model use finite element analysis
The isostructural body Model of bone；And then Bones and joints three-dimensional finite element model is constructed according to continuity and consonance principle.
S2 controls the mapping of Bones and joints motion characteristics parameter and " muscular force-joint moment " according to reflection in myoelectric information
Relationship, the dynamic muscular strength model such as building calculate the muscular force load to Bones and joints and joint moment:
Construct electromyography signal amplitude u and muscular contraction force FA mPositive correlation exponential function relation formula a (u)=(eωuR-1-1)/(eω- 1), the maximum value that wherein R is electromyography signal amplitude u, ω are the muscle activity degree factor (- 5 < ω < 0)；
Muscle model is constructed based on three element Hill models of elasticity member-of connecting elastic member-contraction member in parallel；Then respectively to institute
The muscle model of building solves muscular force-length curve function using cubic spline interpolation method and by linear interpolation
f(Lm)；Muscle force-velocity curve function f is solved using the piecewise function of Lloyd&Besier to muscle modelv(vm)；It ties again
Close maximum isometric muscle force FO m, the first muscular force F of elasticity in parallel can be solvedP m=f (Lm)FO mWith the first muscular force F of contractionA m=a (u) fv
(vm)f(Lm)FO m；And then pass through the first muscular force F of elasticity in parallelP mWith contraction member FA mDirectly summation obtains muscle muscular strength Fm；Finally exist
T moment is when bone joint is when a certain specific location, for Fn mFor muscle muscular strength and with rn(t) (straight for joint torque arm length
Connect and obtained in Bones and joints three-dimensional finite element model) Bones and joints torque G (t)=F1 m(t)r1(t)+F2 m(t)r2(t)+…+Fn m
(t)rn(t), wherein n is muscle total number relevant to Bones and joints.
The biomethanics of S3 bone when body bone joint is in different location changes, and constructs Bones and joints biomechanical model,
The stress transfer and distribution situation in body Bones and joints are detected with neuro-muscular-Bones and joints power conduction pattern:
Construct body Bones and joints etc. dynamic muscular strength model (electromyography signal-muscular force-joint moment relational model) and bone close
The relationship map function T of three-dimensional finite element model is saved, the matrix conversion of two model coordinate systems is carried out, and introduces least square method
Reduce the error of coordinate conversion, to after coordinate conversion etc. dynamic muscular strength model carry out the processing of biomethanics finite element, thus handle
Bones and joints torque G (t) is accurately loaded into Bones and joints three-dimensional finite element model as load and constructs Bones and joints biomechanical model,
Finally go out when Bones and joints are respectively at different motion position stress suffered by bone and its adjacent bone using biomethanics FEM calculation
And stress distribution.
For those skilled in the art, it can be made various corresponding according to above technical solution and design
Change and modification, and all these change and modification should be construed as being included within the scope of protection of the claims of the present invention.
Claims (9)
1. one kind is in body Bones and joints stress distribution detection method, which comprises the steps of:
S1 detection human body controls the electromyography signal variation of the muscle of Bones and joints movement, building when body bone joint is in different location
Multichannel myoelectric information Fusion Model carries out acquisition and mode to the nervous physiology change information for influencing skeleton motion related muscles
Identification；
At the same time, using in each bone of body Bones and joints and articular surface and joint connection relationship, Bones and joints iconography three is constructed
Finite element model is tieed up, the three-dimensional coordinate parameter of the bone and its adjacent bone in body Bones and joints different location is obtained；
S2 is closed according to the mapping of reflection control Bones and joints motion characteristics parameter and " muscle muscular strength-joint moment " in myoelectric information
System, the dynamic muscular strength model such as building, calculates the muscle muscular strength and joint moment loaded to Bones and joints；
The biomethanics of S3 bone when body bone joint is in different location changes, and Bones and joints biomechanical model is constructed, with mind
The stress transfer and distribution situation in body Bones and joints are detected through-muscle-Bones and joints power conduction pattern.
2. according to claim 1 in body Bones and joints stress distribution detection method, which is characterized in that in step S1, detection
Human body controls the electromyography signal variation of the muscle of Bones and joints movement when body bone joint is in different location, constructs multichannel myoelectricity
Information fusion model obtain to the nervous physiology change information for influencing skeleton motion related muscles and pattern-recognition is specific
Are as follows:
Acquire surface electromyogram signal of the human body when body Bones and joints are respectively at various criterion motion bit；To the surface myoelectric of acquisition
Signal constructs electromyography signal characteristic vector space (x with support vector machinesi,yi), wherein xiFor sample input quantity, yiExpression and xi
The desired value of corresponding support vector machines；According to Mercer theorem in Optimal Separating Hyperplane H using q rank multinomial kernel function k (x,
Y)=[(x, y)+1]qTo classify to electromyography signal.
3. according to claim 2 in body Bones and joints stress distribution detection method, which is characterized in that the surface myoelectric letter
It number include main muscle activation timing, time-histories, coodination modes between different muscle, amplitude.
4. according to claim 2 in body Bones and joints stress distribution detection method, which is characterized in that acquisition human body is in body bone
Surface electromyogram signal specific method when joint is respectively at various criterion motion bit is on every piece of muscle along muscle group direction patch table
Face electrode three pieces, centre are reference electrode, are divided into 2cm.
5. according to claim 2 in body Bones and joints stress distribution detection method, which is characterized in that in step S1, in root
Q rank multinomial kernel function k (x, y)=[(x, y)+1] is used in Optimal Separating Hyperplane H according to Mercer theoremqCome to electromyography signal
In being classified, relaxation factor ζ building penalty is introduced to improve the precision of the Feature extraction and recognition of electromyography signal.
6. according to claim 1 in body Bones and joints stress distribution detection method, which is characterized in that in step S1, utilize
In each bone of body Bones and joints and articular surface and joint connection relationship, Bones and joints iconography three-dimensional finite element model is constructed, is obtained
The method of the three-dimensional coordinate parameter of bone and its adjacent bone in body Bones and joints different location specifically:
Human body is acquired in body Bones and joints using three dimensional CT layer scanning technology using the bone overall model of Bones and joints as reference coordinates
It is respectively at CT image when various criterion motion bit；Further using to CT image carry out processing reproduce its bony structure three
Form is tieed up, and constructs with this Bones and joints mathematical model of grid optimization, by this model construction cortex bone, cancellous bone, ossis, tough
Body Model with group, articular cartilage and triangular fiber cartilage, and then construct Bones and joints three-dimensional finite element model.
7. according to claim 1 in body Bones and joints stress distribution detection method, which is characterized in that in step S2, according to
The mapping relations of reflection control Bones and joints motion characteristics parameter and " muscle muscular strength-joint moment ", building etc. in myoelectric information
Dynamic muscular strength model, calculates the muscle muscular strength and joint moment loaded to Bones and joints specifically:
Construct electromyography signal amplitude u and muscular contraction force FA mPositive correlation exponential function relation formula a (u)=(eωuR-1-1)/
(eω- 1), the maximum value that wherein R is electromyography signal amplitude u, ω are the muscle activity degree factor, -5 < ω < 0；
With muscle model is constructed based on three element Hill models of series connection elasticity member-elastic member-contraction member in parallel, then divide
It is other that muscle muscular strength-length curve function f (L is solved to constructed muscle modelm), and muscle flesh is solved to muscle model
Force-velocity curve function fv(vm), in conjunction with maximum isometric muscle force FO m, solve the first muscle muscular strength F of elasticity in parallelP m=f (Lm)
FO mWith muscular contraction force FA m=a (u) fv(vm)f(Lm)FO m, and then pass through the first muscle muscular strength F of elasticity in parallelP mWith muscular contraction force
FA mDirectly summation obtains muscle muscular strength Fn m；
Finally in t moment when bone joint is when a certain specific location, for Fn mFor muscle muscular strength and with rnIt (t) is joint power
The Bones and joints torque G (t) of arm lengths=F1 m(t)r1(t)+F2 m(t)r2(t)+…+Fn m(t)rn(t), wherein n is and Bones and joints phase
The muscle total number of pass.
8. according to claim 7 in body Bones and joints stress distribution detection method, which is characterized in that in step S3, in body
The biomethanics of bone joint bone when different location changes, and Bones and joints biomechanical model is constructed, with neuro-muscular-bone
The method that the power conduction pattern in joint detects the stress transfer and distribution situation in body Bones and joints are as follows:
The pass for the Bones and joints iconography three-dimensional finite element model that the dynamic muscular strength model such as what construction step S2 was obtained and step S1 are obtained
Be mapping function T, carry out the matrix conversion of two model coordinate systems, to after coordinate conversion etc. dynamic muscular strength model carry out biomethanics
Finite element processing, so that being accurately loaded into Bones and joints three-dimensional finite element model using Bones and joints torque G (t) as load constructs bone
Joint biomechanical model, finally using biomethanics FEM calculation go out when Bones and joints are respectively at different motion position bone and
Stress suffered by its adjacent bone and stress distribution.
9. according to claim 8 in body Bones and joints stress distribution detection method, which is characterized in that in step S3, into
In the matrix conversion of two model coordinate systems of row, the error of coordinate conversion is reduced by introducing least square method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610766816.4A CN106175802B (en) | 2016-08-29 | 2016-08-29 | One kind is in body Bones and joints stress distribution detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610766816.4A CN106175802B (en) | 2016-08-29 | 2016-08-29 | One kind is in body Bones and joints stress distribution detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106175802A CN106175802A (en) | 2016-12-07 |
| CN106175802B true CN106175802B (en) | 2018-12-28 |
Family
ID=58088655
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610766816.4A Expired - Fee Related CN106175802B (en) | 2016-08-29 | 2016-08-29 | One kind is in body Bones and joints stress distribution detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106175802B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108309295A (en) * | 2018-02-11 | 2018-07-24 | 宁波工程学院 | A kind of arm muscular strength assessment method |
| CN109008953B (en) * | 2018-05-24 | 2021-07-30 | 中孚医疗（深圳）有限公司 | Bone mechanical property measuring method |
| CN109523625B (en) * | 2018-09-29 | 2023-05-12 | 上海涛影医疗科技有限公司 | Ligament length quantification method |
| US20220047246A1 (en) * | 2018-12-14 | 2022-02-17 | Shenzhen Institutes Of Advanced Technology | Method and device for evaluating muscle tension |
| CN109793500B (en) * | 2019-01-24 | 2021-10-01 | 河南省人民医院 | Knee joint load mechanics analytical equipment |
| CN112364785B (en) * | 2020-11-13 | 2023-07-25 | 中移雄安信息通信科技有限公司 | Exercise training guiding method, device, equipment and computer storage medium |
| CN112587242B (en) * | 2020-12-11 | 2023-02-03 | 山东威高手术机器人有限公司 | Master hand simulation method of surgical robot, master hand and application |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7623944B2 (en) * | 2001-06-29 | 2009-11-24 | Honda Motor Co., Ltd. | System and method of estimating joint loads in a three-dimensional system |
| US20140152443A1 (en) * | 2012-08-09 | 2014-06-05 | Garrett L. Cammans | Posture training device having multiple sensitivity levels and both positive and negative feedback |
| EP2789293A1 (en) * | 2013-04-12 | 2014-10-15 | Commissariat à l'Énergie Atomique et aux Énergies Alternatives | Methods to monitor consciousness |
| CN103761392B (en) * | 2014-01-23 | 2017-02-15 | 南京工程学院 | Muscle strength model optimizing method for humanoid robot synergic movement |
| JP2016096889A (en) * | 2014-11-19 | 2016-05-30 | 株式会社東芝 | Image analysis apparatus, image analysis method and program |
| CN104915519B (en) * | 2015-06-30 | 2018-05-11 | 中国人民解放军第三军医大学第二附属医院 | A kind of cranio-maxillofacial method for establishing model and device |
| CN105139442A (en) * | 2015-07-23 | 2015-12-09 | 昆明医科大学第一附属医院 | Method for establishing human knee joint three-dimensional simulation model in combination with CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) |
-
2016
- 2016-08-29 CN CN201610766816.4A patent/CN106175802B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN106175802A (en) | 2016-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106175802B (en) | One kind is in body Bones and joints stress distribution detection method | |
| San Antonio et al. | Orientation of orthotropic material properties in a femur FE model: A method based on the principal stresses directions | |
| Rane et al. | Deep learning for musculoskeletal force prediction | |
| Riskin et al. | Quantifying the complexity of bat wing kinematics | |
| Swartz et al. | Wing structure and the aerodynamic basis of flight in bats | |
| Seth et al. | Muscle contributions to upper-extremity movement and work from a musculoskeletal model of the human shoulder | |
| Zhu et al. | Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction | |
| CN100415159C (en) | Dynamic characteristic analysis method of real-time tendency of heart state | |
| Huang et al. | An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm | |
| Huang et al. | Muscle-tendon units localization and activation level analysis based on high-density surface EMG array and NMF algorithm | |
| Atasoy et al. | 24 DOF EMG controlled hybrid actuated prosthetic hand | |
| Shaharudin et al. | Muscle synergies of untrained subjects during 6 min maximal rowing on slides and fixed ergometer | |
| Xiao et al. | GADF/GASF-HOG: feature extraction methods for hand movement classification from surface electromyography | |
| Bolsterlee et al. | The effect of scaling physiological cross-sectional area on musculoskeletal model predictions | |
| Zhang et al. | HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion | |
| Goislard de Monsabert et al. | A scaling method to individualise muscle force capacities in musculoskeletal models of the hand and wrist using isometric strength measurements | |
| Ravera et al. | Estimation of muscle forces in gait using a simulation of the electromyographic activity and numerical optimization | |
| Hu et al. | Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination | |
| Tang et al. | Development of software for human muscle force estimation | |
| Segala et al. | Nonlinear smooth orthogonal decomposition of kinematic features of sawing reconstructs muscle fatigue evolution as indicated by electromyography | |
| Auccahuasi et al. | Analysis of a mechanism to evaluate upper limb muscle activity based on surface electromyography using the MYO-EMG device | |
| CN107374610B (en) | Magnetocardiogram generation method and system | |
| Afsharipour | Estimation of load sharing among muscles acting on the same joint and Applications of surface electromyography. | |
| Jiang et al. | Force estimation based on sEMG using wavelet analysis and neural network | |
| YuWei et al. | Research on the motion characteristic of elbow joint angle based on the sEMG of single muscle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20181228 Termination date: 20190829 |