CN1961818A - Human body weight unidimensional distribution recognition system and recognition method thereof - Google Patents

Human body weight unidimensional distribution recognition system and recognition method thereof Download PDF

Info

Publication number
CN1961818A
CN1961818A CN 200610095272 CN200610095272A CN1961818A CN 1961818 A CN1961818 A CN 1961818A CN 200610095272 CN200610095272 CN 200610095272 CN 200610095272 A CN200610095272 A CN 200610095272A CN 1961818 A CN1961818 A CN 1961818A
Authority
CN
China
Prior art keywords
human body
weight
attitude
distribution
parameter
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.)
Granted
Application number
CN 200610095272
Other languages
Chinese (zh)
Other versions
CN100448394C (en
Inventor
徐铭陶
赵邦义
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority to CNB200610095272XA priority Critical patent/CN100448394C/en
Publication of CN1961818A publication Critical patent/CN1961818A/en
Application granted granted Critical
Publication of CN100448394C publication Critical patent/CN100448394C/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The invention relates to a distribution recognize system of human weight, wherein it is formed by weighting bed (1), digit camera (2), electric scale (3), and microcomputer (4). Its recognize method comprises that the digit camera (2) and electric scale (3) collects the physical parameters of eight figures on the weight bed (1), to form the recognize formula (A)(Q)=(W) of integrated gravity distribution mode (Q), the characteristic matrix (A) and input matrix (W), and the mode convert formula (D)(q)=(Q) while the (q) means integrated weight density mode; therefore, using parameter distribution method, the qa(x) and qb(x) of density distribution can be calculated out; by integration, the one-dimension gravity distribution can be obtained as Wa(x) and Wb(x), as curvature diagram. The invention has simple operation and wide application.

Description

Human body weight unidimensional distribution recognition system and recognition methods thereof
Technical field
The invention belongs to the technical field of asking inertial parameters of human body parts, specifically, recognition system and recognition methods thereof that a kind of body gross weight of asking for help distributes along head, trunk, arm and foot leg length direction one dimension have been worked out exactly, the result who identifies can be used as the assessed information of popular routine physical examination and health care, also can be used for the simulation study of athlete's competitive sport, auxiliary clinical diagnosis and man machine system design.
Background technology
So far, the scholars of (mainly being external) are when the distribution of researching human body weight both at home and abroad, and attention still concentrates on the inertial parameters such as weight, position of centre of gravity and rotary inertia of each link of body of asking for help, and the method for employing mainly contains following three kinds:
1, based on the statistic law of human anatomy data, this method is divided into human body some links such as head, trunk, thigh, shank, foot, upper arm, forearm and hands, then based on the human anatomy data, obtain the statistical value of each link inertial parameter, this method is the most ancient, uses also most convenient, but personalized degree is low, be used for concrete people, be difficult to estimate the error of gained data, mainly be used in public man machine system design aspect;
2, based on the mathematical model method of experimenter's body geometric properties measurement data, each link of this method hypothesis human body has simple geometric shape such as sphere, cylindrical, taper shape, and have a uniform weight density, by measuring the characteristic size of all links, and then calculate their inertial parameter.This method operation is loaded down with trivial details, is difficult to automatization, and gained result's personalized degree is still lower, and nowadays this method is mainly used in the kinesiology research;
3, based on the CT scan method of each tissue density's data of human body, this originally is a kind of method that medical research and clinical diagnosis are used, the inertial parameter operation of quoting the body link of asking for help is very complicated, the expense height, radiation diminishes health in addition, embarrass popular acceptance, can only be used for special clinical diagnosis at present.
Summary of the invention
The problem to be solved in the present invention provides a kind of human body weight unidimensional distribution recognition system and recognition methods thereof, in order to identify the one dimension distribution situation of any human body gross weight along head, trunk, arm and foot leg length direction, the personalized degree height of gained data.
For solving the problems of the technologies described above, human body weight unidimensional distribution recognition system of the present invention, constitute by scale bed, digital camera, electronic scale and microcomputer, one end of scale bed be supported in hinged on, the other end is supported on the electronic scale, be provided with horizontal axis along the lateral length direction of scale bed, digital camera is positioned at the dead ahead of scale bed one side, and the data output end of electronic scale and digital camera links to each other with the microcomputer input interface by data wire respectively.
The process of utilizing above-mentioned recognition system to discern is:
(1) makes the some labelling at each articulation center and other desired location of experimenter's body homonymy, allow experimenter's body face upward then and lie on the scale bed, make different attitudes by setting rules, digital camera is made a video recording to each attitude, and with the treated formation attitude geometry of the image file input microcomputer parameter that obtains, electronic scale is measured the treated formation attitude of the support reaction input microcomputer mechanics parameter of each attitude correspondence simultaneously;
(2) human body is reduced to the toggle system, set up the concentrated weight distribution pattern { Q} that distributes along its length with the equivalence of human body gross weight static(al), use and make a concerted effort and the resultant moment theorem, set up the identification equation [A] of Q} Q)={ W}, wherein eigenmatrix [A] is formed by each attitude geometry parameter, { W} is formed by each attitude mechanics parameter the input array, calls [A] and { W} obtains { Q};
(3) use parameter distribution method to { Q} is optimized, to improve its precision;
(4) the concentrated weight density distribution pattern { q} that distributes along its length of foundation and the equivalence of human body gross weight static(al) in the toggle system, with { q} and comply with variable and write out human body and be the analytical expression q (x) that broken line distributes along its length, synthetic and the decomposition method of application of force, set up and concentrate weight density distribution pattern { q} and concentrated weight distribution pattern { pattern transfer equation [the D] { q}={Q} of Q}, det[D] ≠ 0, each unit is formed by each attitude geometry parameter in [D], with after optimizing { Q} substitution pattern transfer equation obtains that { q} is by { q} obtains q (x) again;
(5) q (x) is carried out integration and draw out the one dimension distribution curve W (x) of human body weight.
The present invention is reduced to the toggle system with human body, set up the concentrated weight distribution pattern and the concentrated weight density distribution pattern that distribute along its length on this basis with the equivalence of human body gross weight static(al), and then set up and discern equation and pattern transfer equation, human body only need be shown the attitude of regulation in proper order by setting on the scale bed, respectively microcomputer is gathered and imported to the geometric parameter and the mechanics parameter of each attitude correspondence by digital camera and electronic scale, use identification equation, pattern transfer equation and parameter distribution method and just can solve human body weight one dimension distribution along its length.Whole recognition system is simple in structure, and performing a programme writes in the microcomputer commonly used, and recognition result can directly be presented on the computer display, and also available printer prints goes out.Because the data of gathering are at the concrete experimenter, different human bodies or same individual different periods its attitude geometry parameter and attitude mechanics parameter be not quite similar, therefore the result who obtains because of the people because of the time different, personalized degree height, harmless human body, easy to operate, economical quick, easily accepted and used for masses.
Description of drawings
Fig. 1 is the formation sketch map of embodiment of the invention recognition system.
Fig. 2 is the sketch map that human body is reduced to seven hinges, eight lever systems in the embodiment of the invention.
Fig. 3 is that human body is concentrated weight density distribution pattern figure in the embodiment of the invention.
Fig. 4 is that human body is concentrated the weight distribution ideograph in the embodiment of the invention.
Fig. 5 is the operational flow diagram of recognition methods of the present invention.
Fig. 6-1 to Fig. 6-8 be respectively eight attitude sketch maps that experimenter's body is shown on the scale bed among the embodiment.
Fig. 7 is the human body weight unidimensional distribution curve sketch map of obtaining at last among the present invention.
The specific embodiment
Below in conjunction with the drawings and specific embodiments the present invention is described in further detail:
As shown in Figure 1, the recognition system that the body weight one dimension that is used for asking for help distributes is by scale bed 1, digital camera 2, electronic scale 3 and microcomputer 4 constitute, one end of scale bed 1 is supported on hinged 5, the other end is supported on the electronic scale 3, be provided with horizontal axis 6 along scale bed 1 lateral length direction, digital camera 2 is positioned at the dead ahead of scale bed 1 one sides, the data output end of electronic scale 3 and digital camera 2 links to each other with microcomputer 4 input interfaces by data wire respectively, can on microcomputer 4, connect printer 7, intermediate data and end product that identifying is produced are directly printed by printer 7, scale bed 1 is the rectangle horizontal plate, feel more comfortable for human body being faced upward being overlied, allow to arrange one deck cushion at scale bed 1 upper surface.
Theoretical basis of the present invention is the toggle system that at first human body is reduced to, and sets up human body gross weight W then in the toggle system TThe concentrated weight density distribution pattern { q} and the concentrated weight distribution pattern { Q} that distribute along its length of static(al) equivalence.Use and make a concerted effort and the resultant moment theorem, { the identification equation [A] of Q} { Q}={W}, set up and concentrate weight density distribution pattern { q} and concentrated weight distribution pattern { pattern transfer equation [the D] { q}={Q} of Q} by the synthetic and decomposition method of application of force in foundation.
As shown in Figure 2, in the present embodiment human body is reduced to 7 hinges, 8 lever systems, the knee joint on the human body, hip, lumbar vertebra, cervical vertebra, shoulder, elbow and carpal joint center modelling successively are hinge J 1, J 2, J 3, J 4, J 5, J 6, J 7, 7 hinges are divided into human body 8 sections respectively, are reduced to 8 straight-bar l 1, l 2, l 3, l 4, l 5, l 6, l 7, l 8, bar l 1, l 2, l 3, l 4, l 5Be collectively referred to as the human body master and prop up, bar l 6, l 7, l 8Be collectively referred to as human body branch.
On 7 hinges, 8 lever systems, { { Q}, { q} is with { the equal static(al) of Q} is equivalent to human body gross weight W to foundation concentrated weight density distribution pattern as shown in Figure 3 for q} and concentrated weight distribution pattern as shown in Figure 4 TIn Fig. 3, ankle, cervical vertebra, shoulder, carpal joint center be made as comply with a little, defined respectively and comply with variable p 1, p 2, p 3, p 4:
p 1=α 1q 1,p 2=α 2q 2,p 3=α 3q 3,P 4=α 4q 4
α 1α 4Be called and comply with coefficient, their value need preestablish.With { q} and comply with variable and write out human body and be the analytical expression q (x) that broken line distributes along its length is by main q a(x) and ramose q b(x) form.
In order to obtain the one dimension distributed data of experimenter's body weight, adopt above-mentioned recognition system to discern, as shown in Figure 5, its detailed process is:
(1) do to put labelling in knee joint, hip, lumbar vertebra, cervical vertebra, shoulder, elbow and the carpal joint center of experimenter's body homonymy, in addition, also need other set point in the same side, for example the mid point of ankle joint center, foot, head and hands is also stamped a labelling one by one.Allow experimenter's body face upward then and lie on the scale bed 1, make eight different attitudes in the following order, digital camera 2 is made a video recording to each attitude, and with the image file input microcomputer 4 treated formation attitude geometry parameters that obtain, electronic scale 3 is measured the support reaction input microcomputer 4 treated formation attitude mechanics parameters of each attitude correspondence simultaneously.These eight attitudes are respectively:
1. as Fig. 6-1, the human body level is lain on the back, and two upper limb stretch upwards;
2. as Fig. 6-2, the human body level is lain on the back, two upper arm levels, and two underarms and hands stretch upwards;
3. as Fig. 6-3, the human body level is lain on the back, and both hands rotate horizontal by 30 ± 5 ° of inclination angles to cephalad direction around carpal joint;
4. as Fig. 6-4, the human body level is lain on the back, the parallel trunk of two upper limb;
5. as Fig. 6-5, the human body level is lain on the back, and head upwards rotates 30 ± 5 ° around cervical vertebra;
6. as Fig. 6-6, the human body level is lain on the back, and lumbar vertebra upwards rotates 20 ± 5 ° of angles with top around lumbar vertebra, and two upper limb are parallel to lumbar vertebra with the lower part;
7. as Fig. 6-7, the human body level is lain on the back, and two lower limb upwards change 30 ± 5 ° around hip joint, and two upper limb are parallel to trunk;
8. as Fig. 6-8, the human body level is lain on the back, and both thighs upwards changes 30 ± 5 ° around hip joint, and double-legged lower limb is parallel to trunk.
Can make experimenter's body finish putting of above-mentioned eight attitudes by auxiliary stand.
(2) utilize to concentrate the weight distribution pattern identification equation [A] of Q} Q}={W}, call [A] and W} obtain Q}, wherein eigenmatrix [A] is formed by each attitude geometry parameter, { W} is formed by each attitude mechanics parameter to import array.First row of [A] is 1 entirely, and the unit of all the other eight row determines { W}=[W by the abscissa of nine some labellings in eight attitudes TLF 1... LF 8], L is the support reaction arm of scale bed 1, F 1F 8The support reaction of eight attitude correspondences measuring for electronic scale 3.The abscissa of nine some labellings, human body gross weight W TReach each support reaction and be collectively referred to as the attitude geometry mechanics parameter, they vary with each individual.
(3) use parameter distribution method to { Q} is optimized, to improve its precision.
(4) by concentrate the weight density distribution pattern q} and concentrated weight distribution pattern the pattern transfer equation [D] of Q} q}={Q}, det[D] ≠ 0, call after optimization { Q} obtains q (x), and then obtains q a(x) and q b(x), in the pattern transfer equation in [D] each the unit with 7 the hinge 8 lever systems geometry attitude parameters obtain.
(5) to q a(x) and q b(x) carry out integration and draw out the one dimension distribution curve W of human body weight a(x) and W b(x), as shown in Figure 7, two curves the rightest (on) on behalf of the human body master, end corresponding value H and h on abscissa prop up and ramose length respectively.
The present invention amplifies a little, can distribute in order to the one dimension of every upper and lower limb weight of identification human body; Also can be according to the accuracy of identification needs, be reduced to human body more than 7 hinges or be less than the toggle systems of 7 hinges, and in this system, set up corresponding concentrated weight density distribution pattern and concentrated weight distribution pattern.In Fig. 3, suitably increase and comply with variable, also can improve and concentrate the weight density distribution pattern.

Claims (2)

1, a kind of human body weight unidimensional distribution recognition system, it is characterized in that comprising scale bed (1), digital camera (2), electronic scale (3) and microcomputer (4), one end of scale bed (1) is supported on hinged (5), the other end is supported on the electronic scale (3), be provided with horizontal axis (6) along the lateral length direction of scale bed (1), digital camera (2) is positioned at the dead ahead of scale bed (1) one side, and the data output end of electronic scale (3) and digital camera (2) links to each other with microcomputer (4) input interface by data wire respectively.
2, a kind of recognition methods of human body weight unidimensional distribution is characterized in that following these steps to carrying out:
(1) makes the some labelling at each articulation center and other desired location of experimenter's body homonymy, allow experimenter's body face upward then and lie on the scale bed (1), make different attitudes by setting rules, digital camera (2) is made a video recording to each attitude, and with the treated formation attitude geometry of image file input microcomputer (4) parameter that obtains, electronic scale (3) is measured the treated formation attitude of support reaction input microcomputer (4) mechanics parameter of each attitude correspondence simultaneously;
(2) human body is reduced to the toggle system, sets up and human body gross weight (W T) the concentrated weight distribution pattern { Q} that distributes along its length of static(al) equivalence, use and make a concerted effort and the resultant moment theorem, set up { identification equation [the A] { Q}={W} of Q}, wherein eigenmatrix [A] is formed by each attitude geometry parameter, { W} is formed by each attitude mechanics parameter the input array, calls [A] and { W} obtains { Q};
(3) use parameter distribution method to { Q} is optimized, to improve its precision;
(4) in the toggle system, set up and human body gross weight (W T) the concentrated weight density distribution pattern { q} that distributes along its length of static(al) equivalence, with { q} and comply with variable and write out human body and be the analytical expression q (x) that broken line distributes along its length, synthetic and the decomposition method of application of force, set up and concentrate weight density distribution pattern { q} and concentrated weight distribution pattern { pattern transfer equation [the D] { q}={Q} of Q}, det[D] ≠ 0, each unit is formed by each attitude geometry parameter in [D], with after optimizing { Q} substitution pattern transfer equation obtains that { q} is by { Q} obtains q (x) again;
(5) q (x) is carried out integration and draw out the one dimension distribution curve W (x) of human body weight.
CNB200610095272XA 2006-12-08 2006-12-08 Human body weight unidimensional distribution recognition system and recognition method thereof Expired - Fee Related CN100448394C (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CNB200610095272XA CN100448394C (en) 2006-12-08 2006-12-08 Human body weight unidimensional distribution recognition system and recognition method thereof

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CNB200610095272XA CN100448394C (en) 2006-12-08 2006-12-08 Human body weight unidimensional distribution recognition system and recognition method thereof

Publications (2)

Publication Number Publication Date
CN1961818A true CN1961818A (en) 2007-05-16
CN100448394C CN100448394C (en) 2009-01-07

Family

ID=38081170

Family Applications (1)

Application Number Title Priority Date Filing Date
CNB200610095272XA Expired - Fee Related CN100448394C (en) 2006-12-08 2006-12-08 Human body weight unidimensional distribution recognition system and recognition method thereof

Country Status (1)

Country Link
CN (1) CN100448394C (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109393614A (en) * 2018-09-03 2019-03-01 海盐昆植生物技术有限公司 A kind of software tool for dimensional measurement of cutting the garment according to the figure
CN110916668A (en) * 2019-11-29 2020-03-27 天津大学 Method for measuring gravity center of human body and gravity center of head
CN111380668A (en) * 2018-12-27 2020-07-07 浙江舜宇智能光学技术有限公司 Precision detection system and precision detection method of depth camera

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH10197325A (en) * 1997-01-13 1998-07-31 Hiroshi Kasuga Automatic body weight recording device for bed
US6514219B1 (en) * 2000-11-17 2003-02-04 Biotonix Inc. System and method for automated biomechanical analysis and the detection and correction of postural deviations
US6387061B1 (en) * 2000-12-19 2002-05-14 Dennis J. Nitto Posture and weight distribution analyzer
JP2004271368A (en) * 2003-03-10 2004-09-30 Hitachi Engineering & Services Co Ltd Body weight measuring apparatus for patient
CN100388906C (en) * 2006-03-22 2008-05-21 山东师范大学 Human body centre-of gravity dynamic position measuring instrument and its measuring method

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109393614A (en) * 2018-09-03 2019-03-01 海盐昆植生物技术有限公司 A kind of software tool for dimensional measurement of cutting the garment according to the figure
CN111380668A (en) * 2018-12-27 2020-07-07 浙江舜宇智能光学技术有限公司 Precision detection system and precision detection method of depth camera
CN110916668A (en) * 2019-11-29 2020-03-27 天津大学 Method for measuring gravity center of human body and gravity center of head
CN110916668B (en) * 2019-11-29 2022-06-17 天津大学 Method for measuring gravity center of human body and gravity center of head

Also Published As

Publication number Publication date
CN100448394C (en) 2009-01-07

Similar Documents

Publication Publication Date Title
Nikolova et al. Estimation of male and female body segment parameters of the Bulgarian population using a 16-segmental mathematical model
Yoshioka et al. Biomechanical analysis of the relation between movement time and joint moment development during a sit-to-stand task
CN102028597B (en) Intelligent multi-state balance test training system
CN102525795A (en) Fast automatic positioning method of foot massaging robot
CN103169477B (en) Sleeping posture spine form testing method without interference with sleep
CN109567316A (en) Orthopedic insoles and its 4D printing shaping method is adjusted in rigidity
Roupa et al. On the modeling of biomechanical systems for human movement analysis: a narrative review
Mansour et al. A three dimensional multi-segmental analysis of the energetics of normal and pathological human gait
CN100448394C (en) Human body weight unidimensional distribution recognition system and recognition method thereof
Heyrman et al. Reliability of head and trunk kinematics during gait in children with spastic diplegia
CN113749836B (en) Intelligent dynamic orthosis for lumbar vertebral osteoporosis compression fracture and application method
Visser et al. Computer-aided optimal design of custom scoliosis braces considering clinical and patient evaluations
CN112205979A (en) Device and method for measuring mechanical energy of moving human body in real time
Noamani et al. Optimal estimation of anthropometric parameters for quantifying multisegment trunk kinetics
CN102028599B (en) Digital multi-state balance training device
CN201519149U (en) Intelligent polymorphic balance test training system
CN114948384B (en) Multi-mode database-based intelligent diagnosis and treatment system for cervical spondylosis
CN110314332A (en) A kind of trunk muscle group bends and stretches strength building and test seat and its application method
Eklund et al. A method for measuring the load imposed on the back of a sitting person
CN115006073A (en) Design and manufacturing method of scoliosis orthosis and scoliosis orthosis
CN201930463U (en) Intelligent standing, sitting and lying functional rehabilitation training device
CN111494078B (en) Personalized dynamic joint orthosis and preparation method thereof
Kaneda The features of muscle activity during chair standing and sitting motion in submerged condition
Aguiar et al. Global optimization method applied to the kinematics of gait in pregnant women
Mandal et al. Non-invasive Measurement of Thoracic Kyphosis and Lumber lordosis among Agricultural workers and Corporate Professionals (IT) using Flexicurve Ruler

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
C17 Cessation of patent right
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20090107

Termination date: 20131208