CN110772262B - Comfort evaluation method for human body tower-climbing posture - Google Patents

Comfort evaluation method for human body tower-climbing posture Download PDF

Info

Publication number
CN110772262B
CN110772262B CN201911235962.4A CN201911235962A CN110772262B CN 110772262 B CN110772262 B CN 110772262B CN 201911235962 A CN201911235962 A CN 201911235962A CN 110772262 B CN110772262 B CN 110772262B
Authority
CN
China
Prior art keywords
target
muscle
tower
climbing
sub
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.)
Active
Application number
CN201911235962.4A
Other languages
Chinese (zh)
Other versions
CN110772262A (en
Inventor
胡聪
杨鑫
王岩
石俏
吴慧峰
唐建辉
庄巨周
韩广超
时云月
胡正庭
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.)
Guangdong Power Grid Co Ltd
Foshan Power Supply Bureau of Guangdong Power Grid Corp
Original Assignee
Guangdong Power Grid Co Ltd
Foshan Power Supply Bureau of Guangdong Power Grid Corp
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 Guangdong Power Grid Co Ltd, Foshan Power Supply Bureau of Guangdong Power Grid Corp filed Critical Guangdong Power Grid Co Ltd
Priority to CN201911235962.4A priority Critical patent/CN110772262B/en
Publication of CN110772262A publication Critical patent/CN110772262A/en
Application granted granted Critical
Publication of CN110772262B publication Critical patent/CN110772262B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
    • A61B5/1122—Determining geometric values, e.g. centre of rotation or angular range of movement of movement trajectories
    • 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]
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/20—Workers

Abstract

The invention provides a comfort evaluation method for a tower-climbing posture of a human body, which comprises the following steps: capturing and storing motion trail data of a tester on a target part of the body of a tower climbing worker; analyzing the motion track data; judging whether the activation degree of muscles of the target part of the tower climbing staff is greater than a standard threshold h, and if so, executing the step of acquiring myoelectric signals; otherwise, discarding the muscle records which do not meet the standard; collecting electromyographic signals EMG (electro-magnetic) of muscles of a target part by using a physiological recordere0i(ii) a Adopts a physiological recorder to collect the EMG signals of the muscles of the target part in the maximum autonomous contraction statee1i(ii) a Calculating the muscle activation degree of the muscle at the target part; and establishing a human body tower climbing posture evaluation index by using an analytic hierarchy process. The tower climbing posture assessment system can be used for assessing the comfort degree of the tower climbing posture adopted by a tower climbing worker in the tower climbing process, further guiding the climbing posture of a power overhaul worker in the tower climbing process, and reducing the fatigue feeling of the worker in using brute force to climb the tower.

Description

Comfort evaluation method for human body tower-climbing posture
Technical Field
The invention relates to the technical field of pole tower climbing posture assessment, in particular to a comfort assessment method for a human body tower climbing posture.
Background
In order to optimize energy resource allocation and save corridor land of a transmission line, the ultra-high voltage transmission technology is being developed in China, but transmission towers are higher and higher along with the improvement of transmission voltage grades, so that the workload and the working difficulty of workers in line maintenance operation are higher and higher, and the difficulty of climbing the towers is higher and higher.
At present, the staff climbs the tower and mainly takes the bare-handed mode of climbing of manpower, still need to take necessary maintenance instrument when climbing the tower, consequently, the staff is in the time of climbing the tower except that having higher requirements to self physical stamina, still have certain requirement to the posture of climbing the tower, in actual work, most staff do not study the comfort level of the posture of climbing the tower when climbing the tower, climb all the time by brute force, cause the large amount of consumption of the physical stamina of climbing in-process, if the comfort level of staff's posture of climbing the tower is low, can further increase staff's tired sense, reduce staff's work efficiency, the operating condition when influencing the staff and overhauing, probably cause great work mistake when serious, threaten self life safety.
In conclusion, how to evaluate the comfort level of the tower climbing posture of the human body so as to further guide the climbing posture of the power overhaul worker during tower climbing has very important significance.
Disclosure of Invention
In order to overcome the defects that physical energy is consumed in a large amount and the working state of a worker is affected because the worker does not make a question of the comfort level of the tower climbing posture when climbing an electric power tower, the invention provides a human body tower climbing posture assessment method which guides the climbing posture of an electric power overhaul worker when climbing a tower, saves physical energy and improves the working efficiency of the worker.
The present invention aims to solve the above technical problem at least to some extent.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
a comfort evaluation method for a tower-climbing posture of a human body comprises the following steps:
s1, respectively sticking testers on target parts of bodies of tower climbing workers participating in evaluation;
s2, starting climbing a tower by a tower climbing worker according to a tower climbing posture specified by a climbing standard;
s3, capturing motion trail data of the tester on the target part by using the motion capture camera, and storing the motion trail data by using the three-dimensional motion capture controller;
s4, analyzing and processing the motion trajectory data by using a three-dimensional motion capture analyzer;
s5, driving a human body simulation module through the analyzed and processed motion trail data, wherein the human body simulation module analyzes the activation degree of the muscle of the target part corresponding to the motion trail data;
s6, the human body simulation module judges whether the activation degree of the muscles of the target part of the tower climbing staff is greater than a standard threshold h, if so, the step S7 is executed; otherwise, discarding the muscle records which do not meet the standard threshold h;
s7, pasting a patch electrode on the muscle of the target part meeting the standard threshold h, and collecting the electromyographic signal EMG of the muscle of the target part by using a physiological recordere0i；
S8, calibrating muscles of the target part by using a maximum voluntary contraction myoelectric test method, and acquiring myoelectric signals EMG (electro-magnetic stimulation) of the muscles of the target part in a maximum voluntary contraction state by using a physiological recordere1i；
S9, calculating the muscle activation Act of the muscle at the target partiAnd i represents the total number of muscles of all target sites;
s10, according to the muscle activation degree Act of the muscle of the target partiAnd establishing a human body tower climbing posture evaluation index by using an analytic hierarchy process.
Preferably, the target site of step S1 includes: a torso target, an upper limb target, and a lower limb target. The body of the tower climbing worker is divided into different target parts, so that the evaluation index of the tower climbing posture of the worker can be conveniently calculated.
Preferably, the time interval between two adjacent climbing posture climbing switches in step S2 is 3min to 5min, so as to avoid fatigue feeling of the climbing staff participating in the evaluation from affecting the climbing action.
Preferably, the three-dimensional motion capture controller derives the motion trajectory data in the format of C3D and transmits the data to the three-dimensional motion capture analyzer for analysis.
Preferably, the tester is an infrared reflective ball, and the human body simulation module in step S5 is an Anybody human body simulation module. The infrared reflective balls are made of materials with high light reflection rate, and the motion capture camera determines the coordinates of each reflective ball, so that the motion track data of each infrared reflective ball driven by tower climbing staff is determined.
Preferably, the muscle activation Act of the muscle at the target siteiThe calculation steps are as follows:
step 1: EMG for electromyographic signals of muscles of a target part meeting a standard threshold he0iCarrying out analysis treatment, wherein the treatment process comprises the following steps:
Figure BDA0002304890030000021
wherein RMSe0iEMG representing electromyographic signals of muscles of a target sitee0iA standard value for one sampling period; t is the sampling period, EMGe0iElectromyographic signals of muscles of the target part;
step 2: EMG for electromyographic signals of muscles of a target part in a maximum autonomous contraction statee1iCarrying out analysis treatment, wherein the treatment process comprises the following steps:
Figure BDA0002304890030000031
wherein RMSe1iIndicating the eye in the maximum voluntary contraction stateElectromyographic signals EMG of target site musclese1iA standard value for one sampling period; t is the sampling period, EMGe1iThe myoelectric signal of the muscle of the target part in the maximum autonomous contraction state;
and step 3: obtaining the muscle activation Act of the muscle at the target parti：
Acti＝RMSe0i/RMSe1i
Wherein, ActiI represents the total number of muscles of all target parts for the muscle activation degrees of the muscles of the target parts on different target parts; RMSe0iEMG representing electromyographic signals of muscles of a target sitee0iA standard value for one sampling period; RMSe1iEMG (electromyography) representing electromyographic signals of muscles of a target part in a maximum autonomic contraction statee1iStandard value over one sampling period.
Preferably, the process of using the analytic hierarchy process of step S10 is as follows:
s1001: setting a trunk target A, an upper limb target B and a lower limb target C which are target parts of the body of a tower-climbing worker as first-level targets, and setting a back muscle a1, a chest muscle a2 and an abdomen muscle a3 of the body of the tower-climbing worker as second-level sub-targets of the trunk target A; setting shoulder muscle B1, hind arm muscle B2, forearm muscle B3 and hand muscle B4 of the tower worker's body as second level sub-goals of torso goal B; setting the hip muscle C1, thigh muscle C2, calf muscle C3 and foot muscle C4 of the body of the tower climbing staff as the second level sub-targets of the lower limb target C;
s1002: according to the definition of the comparative importance scale of the known analytic hierarchy process, a judgment matrix As is formed by taking the second level sub-targets under the trunk target A As a row and taking the second level sub-targets under the trunk target A As a column (a)jk)3×3，ajk>0，akj＝1/ajk(ii) a Forming a judgment matrix Bs (B) by taking the second-level sub-target under the upper limb target B as a row and taking the second-level sub-target under the upper limb target B as a columnjk)4×4，bjk>0，bkj＝1/bjk(ii) a With the second level sub-goal under lower limb goal C,the judgment matrix Cs is composed of the second level sub-target under the lower limb target C as column (C)jk)4×4，cjk>0，ckj＝1/cjk；
S1003: calculating the maximum eigenvalue lambda of the judgment matrix AsmaxsAAnd its corresponding feature vector WAs，WAs＝[wa1,wa2,wa3](ii) a Calculating the maximum eigenvalue lambda of the judgment matrix BsmaxsBAnd its corresponding feature vector WBs,WBs＝[wb1,wb2,wb3,wb4](ii) a Calculating the maximum eigenvalue lambda of the judgment matrix CsmaxscAnd its corresponding feature vector Wcs，Wcs＝[wc1,wc2,wc3,wc4]；
S1004: respectively to the maximum eigenvalue lambda of the judgment matrix AsmaxsADetermining the maximum eigenvalue lambda of the matrix BsmaxsBAnd judging the maximum eigenvalue lambda of the matrix CsmaxscCarrying out consistency judgment; if the maximum eigenvalue lambda of the matrix As is judgedmaxsADetermining the maximum eigenvalue lambda of the matrix BsmaxsBAnd judging the maximum eigenvalue lambda of the matrix CsmaxscIf the consistency judgment indexes are all in accordance with the consistency judgment indexes, executing step S1005; otherwise, returning to the step S902 to reconstruct the judgment matrix;
s1005: the feature vector WAsNormalization processing for obtaining weights of muscle activation degrees corresponding to the back muscle a1, the chest muscle a2 and the abdomen muscle a 3; the feature vector WBsNormalization processing is carried out, and weights of muscle activation degrees corresponding to the shoulder muscle b1, the rear arm muscle b2, the forearm muscle b3 and the hand muscle b4 are obtained; the feature vector WcsThe normalization process determines the weights of the muscle activation degrees corresponding to the hip muscle c1, the thigh muscle c2, the calf muscle c3, and the foot muscle c 4.
Preferably, the maximum eigenvalue λ of any one of the judgment matrix As, the judgment matrix Bs and the judgment matrix CsmaxsThe consistency judgment formula that all satisfy is as follows:
Figure BDA0002304890030000041
wherein, CI represents a consistency judgment index; lambda [ alpha ]maxsRepresenting the maximum eigenvalue of any one of the judgment matrix As, the judgment matrix Bs and the judgment matrix Cs; n represents the number of each sub-target of the first-level target, and when the consistency judgment is carried out by the judgment matrix As, n is 3; when the judgment matrix Bs carries out consistency judgment, taking n as 4; when the consistency of the judgment matrix Cs is judged, n is 4;
and verifying the result of the consistency judgment:
Figure BDA0002304890030000042
wherein, RI is an average random consistency index and is only related to n, and when n is 1, RI is 0; when n is 2, RI is 0; when n is 3, RI is 0.58; when n is 4, RI is 0.9; when n is 5, RI is 1.12; when n is 6, RI is 1.24; if CR is equal to 0, judging that the matrix has complete consistency; if CR is less than 0.1, judging that the matrix has better consistency; if CR is more than or equal to 0.1, the matrix is judged to be reconstructed.
Preferably, the maximum eigenvalue λ satisfying the consistency judgmentmaxsThe corresponding feature vector is the weight corresponding to each sub-target, and the feature vector WAsThe formula of the normalization process is:
Figure BDA0002304890030000051
wherein the content of the first and second substances,
Figure BDA0002304890030000052
represents the normalized weight of the p-th sub-target of the decision matrix As, wapRepresenting the weight before normalization of the pth sub-target of the second-level sub-target in the judgment matrix As, wherein p is 1,2 and 3;
the feature vector WBsThe formula of the normalization process is:
Figure BDA0002304890030000053
wherein the content of the first and second substances,
Figure BDA0002304890030000054
represents the normalized weight of the qth sub-target of the decision matrix Bs, wbqRepresenting the weight before the normalization of the qth sub-target of the second-level sub-target in the judgment matrix Bs, wherein q is 1,2,3 and 4;
the feature vector WCsThe formula of the normalization process is:
Figure BDA0002304890030000055
wherein the content of the first and second substances,
Figure BDA0002304890030000056
represents the normalized weight, w, of the r-th sub-target of the decision matrix CscrAnd r is 1,2,3 and 4, and represents the weight before normalization of the mth sub-target of the second-level sub-target in the judgment matrix Cs.
Preferably, the calculation formula of the human body tower-climbing posture comfort level evaluation index is as follows:
Figure BDA0002304890030000057
wherein com represents a human body tower-climbing posture evaluation index, p represents the p-th sub-target of the trunk target A, ActpThe muscle activation degree of the pth sub-target representing the torso target a;
Figure BDA0002304890030000058
representing the weight of the normalized p sub-target of the second level sub-target in the judgment matrix As; q denotes the qth sub-target of the upper limb target, ActqRepresents the muscle activation degree of the qth sub-target of the upper limb target B;
Figure BDA0002304890030000059
representing the weight of the judgment matrix Bs after the q-th sub-target normalization; r denotes the r sub-target of the lower extremity target C, ActrRepresents the muscle activation degree of the r-th sub-target of the lower limb target C;
Figure BDA0002304890030000061
representing the weight of the judgment matrix Cs after the nth sub-target normalization;
and when the value of the human body tower-climbing posture comfort level evaluation index com is smaller than the comfort set value L, the human body tower-climbing posture corresponding to the human body tower-climbing posture comfort level evaluation index com is in accordance with the evaluation requirement. Here, the comfort setting value L also represents, to a certain extent, an activation degree critical value at which muscles of different target parts of the body can generate fatigue under the drive of different tower-climbing postures when the worker steps on the tower, and when the human body tower-climbing posture comfort level evaluation index com is greater than the comfort setting value L, the muscle activation degree representing that the human body tower-climbing posture comfort level evaluation index com corresponds to the tower-climbing posture drive is greater than the muscle activation degree critical value, and the worker can generate high fatigue when the worker steps on the tower using the posture for a long time; on the contrary, when the evaluation index com of the posture comfort degree of the human body climbing the tower is smaller than the comfort setting value L, the muscle activation degree driven by the evaluation index com of the posture comfort degree of the human body climbing the tower corresponding to the posture of the human body is smaller than the muscle activation degree critical value, and the staff can meet the comfort requirement when climbing the tower by using the posture for a long time.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the invention provides an evaluation method of human body tower-climbing posture, capturing the tower-climbing posture motion trail of tower-climbing workers participating in evaluation by a motion capture camera, storing irreversible tower-climbing motions and observing repeatedly, and guiding and analyzing the tower-climbing posture of the existing workers by repeated observation and analysis; the method also analyzes the muscle activation degree of muscles of target parts on different target parts in the tower climbing process, and utilizes an analytic hierarchy process to construct a human body tower climbing posture comfort degree assessment index on the basis of the muscle activation degree and the myoelectric signals of the muscles of the target parts, so that the comfort degree of a tower climbing posture adopted by a tower climbing worker in the tower climbing process is assessed, the climbing posture of the power overhaul worker in the tower climbing process is further guided, and the fatigue feeling of the worker climbing the tower with brute force is reduced.
Drawings
Fig. 1 is a flowchart of a human body tower climbing posture evaluation method according to the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
Fig. 1 is a schematic flow chart of a human body tower-climbing posture assessment method provided by the present invention, which includes the following steps:
s1, respectively sticking testers on target parts of bodies of tower climbing workers participating in evaluation; in this embodiment, the adopted tester is an infrared reflective ball, and since the infrared reflective ball is made of a material with high reflective rate, the motion capture camera determines the coordinates of each reflective ball, so as to determine the motion trajectory data of each infrared reflective ball driven by the tower-climbing staff.
S2, starting climbing a tower by a tower climbing worker according to a tower climbing posture specified by a climbing standard; the time interval of climbing and switching the postures of two adjacent tower climbing is 3-5 min, so that the influence of fatigue on the tower climbing actions caused by the tower climbing workers participating in evaluation is avoided.
S3, capturing motion trail data of the tester on the target part by using the motion capture camera, and storing the motion trail data by using the three-dimensional motion capture controller; the three-dimensional motion capture controller leads out the motion trail data in a format of C3D and transmits the motion trail data to a three-dimensional motion capture analyzer for analysis processing;
s4, analyzing and processing the motion trajectory data by using a three-dimensional motion capture analyzer;
s5, driving a human body simulation module through the analyzed and processed motion trail data, wherein the human body simulation module analyzes the activation degrees of muscles of different target parts corresponding to the motion trail data;
s6, the human body simulation module judges whether the activation degree of the muscles of the target part of the tower climbing staff is greater than a standard threshold h, if so, the step S7 is executed; otherwise, discarding the muscle records which do not meet the standard threshold h;
s7, pasting a patch electrode on the muscle of the target part meeting the standard threshold h, and collecting the electromyographic signal EMG of the muscle of the target part by using a physiological recordere0i；
S8, calibrating muscles of the target part by using a maximum voluntary contraction myoelectric test method, and acquiring myoelectric signals EMG (electro-magnetic stimulation) of the muscles of the target part in a maximum voluntary contraction state by using a physiological recordere1i；
S9, calculating the muscle activation Act of the muscle at the target partiAnd i represents the total number of muscles of all target parts:
step 1: EMG for muscle electromyographic signals of target parts meeting standard threshold he0iCarrying out analysis treatment, wherein the treatment process comprises the following steps:
Figure BDA0002304890030000071
wherein RMSe0iEMG representing electromyographic signals of muscles of a target sitee0iA standard value for one sampling period; t is the sampling period, EMGe0iElectromyographic signals of muscles of the target part;
step 2: EMG for electromyographic signals of muscles of a target part in a maximum autonomous contraction statee1iCarrying out analysis treatment, wherein the treatment process comprises the following steps:
Figure BDA0002304890030000081
wherein RMSe1iEMG (electromyography) representing electromyographic signals of muscles of a target part in a maximum autonomic contraction statee1iA standard value for one sampling period; t is the sampling period, EMGe1iThe myoelectric signal of the muscle of the target part in the maximum autonomous contraction state;
and step 3: obtaining the muscle activation Act of the muscle at the target parti：
Acti＝RMSe0i/RMSe1i
Wherein, ActiI represents the total number of muscles of the target part; RMSe0iEMG representing electromyographic signals of muscles of a target sitee0iA standard value for one sampling period; RMSe1iEMG (electromyography) representing electromyographic signals of muscles of a target part in a maximum autonomic contraction statee1iStandard value over one sampling period.
S10, according to the muscle activation Act of the muscle of the target part on the target partiEstablishing a human body tower climbing posture evaluation index by using an analytic hierarchy process;
the procedure using the analytic hierarchy process is as follows:
s1001: setting a trunk target A, an upper limb target B and a lower limb target C which are different target parts of the body of the tower-climbing worker as first-level targets, and setting a back muscle a1, a chest muscle a2 and an abdomen muscle a3 of the body of the tower-climbing worker as second-level sub-targets of the trunk target A; setting shoulder muscle B1, hind arm muscle B2, forearm muscle B3 and hand muscle B4 of the tower worker's body as second level sub-goals of torso goal B; setting the hip muscle C1, thigh muscle C2, calf muscle C3 and foot muscle C4 of the body of the tower climbing staff as the second level sub-targets of the lower limb target C;
s1002: according to the definition of the comparative importance scale of the known analytic hierarchy process, a judgment matrix As is formed by taking the second level sub-targets under the trunk target A As a row and taking the second level sub-targets under the trunk target A As a column (a)jk)3×3，ajk>0，akj＝1/ajk(ii) a Above the limbs under target BThe second level sub-target is a row, and a judgment matrix Bs is formed by taking the second level sub-target under the upper limb target B as a column (B)jk)4×4，bjk>0，bkj＝1/bjk(ii) a The judgment matrix Cs is composed of the second level sub-target under the lower limb target C as the row and the second level sub-target under the lower limb target C as the column (C)jk)4×4，cjk>0，ckj＝1/cjk；
S1003: calculating the maximum eigenvalue lambda of the judgment matrix AsmaxsAAnd its corresponding feature vector WAs，WAs＝[wa1,wa2,wa3](ii) a Calculating the maximum eigenvalue lambda of the judgment matrix BsmaxsBAnd its corresponding feature vector WBs,WBs＝[wb1,wb2,wb3,wb4](ii) a Calculating the maximum eigenvalue lambda of the judgment matrix CsmaxscAnd its corresponding feature vector Wcs，Wcs＝[wc1,wc2,wc3,wc4]；
S1004: respectively to the maximum eigenvalue lambda of the judgment matrix AsmaxsADetermining the maximum eigenvalue lambda of the matrix BsmaxsBAnd judging the maximum eigenvalue lambda of the matrix CsmaxscCarrying out consistency judgment; if the maximum eigenvalue lambda of the matrix As is judgedmaxsADetermining the maximum eigenvalue lambda of the matrix BsmaxsBAnd judging the maximum eigenvalue lambda of the matrix CsmaxscIf the consistency judgment indexes are all in accordance with the consistency judgment indexes, executing step S1005; otherwise, returning to the step S902 to reconstruct the judgment matrix;
s1005: the feature vector WAsNormalization processing for obtaining weights of muscle activation degrees corresponding to the back muscle a1, the chest muscle a2 and the abdomen muscle a 3; the feature vector WBsNormalization processing is carried out, and weights of muscle activation degrees corresponding to the shoulder muscle b1, the rear arm muscle b2, the forearm muscle b3 and the hand muscle b4 are obtained; the feature vector WcsThe normalization process determines the weights of the muscle activation degrees corresponding to the hip muscle c1, the thigh muscle c2, the calf muscle c3, and the foot muscle c 4.
For the maximum eigenvalue lambda of any one of the judgment matrix As, the judgment matrix Bs and the judgment matrix CsmaxsThe consistency judgment formula that all satisfy is as follows:
Figure BDA0002304890030000091
wherein, CI represents a consistency judgment index; lambda [ alpha ]maxsRepresenting the maximum eigenvalue of any one of the judgment matrix As, the judgment matrix Bs and the judgment matrix Cs; n represents the number of each sub-target of the first-level target, and when the consistency judgment is carried out by the judgment matrix As, n is 3; when the judgment matrix Bs carries out consistency judgment, taking n as 4; when the consistency of the judgment matrix Cs is judged, n is 4;
and verifying the result of the consistency judgment:
Figure BDA0002304890030000092
wherein, RI is an average random consistency index and is only related to n, and when n is 1, RI is 0; when n is 2, RI is 0; when n is 3, RI is 0.58; when n is 4, RI is 0.9; when n is 5, RI is 1.12; when n is 6, RI is 1.24; if CR is equal to 0, judging that the matrix has complete consistency; if CR is less than 0.1, judging that the matrix has better consistency; if CR is more than or equal to 0.1, the matrix is judged to be reconstructed.
Maximum eigenvalue lambda satisfying consistency judgmentmaxsThe corresponding feature vector is the weight corresponding to each sub-target, and the feature vector WAsThe formula of the normalization process is:
Figure BDA0002304890030000101
wherein the content of the first and second substances,
Figure BDA0002304890030000102
represents the normalized weight of the p-th sub-target of the decision matrix As, wapRepresents the first in the judgment matrix AsThe weight before the normalization of the p-th sub-target of the two-level sub-targets, wherein p is 1,2 and 3;
feature vector WBsThe formula of the normalization process is:
Figure BDA0002304890030000103
wherein the content of the first and second substances,
Figure BDA0002304890030000104
represents the normalized weight of the qth sub-target of the decision matrix Bs, wbqRepresenting the weight before the normalization of the qth sub-target of the second-level sub-target in the judgment matrix Bs, wherein q is 1,2,3 and 4;
feature vector WCsThe formula of the normalization process is:
Figure BDA0002304890030000105
wherein the content of the first and second substances,
Figure BDA0002304890030000106
represents the normalized weight, w, of the r-th sub-target of the decision matrix CscrAnd r is 1,2,3 and 4, and represents the weight before normalization of the mth sub-target of the second-level sub-target in the judgment matrix Cs.
The calculation formula of the human body tower-climbing posture comfort evaluation index is as follows:
Figure BDA0002304890030000107
wherein com represents a human body tower-climbing posture evaluation index, p represents the p-th sub-target of the trunk target A, ActpThe muscle activation degree of the pth sub-target representing the torso target a;
Figure BDA0002304890030000108
representing the weight of the judgment matrix As after the normalization of the pth sub-target of the second-level sub-target;q denotes the qth sub-target of the upper limb target, ActqRepresents the muscle activation degree of the qth sub-target of the upper limb target B;
Figure BDA0002304890030000109
representing the weight of the judgment matrix Bs after the q-th sub-target normalization; r denotes the r sub-target of the lower extremity target C, ActrRepresents the muscle activation degree of the r-th sub-target of the lower limb target C;
Figure BDA0002304890030000111
representing the weight of the judgment matrix Cs after the nth sub-target normalization; and when the value of the human body tower-climbing posture comfort level evaluation index com is smaller than the comfort set value L, the human body tower-climbing posture corresponding to the human body tower-climbing posture comfort level evaluation index com is in accordance with the evaluation requirement. Here, the comfort setting value L also represents, to a certain extent, an activation degree critical value at which muscles of different target parts of the body can generate fatigue under the drive of different tower-climbing postures when the worker steps on the tower, and when the human body tower-climbing posture comfort level evaluation index com is greater than the comfort setting value L, the muscle activation degree representing that the human body tower-climbing posture comfort level evaluation index com corresponds to the tower-climbing posture drive is greater than the muscle activation degree critical value, and the worker can generate high fatigue when the worker steps on the tower using the posture for a long time; on the contrary, when the evaluation index com of the posture comfort degree of the human body climbing the tower is smaller than the comfort setting value L, the muscle activation degree driven by the evaluation index com of the posture comfort degree of the human body climbing the tower corresponding to the posture of the human body is smaller than the muscle activation degree critical value, and the staff can meet the comfort requirement when climbing the tower by using the posture for a long time.
The same or similar reference numerals correspond to the same or similar parts;
the positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (8)

1. A comfort evaluation method for a tower-climbing posture of a human body is characterized by comprising the following steps:
s1, respectively sticking testers on target parts of bodies of tower climbing workers participating in evaluation;
s2, starting climbing a tower by a tower climbing worker according to a tower climbing posture specified by a climbing standard;
s3, capturing motion trail data of the tester on the target part by using the motion capture camera, and storing the motion trail data by using the three-dimensional motion capture controller;
s4, analyzing and processing the motion trajectory data by using a three-dimensional motion capture analyzer; the three-dimensional motion capture controller leads out the motion trail data in a format of C3D and transmits the motion trail data to a three-dimensional motion capture analyzer for analysis processing;
s5, driving a human body simulation module through the analyzed and processed motion trail data, wherein the human body simulation module analyzes the activation degree of the muscle of the target part corresponding to the motion trail data;
s6, the human body simulation module judges whether the activation degree of the muscles of the target part of the tower climbing staff is greater than a standard threshold h, if so, the step S7 is executed; otherwise, discarding the muscle records which do not meet the standard threshold h;
s7, pasting a patch electrode on the muscle of the target part meeting the standard threshold h, and collecting the electromyographic signal EMG of the muscle of the target part by using a physiological recordere0i；
S8, calibrating muscles of the target part by using a maximum voluntary contraction myoelectric test method, and acquiring myoelectric signals EMG (electro-magnetic stimulation) of the muscles of the target part in a maximum voluntary contraction state by using a physiological recordere1i(ii) a The muscle activation Act of the muscle at the target site in step S8iThe calculation steps are as follows:
step 1: to pairElectromyographic signals EMG of target region muscles meeting standard threshold he0iCarrying out analysis treatment, wherein the treatment process comprises the following steps:
Figure FDA0002678002520000011
wherein RMSe0iEMG representing electromyographic signals of muscles of a target sitee0iA standard value for one sampling period; t is the sampling period, EMGe0iElectromyographic signals of muscles of the target part;
step 2: EMG for electromyographic signals of muscles of a target part in a maximum autonomous contraction statee1iCarrying out analysis treatment, wherein the treatment process comprises the following steps:
Figure FDA0002678002520000012
wherein RMSe1iEMG (electromyography) representing electromyographic signals of muscles of a target part in a maximum autonomic contraction statee1iA standard value for one sampling period; t is the sampling period, EMGe1iThe myoelectric signal of the muscle of the target part in the maximum autonomous contraction state;
and step 3: obtaining the muscle activation Act of the muscle at the target parti：
Acti＝RMSe0i/RMSe1i
Wherein, ActiI represents the total number of muscles of the target part; RMSe0iEMG representing electromyographic signals of muscles of a target sitee0iA standard value for one sampling period; RMSe1iEMG (electromyography) representing electromyographic signals of muscles of a target part in a maximum autonomic contraction statee1iA standard value for one sampling period;
s9, calculating the muscle activation Act of the muscle at the target partiAnd i represents the total number of muscles of all target sites;
s10, according to the muscle activation degree Act of the muscle of the target partiBy using the analytic hierarchy process,and establishing a human body tower climbing posture evaluation index.
2. The method for assessing the comfort level of a human body in a tower-climbing posture according to claim 1, wherein the target part in step S1 comprises: a torso target, an upper limb target, and a lower limb target.
3. The method for assessing the comfort level of a human body in a tower-climbing posture according to claim 1, wherein the time interval between two adjacent climbing posture switching times is 3min to 5 min.
4. The method for assessing the comfort of a human body boarding posture according to claim 1, wherein the tester is an infrared reflective ball, and the human body simulation module of step S5 is an Anybody human body simulation module.
5. The method for assessing the comfort level of a human body in a tower-climbing posture according to claim 1, wherein the step S10 is performed by using an analytic hierarchy process as follows:
s1001: setting a trunk target A, an upper limb target B and a lower limb target C which are different target parts of the body of the tower-climbing worker as first-level targets, and setting a back muscle a1, a chest muscle a2 and an abdomen muscle a3 of the body of the tower-climbing worker as second-level sub-targets of the trunk target A; setting shoulder muscle B1, hind arm muscle B2, forearm muscle B3 and hand muscle B4 of the tower worker's body as second level sub-goals of torso goal B; setting the hip muscle C1, thigh muscle C2, calf muscle C3 and foot muscle C4 of the body of the tower climbing staff as the second level sub-targets of the lower limb target C;
s1002: according to the definition of the comparative importance scale of the known analytic hierarchy process, the second level sub-targets under the torso target A are used As the row, and the second level sub-targets under the torso target A are used As the column to form a judgment matrix As ═ a (a)jk)3×3，ajk＞0，akj＝1/ajk(ii) a The second level sub-goal under the upper limb goal B is taken as a line, and the second level sub-goal under the upper limb goal B is taken as a lineJudging the matrix Bs as (b)jk)4×4，bjk＞0，bkj＝1/bjk(ii) a The judgment matrix Cs is composed of the second level sub-target under the lower limb target C as the row and the second level sub-target under the lower limb target C as the column (C)jk)4×4，cjk＞0，ckj＝1/cjk；
S1003: calculating the maximum eigenvalue lambda of the judgment matrix AsmaxsAAnd its corresponding feature vector WAs，WAs＝[wa1，wa2，wa3](ii) a Calculating the maximum eigenvalue lambda of the judgment matrix BsmaxsBAnd its corresponding feature vector WBs，WBs＝[wb1，wb2，wb3，wb4](ii) a Calculating the maximum eigenvalue lambda of the judgment matrix CsmaxscAnd its corresponding feature vector Wes，Wcs＝[wc1，wc2，wc3，wc4]；
S1004: respectively to the maximum eigenvalue lambda of the judgment matrix AsmaxsADetermining the maximum eigenvalue lambda of the matrix BsmaxsBAnd judging the maximum eigenvalue lambda of the matrix CsmaxscCarrying out consistency judgment; if the maximum eigenvalue lambda of the matrix As is judgedmaxsADetermining the maximum eigenvalue lambda of the matrix BsmaxsBAnd judging the maximum eigenvalue lambda of the matrix CsmaxscIf the consistency determination index is met, step S1005 is executed; otherwise, returning to the step S902 to reconstruct the judgment matrix;
s1005: the feature vector WAsNormalization processing for obtaining weights of muscle activation degrees corresponding to the back muscle a1, the chest muscle a2 and the abdomen muscle a 3; the feature vector WBsNormalization processing is carried out, and weights of muscle activation degrees corresponding to the shoulder muscle b1, the rear arm muscle b2, the forearm muscle b3 and the hand muscle b4 are obtained; the feature vector WcsThe normalization process determines the weights of the muscle activation degrees corresponding to the hip muscle c1, the thigh muscle c2, the calf muscle c3, and the foot muscle c 4.
6. The human body of claim 5 in a tower-climbing postureThe comfort evaluation method is characterized in that the maximum eigenvalue lambda of any one of the judgment matrix As, the judgment matrix Bs and the judgment matrix CsmaxsThe consistency judgment formula that all satisfy is as follows:
Figure FDA0002678002520000031
wherein, CI represents a consistency judgment index; lambda [ alpha ]maxsRepresenting the maximum eigenvalue of any one of the judgment matrix As, the judgment matrix Bs and the judgment matrix Cs; n represents the number of each sub-target of the first-level target, and when the consistency judgment is carried out by the judgment matrix As, n is 3; when the judgment matrix Bs carries out consistency judgment, taking n as 4; when the consistency of the judgment matrix Cs is judged, n is 4;
and verifying the result of the consistency judgment:
Figure FDA0002678002520000032
wherein, RI is an average random consistency index and is only related to n, and when n is 1, RI is 0; when n is 2, RI is 0; when n is 3, RI is 0.58; when n is 4, RI is 0.9; when n is 5, RI is 1.12; when n is 6, RI is 1.24; if CR is equal to 0, judging that the matrix has complete consistency; if CR is less than 0.1, judging that the matrix has better consistency; if CR is more than or equal to 0.1, the matrix is judged to be reconstructed.
7. The method for assessing the comfort level of a human body boarding posture according to claim 6, characterized in that the maximum eigenvalue λ satisfying the consistency judgmentmaxsThe corresponding feature vector is the weight corresponding to each sub-target, and the feature vector WAsThe formula of the normalization process is:
Figure FDA0002678002520000041
wherein the content of the first and second substances,
Figure FDA0002678002520000042
represents the normalized weight of the p-th sub-target of the decision matrix As, wapRepresenting the weight of the p-th sub-target of the judgment matrix As before normalization, wherein p is 1,2, 3;
the feature vector WBsThe formula of the normalization process is:
Figure FDA0002678002520000043
wherein the content of the first and second substances,
Figure FDA0002678002520000044
represents the normalized weight of the qth sub-target of the decision matrix Bs, wbqRepresenting the weight before the q-th sub-target normalization of the judgment matrix Bs, q is 1,2,3, 4;
the feature vector WCsThe formula of the normalization process is:
Figure FDA0002678002520000045
wherein the content of the first and second substances,
Figure FDA0002678002520000046
represents the normalized weight, w, of the r-th sub-target of the decision matrix CscrThe weight before the nth sub-target of the decision matrix Cs is normalized is represented, and r is 1,2,3, 4.
8. The method for assessing the comfort level of a human body tower-climbing posture according to claim 7, wherein the human body tower-climbing posture comfort level assessment index is calculated by the following formula:
Figure FDA0002678002520000047
wherein com represents a human body tower-climbing posture evaluation index, p represents the p-th sub-target of the trunk target A, ActpThe muscle activation degree of the pth sub-target representing the torso target a;
Figure FDA0002678002520000051
representing the normalized weight of the p sub-target of the judgment matrix As; q denotes the qth sub-target of the upper limb target, ActqRepresents the muscle activation degree of the qth sub-target of the upper limb target B;
Figure FDA0002678002520000052
representing the weight of the judgment matrix Bs after the q-th sub-target normalization; r denotes the r sub-target of the lower extremity target C, ActrRepresents the muscle activation degree of the r-th sub-target of the lower limb target C;
Figure FDA0002678002520000053
representing the weight of the judgment matrix Cs after the nth sub-target normalization;
and when the value of the human body tower-climbing posture comfort level evaluation index com is smaller than the comfort set value L, the human body tower-climbing posture corresponding to the human body tower-climbing posture comfort level evaluation index com is in accordance with the evaluation requirement.
CN201911235962.4A 2019-12-05 2019-12-05 Comfort evaluation method for human body tower-climbing posture Active CN110772262B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911235962.4A CN110772262B (en) 2019-12-05 2019-12-05 Comfort evaluation method for human body tower-climbing posture

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911235962.4A CN110772262B (en) 2019-12-05 2019-12-05 Comfort evaluation method for human body tower-climbing posture

Publications (2)

Publication Number Publication Date
CN110772262A CN110772262A (en) 2020-02-11
CN110772262B true CN110772262B (en) 2020-12-29

Family

ID=69393898

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911235962.4A Active CN110772262B (en) 2019-12-05 2019-12-05 Comfort evaluation method for human body tower-climbing posture

Country Status (1)

Country Link
CN (1) CN110772262B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111898205B (en) * 2020-07-29 2022-09-13 吉林大学 RBF neural network-based human-machine performance perception evaluation prediction method and system

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8444564B2 (en) * 2009-02-02 2013-05-21 Jointvue, Llc Noninvasive diagnostic system
CN104924905B (en) * 2015-07-14 2017-12-12 吉林大学 A kind of automobile pedal position adjusting means for considering driver's leg muscle comfortableness
CN105054927B (en) * 2015-07-16 2017-08-15 西安交通大学 The biological quantitative estimation method for degree of being actively engaged in a kind of lower limb rehabilitation system
CN105615890B (en) * 2015-12-24 2018-08-10 西安交通大学 Human body lower limbs walking joint angles myoelectricity continuous decoding method
US20170312576A1 (en) * 2016-04-02 2017-11-02 Senthil Natarajan Wearable Physiological Sensor System for Training and Therapeutic Purposes
CN108742609A (en) * 2018-04-03 2018-11-06 吉林大学 A kind of driver's lane-change Comfort Evaluation method based on myoelectricity and manipulation information

Also Published As

Publication number Publication date
CN110772262A (en) 2020-02-11

Similar Documents

Publication Publication Date Title
CN108785997B (en) Compliance control method of lower limb rehabilitation robot based on variable admittance
CN107397649B (en) Upper limb exoskeleton movement intention identification method based on radial basis function neural network
CN109243572B (en) Accurate motion evaluation and rehabilitation training system
CN105615890B (en) Human body lower limbs walking joint angles myoelectricity continuous decoding method
CN104107134B (en) Upper limbs training method and system based on EMG feedback
Sparto et al. The effect of fatigue on multijoint kinematics, coordination, and postural stability during a repetitive lifting test
CN104382595A (en) Upper limb rehabilitation system and method based on myoelectric signal and virtual reality interaction technology
CN104207793B (en) A kind of grip function assessment and training system
CN112057040B (en) Upper limb movement function rehabilitation evaluation method
CN103417218A (en) Parameter acquisition evaluating system and method of upper limb movement
CN110974212A (en) Electrocardio and myoelectric characteristic fused rehabilitation training motion state monitoring method and system
CN110339024A (en) Lower limb exoskeleton robot and its real-time gait switching method and storage device
CN114822761A (en) Wrist rehabilitation training system based on muscle cooperation and variable stiffness impedance control
CN114897012A (en) Intelligent prosthetic arm control method based on vital machine interface
CN110772262B (en) Comfort evaluation method for human body tower-climbing posture
CN109758336A (en) A kind of control method carrying out active rehabilitation care using patient's Ipsilateral sEMG
CN110400618B (en) Three-dimensional gait generation method based on human motion structure characteristics
Triolo et al. The theoretical development of a multichannel time-series myoprocessor for simultaneous limb function detection and muscle force estimation
CN116312951B (en) Exercise function assessment method and system based on multi-modal coupling analysis
Li et al. Detection of muscle fatigue by fusion of agonist and synergistic muscle semg signals
CN209203256U (en) View-based access control model-EMG biofeedback muscle damage rehabilitation training system
CN114748079A (en) Wearable myoelectric method for online evaluation of muscle movement fatigue degree
CN114548165A (en) Electromyographic mode classification method capable of crossing users
CN113782148A (en) Upper limb load joint limb intelligent feedback training system
Li et al. Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant