CN1286962A - Real-time body gait image detecting method - Google Patents
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- CN1286962A CN1286962A CN 00129556 CN00129556A CN1286962A CN 1286962 A CN1286962 A CN 1286962A CN 00129556 CN00129556 CN 00129556 CN 00129556 A CN00129556 A CN 00129556A CN 1286962 A CN1286962 A CN 1286962A
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- 230000005021 gait Effects 0.000 title claims abstract description 28
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- 238000005728 strengthening Methods 0.000 claims description 3
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Abstract
After some mark point is adhered to tested joint of the testee, the image is first taken while the testee walks and then treated through filtering, mapping, non-linear transformation, blurring and enhancement. Mark point is found out and its two-dimensional center coordinates are calculated to obtain the gait image. The present invention makes it possible to record body's gait motion path for various statistics, so as to perform research on the motion laws and control mechanism of hip joint, knee joint and ankle joint, or to estimate hands motion function and restoring degree clinically.
Description
The present invention relates to a kind of body gait image real-time detection method, belong to biomedical engineering field.
The body gait detection method of clinical practice at present mainly contains two kinds based on electronics angle measurement instrument and PS.The body gait image detection system is because the additament of placing at human body is few, and less to the influence of walking naturally of human body, accuracy of detection is higher thereby adopted maximum.The external body gait image detection system of producing mainly contains with infrarede emitting diode as active flag point (the CODA series of products of making as Charnwood Dynamics company) with use the highstrung reflectorized material of infrared ray as passive index point (as the Vicon series of products of Oxford Metrics company manufacturing) two big classes, it is characterized in that shooting with video-corder the infrared ray that the joint index point sends or reflects, this human motion image detecting method based on infrared principles is not only had relatively high expectations to testing background, and device costs an arm and a leg.Although domestic have a R﹠D work of having carried out the body gait image detection system that is intended to reduce cost, as the infrared light spot system of development (referring to people's such as Ding Haishu paper " two and three dimensions with PSD pick off realization multiple spot movement locus detects in real time ", Tsing-Hua University's journal, 1992; 32 (4)) and traditional body gait analytical system based on common shooting principle.Although the method that traditional body movement detection method based on common shooting principle has overcome based on the infrared photography principle requires harsh and the high deficiency of price to detection background, but this method adopts and earlier image is recorded to video-tape, and then gather image with the digitized frame by frame mode of image-capture card, use with operation loaded down with trivial details, need nonshared control unit to coordinate the synchronous acquisition of image and Li Tai and electromyographic signal, synchronization accuracy is relatively poor, and data management inconvenience can not be carried out online detection.
The objective of the invention is to propose a kind of body gait image real-time detection method.On the basis of research tradition type for many years based on the body gait image detection method of common shooting principle and device, propose a kind of dynamic image that utilizes and obtained real-time body gait image detecting method with recognition technology, to overcome the deficiency that existing method exists at aspects such as performance and prices, for China hospital provide a kind of with low cost, to testing background body gait image detection technique less demanding, simple to operation, promote modern human body gait analysis technology popularizing in China.
A kind of body gait image real-time detection method that the present invention proposes is characterized in that this method may further comprise the steps:
(1) pastes index point in measured joint to be measured, the measured is walked on pavement;
(2) absorb the walking process image of people on pavement, and image is collected computer in real time;
(3) image is carried out filtering with median filtering method, filtered image is mapped as fuzzy matrix, fuzzy matrix is made nonlinear transformation, blur enhancing, then the image after strengthening is done inverse mapping, obtain fuzzy enhanced image;
(4) above-mentioned image is scanned, search out all connected domains according to ganmma controller; Search the sealing connected domain and obtain the outline full-size of sealing connected domain; Sealing connected domain outer profile size screened obtain and the consistent index point of former stickup index point size range;
(5) to finding the 1st and the 2nd width of cloth motor process image behind the index point, progressively extrapolate from beginning flag point and to search action back and put corresponding index point with original logo, all images later to the 3rd width of cloth are put corresponding index point with original logo behind the single order extrapolation location lookup subsequent action of the index point that pro-two width of cloth are determined;
(6) calculate the two-dimentional centre coordinate of index point according to the shape of the above-mentioned index point of searching;
(7) can obtain body gait motion rod figure by the dissection above-mentioned centre coordinate that sticks on the index point on joint or the end that is linked in sequence; Connect the displacement movement curve that same index point coordinate in the different images just obtains the joint by the sampling order; Two straight lines according to per 3 somes decision among the motion rod figure can calculate two angles that straight line is folded, i.e. the arthrogryposis angle.According to displacement and angular displacement, utilize differential method can obtain the speed of its motion and the Changing Pattern of angular velocity, acceleration and angular acceleration.
Utilize method that the present invention proposes detection record body gait movement locus in real time, and it is carried out various statistics, with research hip, knee joint and ankle joint the characteristics of motion and control mechanism, or on clinical medicine quantitative assessment human hand movement function and rehabilitation degree thereof.The method that the present invention proposes is owing to arrive computer with image acquisition in real time, therefore realized measured's information and detection and analysis result are managed automatically, and this method is low to the detection background requirement, and the medical institutions and the related scientific research mechanism that are highly suitable for developing country use.
Description of drawings:
The motion rod figure of the one-sided lower limb of measured in gait cycle of Fig. 1.
The one-sided hip joint angular displacement of measured curve in gait cycle of Fig. 2
Measured's unilateral knee joint angle displacement curve in gait cycle of Fig. 3
Measured's unilateral ankle angular displacement curve in gait cycle of Fig. 4.
Below in conjunction with accompanying drawing, introduce content of the present invention in detail:
(1) waist of locating about measured's toe, ankle joint, knee joint, hip joint and hip joint top 10cm is pasted black and white annulus index point.Index point sticks on the sagittal plane outside, and the index point of ankle joint, knee joint, hip joint and waist is when the people stands point-blank (sagittal plane).And the measured is walked on pavement;
(2) absorb the walking process image of people on pavement, and image is collected computer in real time;
(3) image is carried out filtering with median filtering method, filtered image is mapped as fuzzy matrix, fuzzy matrix is made nonlinear transformation, blur enhancing, then the image after strengthening is done inverse mapping, obtain fuzzy enhanced image.Its concrete enhancing process is as follows:
With the image representation of width of cloth L gray level M * N is X=[l
Mn], m=1,2 ... M, n=1,2 ... N (l wherein
MmBe visual pixel (m, gray level n)) that corresponding fuzzy matrix is
N=1,2 ... N (μ wherein
MnBe l
MnDegree of membership with respect to certain particular gray level).Getting membership function is:
L wherein
MnFor fuzzy strengthen the back pixel (m, gray level n), then
Can finish the fuzzy enhancing of image by (1), (2) two formulas.In the specific implementation of algorithm, for reducing amount of calculation, it is as follows in advance formula (2) to be made form Tablel, Table2:
Table1［i］=(int)(i×i/l
c+0.5) i=0、1、2、…l
c
Table2[i]=(int)(L-1-(L-1-i)
2/(L-1-l
c)+0.5)i=l
c+1、l
c+2、…L-1
(4) search index point:
Pointwise scans image and makes comparisons with threshold value, if the continuity point that satisfies threshold condition is arranged, then write down its parameter line (row number), row (initial point range number) and long (continuity point number), such continuity point is exactly " bar ", and by following 3 structure of transvers plate connected domains.
Template 1: if certain " bar " do not link to each other with any existing territory, think that then this " bar " belongs to neofield, and set up a neofield and deposit this " bar " parameter (row, column, length) in.
Template 2:, think that then this " bar " belongs to coupled territory, and should " bar " add its continuous territory if certain " bar " only links to each other with an existing territory.
Template 3: if certain " bar " links to each other with a plurality of existing territories, think that then these territories are a neofield, they are merged into a territory, wherein each " bar " recombinated by the row, column order and should be discharged each original territory simultaneously by " bar " adding neofield.
Each " bar " of scanning and above-mentioned 3 templates are compared, find the template and of conforming to, can realize the automatic detection of arbitrary shape connected domain by template operation.Owing to remain each " bar " in the whole process by the row, column sequence arrangement in the territory in, when therefore searching, need make comparisons with " bar " of each territory last column as template, amount of calculation is very little.In the specific implementation of algorithm, be further to reduce amount of calculation and to save internal memory, the definition of " bar " has been added stricter restriction, have only those length to surpass MIN continuity point and just be considered to " bar ".When such qualification can avoid image " pit " to occur, identify the connected domain of too much null(NUL).As long as connected domain contour dimension and known markers point gabarit size coupling think that promptly this connected domain is an index point.
(5) near tracking mark point:, beginning flag point, search index point to the 1st and 2 images; To the later image of the 3rd width of cloth figure, search index point near the outer push position of the single order of index point among pro-two width of cloth figure, find out the same index point in the different images, promptly carry out index point and follow the tracks of.
(6) calculate the geometric center coordinate of index point according to the shape of index point;
(7) can obtain body gait motion rod figure shown in Figure 1 by the be linked in sequence coordinate of the toe that sticks on the measured, ankle joint, knee joint, hip joint and hip joint top waist index point of dissection.Connect the displacement movement curve that certain index point coordinate just obtains the joint by the sampling order; Two straight lines according to per 3 somes decision adjacent among the motion rod figure, can calculate two angles that straight line is folded, for example can calculate the angle of hip joint, connect the hip joint angle curve that can obtain as shown in Figure 2 by the sampling time order according to the index point coordinate of knee joint, hip joint and hip joint top waist; Index point coordinate according to ankle joint, knee joint and hip joint can calculate kneed angle, connects the knee joint angle curve that can obtain as shown in Figure 3 by the sampling time order; Can calculate the angle of ankle joint according to toe, ankle joint and kneed index point coordinate, connect the ankle joint angle curve that can obtain as shown in Figure 4 by the sampling time order.According to displacement and angular displacement, utilize differential method can obtain the speed of its motion and the Changing Pattern of angular velocity, acceleration and angular acceleration.
(8) aforementioned calculation result and curve are carried out statistical disposition, in the time of can obtaining human body walking, the characteristics of motion in each joint.By various computing result and the curve that obtains carried out various combinative analysiss, adjuster's walking function quantitatively.For example, can observe ankle joint, knee joint, the hip joint characteristics of motion spatially in measured's gait processes by motion rod figure; Can observe ankle joint, knee joint, the hip joint situation of bending and stretching in time in measured's gait processes by the joint angle displacement curve.
The measured is one 26 years old healthy male among the embodiment, height 172cm, body weight 65kg, and the body gait image real-time detection method that utilizes the present invention to propose is handled, and Fig. 1-Fig. 4 is the result who obtains.
Claims (1)
1, a kind of body gait image real-time detection method is characterized in that this method may further comprise the steps:
(1) pastes index point in measured joint to be measured, the measured is walked on pavement;
(2) absorb the walking process image of people on pavement, and image is collected computer in real time;
(3) image is carried out filtering with median filtering method, filtered image is mapped as fuzzy matrix, fuzzy matrix is made nonlinear transformation, blur enhancing, then the image after strengthening is done inverse mapping, obtain fuzzy enhanced image;
(4) above-mentioned image is scanned, search out all connected domains according to ganmma controller; Search the sealing connected domain and obtain the outline full-size of sealing connected domain; Sealing connected domain outer profile size screened obtain and the consistent index point of former stickup index point size range;
(5) to finding the 1st and the 2nd width of cloth motor process image behind the index point, progressively extrapolate from beginning flag point and to search action back and put corresponding index point with original logo, all images later to the 3rd width of cloth are put corresponding index point with original logo behind the single order extrapolation location lookup subsequent action of the index point that pro-two width of cloth are determined;
(6) calculate the two-dimentional centre coordinate of index point according to the shape of the above-mentioned index point of searching;
(7) can obtain body gait motion rod figure by the dissection above-mentioned centre coordinate that sticks on the index point on joint or the end that is linked in sequence; Connect the displacement movement curve that same index point coordinate in the different images just obtains the joint by the sampling order; Two straight lines according to per 3 somes decision among the motion rod figure can calculate two angles that straight line is folded, i.e. the arthrogryposis angle.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102133103A (en) * | 2010-12-15 | 2011-07-27 | 河北工业大学 | Method for recognizing human walking gait cycle with electromyographic signal |
| CN102679964A (en) * | 2012-06-12 | 2012-09-19 | 清华大学深圳研究生院 | Gait parameter measurement system and data processing device and method thereof |
| CN103340632A (en) * | 2013-06-28 | 2013-10-09 | 北京航空航天大学 | Human joint angle measuring method based on feature point space position |
| ES2432228A1 (en) * | 2013-02-15 | 2013-12-02 | Asociación Instituto De Biomecánica De Valencia | Procedure and installation for characterizing the support pattern of a subject (Machine-translation by Google Translate, not legally binding) |
| CN104408718A (en) * | 2014-11-24 | 2015-03-11 | 中国科学院自动化研究所 | Gait data processing method based on binocular vision measuring |
| CN108577849A (en) * | 2017-12-15 | 2018-09-28 | 华东师范大学 | A kind of physiological function detection method based on mist computation model |
| CN110132241A (en) * | 2019-05-31 | 2019-08-16 | 吉林化工学院 | A kind of high-precision gait recognition method and device based on time series analysis |
| CN110638449A (en) * | 2019-09-30 | 2020-01-03 | 福州大学 | Muscle quantitative analysis method based on mechanical work |
| CN112998700A (en) * | 2021-05-26 | 2021-06-22 | 北京欧应信息技术有限公司 | Apparatus, system and method for assisting assessment of a motor function of an object |
-
2000
- 2000-10-09 CN CN 00129556 patent/CN1286962A/en active Pending
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102133103A (en) * | 2010-12-15 | 2011-07-27 | 河北工业大学 | Method for recognizing human walking gait cycle with electromyographic signal |
| CN102133103B (en) * | 2010-12-15 | 2012-08-15 | 河北工业大学 | Method for recognizing human walking gait cycle with electromyographic signal |
| CN102679964A (en) * | 2012-06-12 | 2012-09-19 | 清华大学深圳研究生院 | Gait parameter measurement system and data processing device and method thereof |
| ES2432228A1 (en) * | 2013-02-15 | 2013-12-02 | Asociación Instituto De Biomecánica De Valencia | Procedure and installation for characterizing the support pattern of a subject (Machine-translation by Google Translate, not legally binding) |
| CN103340632A (en) * | 2013-06-28 | 2013-10-09 | 北京航空航天大学 | Human joint angle measuring method based on feature point space position |
| CN104408718B (en) * | 2014-11-24 | 2017-06-30 | 中国科学院自动化研究所 | A kind of gait data processing method based on Binocular vision photogrammetry |
| CN104408718A (en) * | 2014-11-24 | 2015-03-11 | 中国科学院自动化研究所 | Gait data processing method based on binocular vision measuring |
| CN108577849A (en) * | 2017-12-15 | 2018-09-28 | 华东师范大学 | A kind of physiological function detection method based on mist computation model |
| CN110132241A (en) * | 2019-05-31 | 2019-08-16 | 吉林化工学院 | A kind of high-precision gait recognition method and device based on time series analysis |
| CN110638449A (en) * | 2019-09-30 | 2020-01-03 | 福州大学 | Muscle quantitative analysis method based on mechanical work |
| CN110638449B (en) * | 2019-09-30 | 2021-05-18 | 福州大学 | Muscle quantitative analysis method based on mechanical work |
| CN112998700A (en) * | 2021-05-26 | 2021-06-22 | 北京欧应信息技术有限公司 | Apparatus, system and method for assisting assessment of a motor function of an object |
| CN112998700B (en) * | 2021-05-26 | 2021-09-24 | 北京欧应信息技术有限公司 | Apparatus, system and method for assisting assessment of a motor function of an object |
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