Article(id=1203753460488445967, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1203753457208504777, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2407690, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1708617600000, receivedDateStr=2024-02-23, revisedDate=1732291200000, revisedDateStr=2024-11-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1764926789638, onlineDateStr=2025-12-05, pubDate=1737129600000, pubDateStr=2025-01-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764926789638, onlineIssueDateStr=2025-12-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764926789638, creator=13701087609, updateTime=1764926789638, updator=13701087609, issue=Issue{id=1203753457208504777, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='2', pageStart='439', pageEnd='878', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764926788856, creator=13701087609, updateTime=1764928745558, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1203761664261858014, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1203753457208504777, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1203761664261858015, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1203753457208504777, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=459, endPage=472, ext={EN=ArticleExt(id=1203753463458013409, articleId=1203753460488445967, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Human Posture Recognition and Quality Evaluation for Martial Arts, columnId=1156264149949735860, journalTitle=Science Technology and Engineering, columnName=Papers·Mechanics, runingTitle=null, highlight=null, articleAbstract=

In order to evaluate the quality of professional athletes in martial arts, the camera array based measurement and multi-view geometry were combined to develop a refined recognition method of human movements under the constraints of human parametric model, and a quantitative evaluation method system of martial arts movements was established based on the obtained joint position and angle information, and the technical movements of athletes of different levels in the five-step boxing event were measured and evaluated. The results show that the method developed in this paper can effectively realize the identification and quality evaluation of athletes’ movements in Wushu events, and the research results can also be extended to other competitive sports and public health, so as to provide support for scientific training and sports rehabilitation.

, correspAuthors=Jing ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Wei MA, Chuan-bao CAO, Qin-wei MA, Shi-chao ZHOU, Ke-yi HOU, Wen-zhang SUN, Bo-fang BAI, Jing ZHANG), CN=ArticleExt(id=1203753469451674484, articleId=1203753460488445967, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=武术技术动作识别与质量评价研究, columnId=1156264150092342198, journalTitle=科学技术与工程, columnName=论文·力学, runingTitle=null, highlight=null, articleAbstract=

为解决武术项目专业运动员动作质量定量化评价的难题,将阵列式摄像测量技术与多视角几何原理结合,发展了人体参数化模型约束下的人体动作精细化识别方法,基于所获得的关节位置与角度信息建立了武术套路动作定量化评价方法体系,对五步拳项目不同水平运动员的技术动作进行了测量和评价。结果表明:本文方法能够有效地实现武术项目运动员动作质量识别与质量评价,研究结果也可以拓展至其他竞技体育运动与大众健康,为科学化训练与运动康复提供支持。

, correspAuthors=张晶, authorNote=null, correspAuthorsNote=
* 张晶(1985—),男,回族,河北邯郸人,硕士,副教授。研究方向:武术训练。E-mail:
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马威 (1987—),男,回族,河北沧州人,硕士,讲师。研究方向:武术文化。E-mail:

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马威 (1987—),男,回族,河北沧州人,硕士,讲师。研究方向:武术文化。E-mail:

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马威 (1987—),男,回族,河北沧州人,硕士,讲师。研究方向:武术文化。E-mail:

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白色点为关节坐标

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ClCr分别为左、右相机光心;P为空间中一点;P1P2为投影点

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①~㉓为体关节序号

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language=EN, label=Fig.11, caption=The three-dimensional movement posture of the three athletes “hugging hands and punching”, figureFileSmall=ycK64QZ2otK157nJWgAdFw==, figureFileBig=6IhNBROo/7mFhCfiOniN1w==, tableContent=null), ArticleFig(id=1203787167022686328, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=CN, label=图11, caption=3名运动员“搂手冲拳”三维动作姿态, figureFileSmall=ycK64QZ2otK157nJWgAdFw==, figureFileBig=6IhNBROo/7mFhCfiOniN1w==, tableContent=null), ArticleFig(id=1203787167161098360, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=EN, label=Fig.12, caption=Comparison of ∠13 of three athletes, figureFileSmall=YtSv4HpsIs1ARXgMUa2F8Q==, figureFileBig=rje4v3RrP2sLP4lhtwNN8w==, tableContent=null), ArticleFig(id=1203787167278538879, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=CN, label=图12, caption=3名运动员∠13角度的对比图, 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label=Fig.16, caption=Comparison of the angle of right arm, figureFileSmall=kXnUj+kzbO/c4EqQrECWSw==, figureFileBig=F3ge8hn3gy5bhBjo2kPgKg==, tableContent=null), ArticleFig(id=1203787169040146600, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=CN, label=图16, caption=3名运动员右臂水平角度, figureFileSmall=kXnUj+kzbO/c4EqQrECWSw==, figureFileBig=F3ge8hn3gy5bhBjo2kPgKg==, tableContent=null), ArticleFig(id=1203787169140809899, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=EN, label=Fig.17, caption=Comparison of the position error between the hand and the waist, figureFileSmall=57u66d0T0s3UxEmTCTm+2g==, figureFileBig=JBWfv4YdpxlQiLmdgpyykQ==, tableContent=null), ArticleFig(id=1203787169220501680, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=CN, label=图17, caption=3名运动员手与腰部位置误差对比图, figureFileSmall=57u66d0T0s3UxEmTCTm+2g==, 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journalId=1146123166801305609, articleId=1203753460488445967, language=CN, label=图19, caption=3名运动员穿掌速度对比图, figureFileSmall=2WE3eO7t2MgO86iRKAJfnw==, figureFileBig=apN6joiMhyLT2n1GG4i2RA==, tableContent=null), ArticleFig(id=1203787169769955524, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=EN, label=Fig.20, caption=Comparison of the kick speed, figureFileSmall=tXuXvhpSoUFDXO+XF9cEqA==, figureFileBig=l6lKLcIbijKk9wkTrYn4Hw==, tableContent=null), ArticleFig(id=1203787169849647305, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=CN, label=图20, caption=3名运动员的踢腿速度对比图, figureFileSmall=tXuXvhpSoUFDXO+XF9cEqA==, figureFileBig=l6lKLcIbijKk9wkTrYn4Hw==, tableContent=null), ArticleFig(id=1203787169950310608, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=EN, label=Table 1, caption=

Quality evaluation index of five-step punch action

, figureFileSmall=null, figureFileBig=null, tableContent=
招式 分解动作 质量标准 定量标准
并步抱拳
(预备式)
①两腿并拢;
②抱拳摆头;
③双拳抱于两侧腰间
①两腿大腿小腿之间角度为180°;
②手部位置与腰部平齐
①∠9=180°, ∠12=180°;
y4=0.5(y1+y8), y7=0.5(y1+y11)
搂手冲拳
(过渡)
①从并步站立到弓步冲拳的过渡动作要求上体挺直,左手从右侧搂手至左前方,下肢尽量下蹲;
②左手停于右肩,指尖向上
①左腿大小腿之间成180°,右腿大小腿之间小于135°;
②左臂大小臂之间成90°,右臂大小臂之间成90°
①∠12=180°, ∠9<135°;
②∠6=90°, y7=0.5(y1+y11)
搂手冲拳
(过渡)
①要求弓步前腿大小腿成90°,大腿达水平,后腿绷直;
②左手抱拳于腰间,右手向前冲平
①左腿大小腿成90°,大腿与水平面平行(0°),右腿大小腿成180°;
②左臂大小臂成90°,右臂大小臂之间成180°,右臂与水平面平行(0°)
①∠12=90°, y11=y12 y13=0,
∠9=180°;
y7=0.5(y1+y11), ∠3=90°,
y2=y3=y4
弹踢冲拳 ①两腿绷直,右腿由曲到伸(弹腿),右脚脚尖绷直,右腿要高于水平,上体保持正直;
②右拳抱于腰间,左臂向前冲平,上体保持正直,目视前方
①左腿大小腿之间角度180°,并垂直于地面(90°),右腿大小腿之间角度180°,左腿大腿与右腿大腿之间角度大于90°;
②左臂大小臂之间180°,并于水平面平行(0°),右臂大小臂之间90°,视线与水平方向平行(0°)
①∠12=180°, ∠13=90°,∠9=180°,y10>y9>y8;
②∠6=180°,y5=y6=y7, y4=0.5(y1+y8), y14=y15
马步架打 ①下肢达水平,双脚脚尖不能内外展,大小腿成90°;
②躯干保持正直,向右摆头;
③右臂冲平拳,左臂弯曲角度不能超过30°,左手掌外翻,掌心向上,目视右拳方向
①左右腿大腿与水平方向平行(0°),两腿大小腿之间成90°;
②目视方向与右手臂方向一致;
③右臂大小臂之间成180°,与水平方向平行(0°),左臂大小臂之间角度大于150°且小于180°
y8=y9,y11=y12,∠9=90°,
∠12=90°;
y14=y15;
③∠3=180°,y2=y3=y4, 150°<∠6<180°
撤步按掌
(过渡)
①身体正直,右手按平掌于胸前,左手抱拳于腰间(过渡动作) ①两腿大小腿之间成180°,左臂大小臂之间成90°,右臂大小臂之间成180°,并于水平面平行 ①∠12=180°,
∠9=180°,y7=0.5(y1+y11),
∠3=180°,y2=y3=y4
歇步冲拳 ①左脚跟与臀部接触,左腿膝关节插于右腿小腿外侧,右腿大小腿角度尽量小;
②左手冲平,目视左拳方向,右手抱拳与腰间
①左腿大小腿之间角度小于30°,右腿大小腿之间角度小于45°,两腿大腿之间角度大于0°(交叉);
②左臂大小臂之间角度180°,与水平面平行,右臂大小臂之间角度成45°,视线与左臂方向一致
①∠12<30°,∠9<45°;
②∠6=180°,y5=y6=y7,
y4=0.5(y1+y8),y14=y15
提膝穿掌 ①支撑腿(右腿)站直,左腿提膝过腰,大小腿尽量折叠,左脚护于右膝关节上侧;
②右手穿掌于右侧头上方,右臂伸直,目视右上方,左手驾于右肩
①支撑腿(右腿)大小腿之间成180°,且与水平面成90°,左腿大腿与水平面角度大于10°,左腿大小腿之间角度小于30°;
②右臂大小臂之间成180°,且与水平面成30°,左臂大小臂之间成90°
①∠9=180°,∠10=90°,
y12>0.5(y1+y11),∠12<30°;
②∠3=180°,y4>y3>y2,y7=y2
仆步穿掌 ①右腿大小腿全蹲,大腿不能高于水平,右腿膝关节外展,左腿完全伸直,左脚内扣,双脚全脚掌着地;
②两臂伸直成一条线,左臂不能低于水平,右臂高于水平
①右腿大小腿之间角度>90°,且右腿与水平面夹角>10°(低于水平),左腿大小腿之间角度180°,左右腿大腿间角度大于90°;
②左臂大小臂之间成180°,右臂大小臂之间成180°,左臂与水平面夹角>0°(向上),右臂与水平面夹角>0°(向下)
①∠9<90°,y8<y9,
∠12=180°,x9-12>x8-11;
②∠3=180°,∠6=180°,
y4>y3>y2>y5>y6>y7,y14=y15
并步撑掌 ①并步站立,两腿并拢,躯干保持正直,目视正前方;
②右臂收拢,左臂架直,弯曲角度不超过30°
①两腿大小腿间成180°,两腿与地面成90°,视线与身体垂直方向成0°;
②左臂大小臂之间角度大于150°且小于180°,右臂大小臂之间成90°
①∠12=180°,∠13=90°,
∠9=180°,∠10=90°y14=y15;
②150°<∠6<180°, y4=0.5(y1+y8)
虚步挑掌 ①左腿屈蹲大小腿达90°,大腿达水平,屈蹲腿脚掌全掌不能离地,右脚脚尖点地;
②躯干保持正直,目视前方;
③上肢右臂水平,左手直臂高于水平(45°),右手掌与小臂成90°
①左腿大小腿之间成90°,大腿与水平方向之间成0°,右腿大小腿之间成180°;
②视线与水平方向一致(0°);
③右臂大小臂之间成180°,左臂大小臂之间成180°,左臂大臂与水平成45°
①∠12=90°,y11=y12,y13=0,
∠9=180°;
y14=y15;
③∠6=180°,∠3=180°, y7>y6>y5,∠5=135°
搂手冲拳
(过渡)
①从并步站立到弓步冲拳的过渡动作要求上体挺直,左手从右侧搂手至左前方,下肢尽量下蹲;
②左手停于右肩,指尖向上
①左腿大小腿之间成180°,右腿大小腿之间<135°;
②左臂大小臂之间成90°,右臂大小臂之间成90°
①∠12=180°,∠9<135°;
②∠6=90°,y7=0.5(y1+y11)
搂手冲拳
(过渡)
①要求弓步前腿大小腿成90°,大腿达水平,后腿绷直;
②左手抱拳于腰间,右手向前冲平;
③挺胸抬头
①左腿大小腿成90°,大腿与水平面平行(0°),右腿大小腿成180°;
②左臂大小臂成90°,右臂大小臂之间成180°,右臂与水平面平行(0°);
③视线与水平方向平行(0°)
①∠12=90°,y11=y12 y13=0,
∠9=180°;
y7=0.5(y1+y11),∠3=90°
y2=y3=y4;
y14=y15
弹踢冲拳 ①两腿绷直,右腿由曲到伸(弹腿),右脚脚尖绷直,右腿要高于水平,上体保持正直;
②右拳抱于腰间,左臂向前冲平,上体保持正直
①左腿大小腿之间角度180°,并垂直于地面(90°),右腿大小腿之间角度180°,左腿大腿与右腿大腿之间角度大于90°;
②左臂大小臂之间180°,并于水平面平行(0°),右臂大小臂之间90°,视线与水平方向平行(0°)
①∠12=180°,∠13=90°, ∠9=180°,y10>y9>y8;
②∠6=180°,y5=y6=y7, y4=
0.5(y1+y8),y14=y15
马步架打 ①下肢达水平,双脚脚尖不能内外展,大小腿成90°;
②躯干保持正直,向右摆头;
③右臂冲平拳,左臂弯曲角度不能超过30°,左手掌外翻,掌心向上,目视右拳方向
①左右腿大腿与水平方向平行(0°),两腿大小腿之间成90°;
②目视方向与右手臂方向一致;
③右臂大小臂之间成180°,与水平方向平行(0°),左臂大小臂之间角度大于150°且小于180°
y8=y9,y11=y12,∠9=90°,
∠12=90°;
y14=y15;
③∠3=180°,y2=y3=y4, 150°<∠6<180°
撤步按掌
(过渡)
①身体正直,右手按平掌于胸前,左手抱拳于腰间(过渡动作) ①两腿大小腿之间成180°,左臂大小臂之间成90°,右臂大小臂之间成180°,并于水平面平行 ①∠12=180°,
∠9=180°,y7=0.5(y1+y11), ∠3=180°,y2=y3=y4
歇步冲拳 ①左脚跟与臀部接触,左腿膝关节插于右腿小腿外侧,右腿大小腿角度尽量小;
②左手冲平,目视左拳方向,右手抱拳与腰间
①左腿大小腿之间角度小于30°,右腿大小腿之间角度小于45°,两腿大腿之间角度大于0°(交叉);
②左臂大小臂之间角度180°,与水平面平行,右臂大小臂之间角度成45°,视线与左臂方向一致
①∠12<30°, ∠9<45°;
②∠6=180°,y5=y6=y7,
y4=0.5(y1+y8),y14=y15
提膝穿掌 ①支撑腿(右腿)站直,左腿提膝过腰,大小腿尽量折叠,左脚护于右膝关节上侧;
②右手穿掌于右侧头上方,右臂伸直,目视右上方,左手驾于右肩
①支撑腿(右腿)大小腿之间成180°,且与水平面成90°,左腿大腿与水平面角度大于10°,左腿大小腿之间角度小于30°;
②右臂大小臂之间成180°,且与水平面成30°,左臂大小臂之间成90°
①∠9=180°,∠10=90°,
y12>0.5(y1+y11),∠12<30°;
②∠3=180°,y4>y3>y2,y7=y2
仆步穿掌 ①右腿大小腿全蹲,大腿不能高于水平,右腿膝关节外展,左腿完全伸直,左脚内扣,双脚全脚掌着地;
②两臂伸直成一条线,左臂不能低于水平,右臂高于水平,目视前方
①右腿大小腿之间角度小于90°,且右腿与水平面夹角大于10°(低于水平),左腿大小腿之间角度180°,左右腿大腿间角度大于90°;
②左臂大小臂之间成180°,右臂大小臂之间成180°,左臂与水平面夹角大于0°(向上),右臂与水平面夹角大于0°(向下)
①∠9<90°,y8<y9,
∠12=180°,x9-12>x8-11;
②∠3=180°,∠6=180°,
y4>y3>y2>y5>y6>y7,y14=y15
并步撑掌 ①并步站立,两腿并拢,躯干保持正直,目视正前方;
②右臂收拢,左臂架直,弯曲角度不超过30°
①两腿大小腿之间成180°,两腿与地面成90°,视线与身体垂直方向成0°;
②左臂大小臂之间角度大于150°且小于180°,右臂大小臂之间成90°
①∠12=180°,∠13=90°,
∠9=180°,∠10=90°,y14=y15;
②150°<∠6<180°, y4=0.5(y1+y8)
虚步挑掌 ①左腿屈蹲大小腿达90°,大腿达水平,屈蹲腿脚掌全掌不能离地,右脚脚尖点地;
②躯干保持正直,目视前方;
③上肢右臂水平,左手直臂高于水平(45°),右手掌与小臂成90°
①左腿大小腿之间成90°,大腿与水平方向之间成0°,右腿大小腿之间成180°;
②视线与水平方向一致(0°);
③右臂大小臂之间成180°,左臂大小臂之间成180°,左臂大臂与水平成45°
①∠12=90°,y11=y12,y13=0,
∠9=180°;
y14=y15;
③∠6=180°,∠3=180°, y7>y6>y5,∠5=135°
), ArticleFig(id=1203787170059362518, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1203753460488445967, language=CN, label=表1, caption=

五步拳动作质量评价指标

, figureFileSmall=null, figureFileBig=null, tableContent=
招式 分解动作 质量标准 定量标准
并步抱拳
(预备式)
①两腿并拢;
②抱拳摆头;
③双拳抱于两侧腰间
①两腿大腿小腿之间角度为180°;
②手部位置与腰部平齐
①∠9=180°, ∠12=180°;
y4=0.5(y1+y8), y7=0.5(y1+y11)
搂手冲拳
(过渡)
①从并步站立到弓步冲拳的过渡动作要求上体挺直,左手从右侧搂手至左前方,下肢尽量下蹲;
②左手停于右肩,指尖向上
①左腿大小腿之间成180°,右腿大小腿之间小于135°;
②左臂大小臂之间成90°,右臂大小臂之间成90°
①∠12=180°, ∠9<135°;
②∠6=90°, y7=0.5(y1+y11)
搂手冲拳
(过渡)
①要求弓步前腿大小腿成90°,大腿达水平,后腿绷直;
②左手抱拳于腰间,右手向前冲平
①左腿大小腿成90°,大腿与水平面平行(0°),右腿大小腿成180°;
②左臂大小臂成90°,右臂大小臂之间成180°,右臂与水平面平行(0°)
①∠12=90°, y11=y12 y13=0,
∠9=180°;
y7=0.5(y1+y11), ∠3=90°,
y2=y3=y4
弹踢冲拳 ①两腿绷直,右腿由曲到伸(弹腿),右脚脚尖绷直,右腿要高于水平,上体保持正直;
②右拳抱于腰间,左臂向前冲平,上体保持正直,目视前方
①左腿大小腿之间角度180°,并垂直于地面(90°),右腿大小腿之间角度180°,左腿大腿与右腿大腿之间角度大于90°;
②左臂大小臂之间180°,并于水平面平行(0°),右臂大小臂之间90°,视线与水平方向平行(0°)
①∠12=180°, ∠13=90°,∠9=180°,y10>y9>y8;
②∠6=180°,y5=y6=y7, y4=0.5(y1+y8), y14=y15
马步架打 ①下肢达水平,双脚脚尖不能内外展,大小腿成90°;
②躯干保持正直,向右摆头;
③右臂冲平拳,左臂弯曲角度不能超过30°,左手掌外翻,掌心向上,目视右拳方向
①左右腿大腿与水平方向平行(0°),两腿大小腿之间成90°;
②目视方向与右手臂方向一致;
③右臂大小臂之间成180°,与水平方向平行(0°),左臂大小臂之间角度大于150°且小于180°
y8=y9,y11=y12,∠9=90°,
∠12=90°;
y14=y15;
③∠3=180°,y2=y3=y4, 150°<∠6<180°
撤步按掌
(过渡)
①身体正直,右手按平掌于胸前,左手抱拳于腰间(过渡动作) ①两腿大小腿之间成180°,左臂大小臂之间成90°,右臂大小臂之间成180°,并于水平面平行 ①∠12=180°,
∠9=180°,y7=0.5(y1+y11),
∠3=180°,y2=y3=y4
歇步冲拳 ①左脚跟与臀部接触,左腿膝关节插于右腿小腿外侧,右腿大小腿角度尽量小;
②左手冲平,目视左拳方向,右手抱拳与腰间
①左腿大小腿之间角度小于30°,右腿大小腿之间角度小于45°,两腿大腿之间角度大于0°(交叉);
②左臂大小臂之间角度180°,与水平面平行,右臂大小臂之间角度成45°,视线与左臂方向一致
①∠12<30°,∠9<45°;
②∠6=180°,y5=y6=y7,
y4=0.5(y1+y8),y14=y15
提膝穿掌 ①支撑腿(右腿)站直,左腿提膝过腰,大小腿尽量折叠,左脚护于右膝关节上侧;
②右手穿掌于右侧头上方,右臂伸直,目视右上方,左手驾于右肩
①支撑腿(右腿)大小腿之间成180°,且与水平面成90°,左腿大腿与水平面角度大于10°,左腿大小腿之间角度小于30°;
②右臂大小臂之间成180°,且与水平面成30°,左臂大小臂之间成90°
①∠9=180°,∠10=90°,
y12>0.5(y1+y11),∠12<30°;
②∠3=180°,y4>y3>y2,y7=y2
仆步穿掌 ①右腿大小腿全蹲,大腿不能高于水平,右腿膝关节外展,左腿完全伸直,左脚内扣,双脚全脚掌着地;
②两臂伸直成一条线,左臂不能低于水平,右臂高于水平
①右腿大小腿之间角度>90°,且右腿与水平面夹角>10°(低于水平),左腿大小腿之间角度180°,左右腿大腿间角度大于90°;
②左臂大小臂之间成180°,右臂大小臂之间成180°,左臂与水平面夹角>0°(向上),右臂与水平面夹角>0°(向下)
①∠9<90°,y8<y9,
∠12=180°,x9-12>x8-11;
②∠3=180°,∠6=180°,
y4>y3>y2>y5>y6>y7,y14=y15
并步撑掌 ①并步站立,两腿并拢,躯干保持正直,目视正前方;
②右臂收拢,左臂架直,弯曲角度不超过30°
①两腿大小腿间成180°,两腿与地面成90°,视线与身体垂直方向成0°;
②左臂大小臂之间角度大于150°且小于180°,右臂大小臂之间成90°
①∠12=180°,∠13=90°,
∠9=180°,∠10=90°y14=y15;
②150°<∠6<180°, y4=0.5(y1+y8)
虚步挑掌 ①左腿屈蹲大小腿达90°,大腿达水平,屈蹲腿脚掌全掌不能离地,右脚脚尖点地;
②躯干保持正直,目视前方;
③上肢右臂水平,左手直臂高于水平(45°),右手掌与小臂成90°
①左腿大小腿之间成90°,大腿与水平方向之间成0°,右腿大小腿之间成180°;
②视线与水平方向一致(0°);
③右臂大小臂之间成180°,左臂大小臂之间成180°,左臂大臂与水平成45°
①∠12=90°,y11=y12,y13=0,
∠9=180°;
y14=y15;
③∠6=180°,∠3=180°, y7>y6>y5,∠5=135°
搂手冲拳
(过渡)
①从并步站立到弓步冲拳的过渡动作要求上体挺直,左手从右侧搂手至左前方,下肢尽量下蹲;
②左手停于右肩,指尖向上
①左腿大小腿之间成180°,右腿大小腿之间<135°;
②左臂大小臂之间成90°,右臂大小臂之间成90°
①∠12=180°,∠9<135°;
②∠6=90°,y7=0.5(y1+y11)
搂手冲拳
(过渡)
①要求弓步前腿大小腿成90°,大腿达水平,后腿绷直;
②左手抱拳于腰间,右手向前冲平;
③挺胸抬头
①左腿大小腿成90°,大腿与水平面平行(0°),右腿大小腿成180°;
②左臂大小臂成90°,右臂大小臂之间成180°,右臂与水平面平行(0°);
③视线与水平方向平行(0°)
①∠12=90°,y11=y12 y13=0,
∠9=180°;
y7=0.5(y1+y11),∠3=90°
y2=y3=y4;
y14=y15
弹踢冲拳 ①两腿绷直,右腿由曲到伸(弹腿),右脚脚尖绷直,右腿要高于水平,上体保持正直;
②右拳抱于腰间,左臂向前冲平,上体保持正直
①左腿大小腿之间角度180°,并垂直于地面(90°),右腿大小腿之间角度180°,左腿大腿与右腿大腿之间角度大于90°;
②左臂大小臂之间180°,并于水平面平行(0°),右臂大小臂之间90°,视线与水平方向平行(0°)
①∠12=180°,∠13=90°, ∠9=180°,y10>y9>y8;
②∠6=180°,y5=y6=y7, y4=
0.5(y1+y8),y14=y15
马步架打 ①下肢达水平,双脚脚尖不能内外展,大小腿成90°;
②躯干保持正直,向右摆头;
③右臂冲平拳,左臂弯曲角度不能超过30°,左手掌外翻,掌心向上,目视右拳方向
①左右腿大腿与水平方向平行(0°),两腿大小腿之间成90°;
②目视方向与右手臂方向一致;
③右臂大小臂之间成180°,与水平方向平行(0°),左臂大小臂之间角度大于150°且小于180°
y8=y9,y11=y12,∠9=90°,
∠12=90°;
y14=y15;
③∠3=180°,y2=y3=y4, 150°<∠6<180°
撤步按掌
(过渡)
①身体正直,右手按平掌于胸前,左手抱拳于腰间(过渡动作) ①两腿大小腿之间成180°,左臂大小臂之间成90°,右臂大小臂之间成180°,并于水平面平行 ①∠12=180°,
∠9=180°,y7=0.5(y1+y11), ∠3=180°,y2=y3=y4
歇步冲拳 ①左脚跟与臀部接触,左腿膝关节插于右腿小腿外侧,右腿大小腿角度尽量小;
②左手冲平,目视左拳方向,右手抱拳与腰间
①左腿大小腿之间角度小于30°,右腿大小腿之间角度小于45°,两腿大腿之间角度大于0°(交叉);
②左臂大小臂之间角度180°,与水平面平行,右臂大小臂之间角度成45°,视线与左臂方向一致
①∠12<30°, ∠9<45°;
②∠6=180°,y5=y6=y7,
y4=0.5(y1+y8),y14=y15
提膝穿掌 ①支撑腿(右腿)站直,左腿提膝过腰,大小腿尽量折叠,左脚护于右膝关节上侧;
②右手穿掌于右侧头上方,右臂伸直,目视右上方,左手驾于右肩
①支撑腿(右腿)大小腿之间成180°,且与水平面成90°,左腿大腿与水平面角度大于10°,左腿大小腿之间角度小于30°;
②右臂大小臂之间成180°,且与水平面成30°,左臂大小臂之间成90°
①∠9=180°,∠10=90°,
y12>0.5(y1+y11),∠12<30°;
②∠3=180°,y4>y3>y2,y7=y2
仆步穿掌 ①右腿大小腿全蹲,大腿不能高于水平,右腿膝关节外展,左腿完全伸直,左脚内扣,双脚全脚掌着地;
②两臂伸直成一条线,左臂不能低于水平,右臂高于水平,目视前方
①右腿大小腿之间角度小于90°,且右腿与水平面夹角大于10°(低于水平),左腿大小腿之间角度180°,左右腿大腿间角度大于90°;
②左臂大小臂之间成180°,右臂大小臂之间成180°,左臂与水平面夹角大于0°(向上),右臂与水平面夹角大于0°(向下)
①∠9<90°,y8<y9,
∠12=180°,x9-12>x8-11;
②∠3=180°,∠6=180°,
y4>y3>y2>y5>y6>y7,y14=y15
并步撑掌 ①并步站立,两腿并拢,躯干保持正直,目视正前方;
②右臂收拢,左臂架直,弯曲角度不超过30°
①两腿大小腿之间成180°,两腿与地面成90°,视线与身体垂直方向成0°;
②左臂大小臂之间角度大于150°且小于180°,右臂大小臂之间成90°
①∠12=180°,∠13=90°,
∠9=180°,∠10=90°,y14=y15;
②150°<∠6<180°, y4=0.5(y1+y8)
虚步挑掌 ①左腿屈蹲大小腿达90°,大腿达水平,屈蹲腿脚掌全掌不能离地,右脚脚尖点地;
②躯干保持正直,目视前方;
③上肢右臂水平,左手直臂高于水平(45°),右手掌与小臂成90°
①左腿大小腿之间成90°,大腿与水平方向之间成0°,右腿大小腿之间成180°;
②视线与水平方向一致(0°);
③右臂大小臂之间成180°,左臂大小臂之间成180°,左臂大臂与水平成45°
①∠12=90°,y11=y12,y13=0,
∠9=180°;
y14=y15;
③∠6=180°,∠3=180°, y7>y6>y5,∠5=135°
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武术技术动作识别与质量评价研究
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马威 1 , 曹传宝 1 , 马沁巍 2 , 周士潮 3 , 候珂毅 2 , 孙文章 2 , 白博方 2 , 张晶 1, *
科学技术与工程 | 论文·力学 2025,25(2): 459-472
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科学技术与工程 | 论文·力学 2025, 25(2): 459-472
武术技术动作识别与质量评价研究
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马威1 , 曹传宝1, 马沁巍2, 周士潮3, 候珂毅2, 孙文章2, 白博方2, 张晶1, *
作者信息
  • 1 河北体育学院武术系, 石家庄 050800
  • 2 北京理工大学宇航学院, 北京 100081
  • 3 上海交通大学船舶海洋与建筑工程学院, 上海 200240
  • 马威 (1987—),男,回族,河北沧州人,硕士,讲师。研究方向:武术文化。E-mail:

通讯作者:

* 张晶(1985—),男,回族,河北邯郸人,硕士,副教授。研究方向:武术训练。E-mail:
Human Posture Recognition and Quality Evaluation for Martial Arts
Wei MA1 , Chuan-bao CAO1, Qin-wei MA2, Shi-chao ZHOU3, Ke-yi HOU2, Wen-zhang SUN2, Bo-fang BAI2, Jing ZHANG1, *
Affiliations
  • 1 Hebei Institute of Physical Education, Shijiazhuang 050041, China
  • 2 School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
  • 3 School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
出版时间: 2025-01-18 doi: 10.12404/j.issn.1671-1815.2407690
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为解决武术项目专业运动员动作质量定量化评价的难题,将阵列式摄像测量技术与多视角几何原理结合,发展了人体参数化模型约束下的人体动作精细化识别方法,基于所获得的关节位置与角度信息建立了武术套路动作定量化评价方法体系,对五步拳项目不同水平运动员的技术动作进行了测量和评价。结果表明:本文方法能够有效地实现武术项目运动员动作质量识别与质量评价,研究结果也可以拓展至其他竞技体育运动与大众健康,为科学化训练与运动康复提供支持。

人体动作测量  /  视觉测量  /  动作评价  /  武术  /  五步拳

In order to evaluate the quality of professional athletes in martial arts, the camera array based measurement and multi-view geometry were combined to develop a refined recognition method of human movements under the constraints of human parametric model, and a quantitative evaluation method system of martial arts movements was established based on the obtained joint position and angle information, and the technical movements of athletes of different levels in the five-step boxing event were measured and evaluated. The results show that the method developed in this paper can effectively realize the identification and quality evaluation of athletes’ movements in Wushu events, and the research results can also be extended to other competitive sports and public health, so as to provide support for scientific training and sports rehabilitation.

humanposture measurement  /  visual measurements  /  motion evaluation  /  martial arts  /  five-step punch
马威, 曹传宝, 马沁巍, 周士潮, 候珂毅, 孙文章, 白博方, 张晶. 武术技术动作识别与质量评价研究. 科学技术与工程, 2025 , 25 (2) : 459 -472 . DOI: 10.12404/j.issn.1671-1815.2407690
Wei MA, Chuan-bao CAO, Qin-wei MA, Shi-chao ZHOU, Ke-yi HOU, Wen-zhang SUN, Bo-fang BAI, Jing ZHANG. Human Posture Recognition and Quality Evaluation for Martial Arts[J]. Science Technology and Engineering, 2025 , 25 (2) : 459 -472 . DOI: 10.12404/j.issn.1671-1815.2407690
武术套路是一连串含有技击和攻防含义的动作组合,其以技击动作为素材,以攻守进退、动静疾徐、刚柔虚实等矛盾运动的变化规律编制成为整套练习形式。武术套路具备非常强的竞技体育属性,其成绩评定也需要标准化的规范与流程。为此,国家体育总局武术运动管理中心于2012年审定并发布了《武术套路竞赛规则与裁判法》[1],详细说明了竞赛的规则与评分标准。从评分标准方面分析,一名运动员的成绩由动作质量、演练水平和难度共同决定,其核心在于判定运动员是否按照要求完成了规定动作,同时根据动作的完成质量给出相应的分数。目前,对于动作质量的评价仍采用肉眼观察和经验判定的形式,缺乏定量化和科学性,严重制约了武术套路项目的进一步发展。
如想实现武术套路项目动作质量的定量化评价,需要解决人体动作测量和动作评价两个难题。对于人体动作测量,目前已有较多研究工作,主要分为接触式和非接触式两种:接触式人体动作测量通过包含惯性测量单元(inertial measurement unit,IMU)等位姿传感器的可穿戴式设备实现。在测量时将其布置在人体手腕、腰部或腿部等关节部位,通过分析加速度或角速度来获得人体骨骼运动参数[2-3]。例如,郝正东[4]使用惯性传感器开发了一套皮划艇动作捕捉系统,基于传感器数据重建出人体三维关节坐标进而对运动员上肢技术动作以及稳定性进行了分析。张艺佳等[5]基于可穿戴惯性传感器测量了滑雪运动员姿态并对其技术水平进行了评估。Wang等[6]提出了一种基于深度学习的竞技运动训练运动检测方法,借助运动惯性传感器和关节自由度,获得肢体运动检测结果。可穿戴式设备集成化程度高、响应快速,但是存在累积误差和积分误差,测量精度较差。更为重要的是,在人体关节部位佩戴相关设备对运动员动作和心理干扰较大,会影响其运动成绩。
为此,有研究者提出了光学非接触测量方法来解决可穿戴设备带来的运动干扰问题,使得竞技运动中“无感化”测量成为可能。此类方法使用图像采集设备采集人体运动的图像并对其进行分析来获得人体关节点坐标,进而计算得到骨骼运动参数,主要分为两类:基于合作目标的人体运动参数测量方法,其核心原理是标志点识别,即在测量中在人体关节位置布设标志点(如反光球等),之后通过分析图像中标志点的位置来计算关节的位置[7]。结合双(多)目立体视觉方法,还可以获得关节点的三维坐标,进而计算得到人体动作参数。此类方法较为成熟,在人体运动参数测量方面也已有Optitrak和 Vicon等商业化设备出现,最初用于影视制作等领域,被称之为“动作捕捉”系统[8-9],在竞技体育动作分析领域也有大量应用的案例。此类方法虽然测量精度高,但是仍需要佩戴标志,无法真正达到不干扰运动员动作的效果。近年来,有研究者提出将深度学习与立体视觉结合,利用大规模数据集训练神经网络算法以识别图像中的二维人体关节坐标,代表性算法为 AlphaPose[10]和OpenPose[11]。此方法无需在测试对象身体上穿戴任何的装备或安装任何附加标记点,能够实现真正的“无感化”测量,使其特别适用于竞技体育真实训练和比赛场景下的测量,但其对人体关节识别基于的是大数据模型,分析结果难以满足“个性化”的要求,使得其测量精度较低。
在动作质量识别方面,以动作数据为基础的人体行为分析以及步态分析等已经在运动心理学与运动康复学中得到了广泛的应用,但是其一般将此类问题看作是一个分类问题,得出的结果也是正常和异常的判定性结果。对于武术套路应用而言,需要将动作姿态质量进行分级评价并给出定量化的分值指标结果,这对动作质量评价提出了新的挑战。2019年世锦赛与2021年的东京奥运会,国际体操协会应用了日本富士通公司开发的AI评分辅助系统[12],其可以捕捉运动员瞬间的动作和身体扭转角度,用于辅助裁判评估。大连理工大学刘胜蓝团队基于视觉技术开发了针对花样滑冰的动作质量评价系统[13],也获得了较好的效果。但是,目前针对武术套路项目的动作测量与质量评价仍属空白,其主要困难在于武术套路动作繁多,对动作识别精度要求更高。同时,评分标准体系复杂,多是支撑腿弯曲、前举腿低于水平、前腿脚内扣触地等定性指标,定量化工作难度大。
为此,现基于人体参数化模型约束下的人体动作精细化识别方法,实现无感化的武术套路运动员姿态的精细化识别。基于所获得的关节位置与角度信息建立武术套路动作定量化评价方法体系。最后,以五步拳项目为研究对象,对不同水平运动员的技术动作进行测量,对其动作质量进行评价。
SMPL(skinned multi-person linear model)[14]是目前应用最广泛的人体参数化模型,其结合线性混合蒙皮方法,从大量三维扫描数据中学习体表形变信息,生成与真实人体动作和形态紧密匹配的模型。SMPL 模型由形态参数(shape)与姿态参数(pose)控制:形态参数定义身体外形的特征。个体的体表形态被表示为基础形态的线性组合,并从大量人体扫描数据中学习到参数与体表的映射关系。由此可见,形态参数是表征人体高矮胖瘦与肢体长度等人体“个性化”的特征参数;姿态参数定义了模型的动作姿态,通过控制子关节相对于父关节的旋转量,从而控制模型的关节运动。由此可见,姿态参数是表征人体运动状态的参数,本质上就是人体的动作参数。动作姿态的测量需要通过优化SMPL中的形态参数,使其与被试者 “形态最相像”,通过优化姿态参数使其与被试者“动作最相像”。
SMPL模型中的姿态参数表征了人体关节角度信息,形态参数表征了人体表面形貌点云坐标,通过关节回归矩阵计算出人体的动作参数(图1)。因此,基于参数化模型的人体动作测量过程就是参数化模型中参数的优化过程。
SMPL模型的两类参数获取是一个基于测量数据的优化过程,可使用两种方法:第一种是通过扫描等方式获得人体体表上特征点的三维坐标作为输入,之后驱动 SMPL 模型使得模型计算的特征点坐标与采集到特征点坐标偏差最小来实现模型参数的优化;第二种是使用可穿戴传感器或者基于视觉的标记点识别定位得到人体关节点处的坐标,实现模型参数的优化。
SMPL模型的参数优化过程可分为三步,分别为融入形态参数对于体表的影响、融入姿态参数对于体表的影响及体表和关节坐标获取(图2)。
为了描述不同人体的体表形态特征,需要使基础SMPL模型的顶点发生形变。SMPL模型使用一组形态参数(β∈R1×10)控制这一形变过程。“个性化”的形态可表示为基础形态的线性组合。通过形态参数控制基础SMPL模板产生形态位移(Sn∈R3×6 890)的过程由线性函数BS(β;Sn)定义为
B S ( β ; S n ) = n = 1 10 β n S n
β = [ β 1 , β 2 , , β 10 ] T
式中:Sn为模型顶点形态位移的正交主成分。
不同的人体对应不同的形态参数,通过低维度的形态参数可由式(2)计算出体表的形变量,叠加到基础SMPL模板之后的结果如图2(b)所示。
针对融合形态参数后的SMPL模型,可使用关节回归矩阵计算其关节坐标。不同的人体体型的关节位置不同,所以关节回归矩阵是与形态参数相关的函数。考虑形态影响的关节坐标由其体表顶点计算获得
J ( β ; J , T , S n ) = J [ T + B S ( β ; S n ) ]
式(3)中:J∈R(6 890×3)×(23×3)是一个将顶点坐标转换为关节坐标的矩阵;T为基础SMPL模型。
人体在运动过程中,不同的姿态会引起体表肌肉的互相挤压导致体表形态变化。SMPL模型定义了姿态混合形态来计算不同的姿态对于体表形态的影响。姿态参数 θ公式为
θ = [ ω T 0 , ω T 1 , , ω T 23 ]
姿态参数定义了模型的姿态(24个关节的旋转角)。ωK∈R3(K=0,1,2,…,23)表示子关节相对于父关节的相对旋转,使用轴角来表达。通过映射函数BP(θ;P): R23×3→R3×6 890将姿态向量 θ映射到模型的顶点之上的结果如图2(c)所示,其中P为学习到的姿态对模型顶点的影响矩阵。
经过前述步骤,已经针对特定人体的特定动作进行了形态参数和姿态参数的融合生成了变形后的SMPL模型顶点坐标。然而模型仍是T-Pose姿态下,需要对模型的关节与顶点进行旋转,使其生成对应姿态的模型。关节坐标可通过关节旋转参数进行旋转,此外模型顶点t也需依据旋转参数与关节参数旋转到指定位置t'。
t ' = k = 1 23 w k , i G ' k ( θ , J ) t
G ' k ( θ , J ) = G k ( θ , J ) G k ( θ * , J ) - 1
G k ( θ , J ) = j A (k) e x p ( ω j ) j j 0 1
式中:wk,i为混合权重W中的元素,表示人体各个部分的旋转量对于最终顶点位移结果的影响大小;exp(ωj)为相对于父关节j的局部旋转矩阵;Gk(θ,J)为关节k的全局旋转量;G'k(θ,J)为去除掉T-Pose下的关节旋转量之后的相对旋转量;jj为第j个关节的关节中心;A(k)为关节父节点与子节点链接关系。
通过式(5)~式(7)即可依据关节旋转量对关节与模型顶点进行变形,从而获取最终的体表和关节运动信息,如图2(d)所示。
SMPL模型优化需要输入人体关节点坐标或者人体表面形貌信息,此信息需要通过立体视觉测量方法来获取。此过程基于多视图几何原理,整体流程可以分为两步:①由不同视角下的人体图像中识别出人体二维关节点坐标,之后基于双(多)目立体视觉重建出人体三维关节坐标;②将测量得到人体三维关节坐标作为初值输入SMPL模型,通过优化的方法逐渐不断SMPL模型的形态参数与姿态参数,使得SMPL模型的关节坐标与基于多视图几何得到的三维关节对应,从而得到针对特定人体的SMPL模型。其整体流程如图3所示。
基于立体视觉的人体三维关节测量方法的原理源自多视角几何,即从不同的视角捕获场景的多幅图像,然后通过找到图像间的对应点,利用相机的几何关系来重建出三维空间中的点。
2D人体姿态检测方法通过深度学习在大量数据中学习到图像中的人体关节特征,可以从图像或视频中识别出人体的关节位置。本文研究使用AlphaPose方法[10]获取图像中的二维人体关节坐标,其处理流程如下:首先在整幅图像中检测出每个人体,之后使用深度卷积神经网络(convolutional neural networks, CNN)来检测人体的关节点。在处理过程中通过对称积分关键点回归(symmetric integral keypoint regression, SIKR)解决热力图在激活之后的不对称性问题,以优化网络训练的稳定性和效率。此外,通过使用参数化的姿态非最大抑制(parametric pose no-maximum-suppression,P-NMS)来解决由于低置信度阈值带来的姿态估计冗余问题。使用姿态引导的注意力机制来预测人体重识别特征,以准确提取关注的人体形态。最终的多阶段身份匹配结合了姿态、人体重识别特征和检测框,得到最终的人体二维关节点检测结果。
AlphaPose网络输出的结果为Halpe26点人体模型,其关节索引与关节定义如图4所示。为了统一不同数据集下的结果,利用EasyMocap方法将所有点统一到COCO25点人体模型之下[15],舍弃Halpe26点人体模型中多余的点并且利用左右肩关节以及左右髋关节中点计算COCO25人体模型中的颈部与骨盆重点坐标。
图像记录的是三维场景在二维平面上的投影,成像过程中丢失了深度信息。对于特定的二维图像点,相应的三维点被限制在视线上。使用单一图像无法确定视线上哪一点对应于图像点。但如果有两张或者两张以上的图像可用,则可以通过两条投影射线的交点找到三维点的位置,这个过程称为立体测量。
立体测量的关键是要有至少两个视角下相机对被测物进行交会成像。传统相机模型存在4个坐标系:世界坐标系(XW,YW,ZW)、相机坐标系(XC, YC,ZC)、图像物理坐标系(xc,yc)和图像像素坐标系(x, y)。世界坐标系是客观三维世界的绝对坐标系,用于描述物体在真实世界中的位置。世界坐标系随物体的大小和位置而变化,单位通常为长度单位。相机坐标系以相机的光心为坐标原点,XC轴和YC轴平行于图像的xy方向,ZC轴与光轴平行,三轴互相垂直,单位是长度单位。光心是指图像的中心,而光轴是穿过光心且与图像平面垂直的轴。图像物理坐标系以主光轴ZC与图像平面的交点为原点,xy方向如图所示,单位是长度单位。图像像素坐标系以图像的四个顶点为坐标原点,单位是像素。图像物理坐标系与像素坐标系之间的转换通过平移实现(图5)。
当一个特征点(即本文研究中的关节点)在特定相机的视野内被捕捉时,可以通过相机的位姿确定从相机中心发出并指向该特征点的一条视线。计算由多个相机延伸出来的观测线的交点能够确定该特征点在三维空间中的确切位置。假设三维特征点Pi=(xi,yi,zi)被M个相机观测到,已知相机外参RjTj,相机内参Kj,其中j=1,2,…,M,特征点在各个相机中的坐标为pij=[uij,vij],二者在不考虑畸变误差的情况下的关系式为
z i u i j v i j 1 = K j 0 0 1 R j T j 0 1 x i y i z i 1
可以通过最小化三维距离的方式来求解该二维点在空间中的坐标:由每个视角下的二维点和相机位姿可以引出一条射线,则在世界坐标下只需要找到一个距离有所观测射线距离最近的三维点。每个视角下的射线方程为
x = T j + k R T j u i j , k ( 0 , + )
特征点到射线的距离为 ( P i - T j ) R j u i j。定义目标函数为世界坐标系下的特征点Pi到所有射线的距离的平方和最小,公式为
m i n j = 1 M ( P i - T j ) R T j u i j 2
求解最小二乘误差最小的点即可求出特征点在世界坐标系下的三维坐标。基于多视角图像重建的三维人体关节坐标如图6所示。
以武术套路为代表的项目其成套动作由多个规定动作组成,规定动作间又包含有多个连接动作。因此针对动作的质量评价首先需要将连续动作分解为一个个的子动作,之后根据评价标准对每个子动作进行单独评价,之后再给出综合评价。
一个成套动作可以表达为式(11)所示的集合,即人体动作序列。
X (t) = [ X 1 (t) , X 2 (t) , , X m (t) ]
式(11)中:X(t)为成套动作的人体动作序列;x1(t),x2(t),…,xm(t)为m个关节点的空间坐标随时间的演化。
在动作分割前,应预先通过人工分割方式获取一个模板动作x(t)。
x (t) = [ x 1 (t) , x 2 (t) , , x m (t) ]
动作分割的目标就是通过模板在成套动作的人体动作序列中找到一段与模板最相似的子动作,即需要在X(t)中寻找到一段数据,使其与x(t)最相似。在使用模板动作进行成套动作的分割时,往往存在一个问题:每个运动员完成动作的时间与模板动作完成时间不同,导致无法对两段动作序列的每一帧在时间上一一对应。因此需要采用DTW(dynamic time warping)算法,即动态时间弯曲算法。该方法是一种用于分析两个时间序列相似度的方法,它允许两个时间序列在时间轴上进行某种程度的伸缩和弯曲,从而找到它们之间最优的对齐方式。具体来说,需要比较的两段数据中的序列点之间的欧式距离,将其作为两点之间相似度的指标,进而对实测的数据序列进行缩短或延长,使两者的时间长度相等,最终得到两段数据序列的相似度。为此,需要定义一个动作序列中帧到帧之间的欧式距离函数,即
d (t) = X (t) - x (t)
动态时间规整算法的关键在于寻找一条最优路径,使得两段动作序列整体之间的欧式距离最小。如将式(13)中的时间变为帧编号k,将欧式距离定义为δ,则分割过程可以表达为寻找最小δ下的帧编号k的问题。
m i n δ k = k = 1 L X (k) - x (k)
式(14)中:L为匹配窗口的长度。
在完成动作分割后,需要对动作质量进行评价。动作质量的评价分为两个方面:首先判断动作是否存在失误,即“成套动作中缺少规定动作”或“成套动作中规定动作的次序不符”;其次通过相关规范对每个子动作进行动作质量定量化评价和评分。
对于判断动作是否失误,可以按照运动员成套动作的标准,预先设置需要比对的所有动作模板并按照动作次序逐个进行分割,具体遵循以下流程。
首先,设定运动员此套动作的动作次序。
S = [ p 1 , p 2 , , p n ]
式(15)中:S为成套动作;p1,p2, …,pn为子动作。
另设n个标准动作模板,q1,q2,…,qn
其次,计算第一个标准姿势q1与成套动作S中每一个元素的欧式距离,寻找式(14)的最小的位置即为实际位置。如最小值超出了预设的阈值,则说明算法所找到的最相似子动作也与标准动作模板有很大差异,则说明此动作缺失,
最后,重复上述对比步骤n-1次,完成所有子动作的判定。通过对n个出现的次序进行判定,如与指定的动作次序不同,则判定出现与规定动作次序不符的情况。
动作质量的定量评价主要是针对成套动作中的每个子动作开展。对于武术项目而言,其一个完整的套路动作由若干规定动作和若干连接动作组成,其中与成绩密切相关的是规定动作。每个规定动作的要求均不相同,因此对其动作质量的评价也应该针对每个规定动作制定单独的标准。通过对武术套路项目裁判规则的深入分析,发现可以将动作质量的评价指标统一为“位置指标”和“角度指标”。如躯干低于水平、两大腿未交叉等可以使用位置指标来定量化评价,“支撑腿弯曲”“上体前倾超过45°”可以使用角度指标来定量化评价。位置指标与角度指标具体可以根据实际情况,使用关节位置测量结果和关节角度测量结果来进行定量化表征。
无论是位置指标还是角度指标,均不是归一化指标,因此难以将其与五分制或者百分制建立起对应的联系。但是对于武术套路项目而言,其动作质量的评分采用的是加扣分制,规定动作未完成将扣除相应的分数,难度动作完成将加上相应的分数。因此对于武术套路项目的动作质量评判,可以采取设定阈值,判定合格/不合格的方式进行定量化的质量评价。
五步拳的动作简单易学,可以帮助练习者掌握基本的武术动作,增强身体协调性和力量,特别适合初学者练习。同时,通过反复练习,可以提高动作的流畅性和速度,为学习更复杂的武术打下坚实的基础。五步拳分为并步抱拳(起势)、搂手冲拳、弹踢冲拳、马步架打、撤步按掌、歇步冲拳、提膝穿掌、仆步穿掌、并步撑掌、虚步挑掌、并步还原(收势),如图7所示。
基于上述对五步拳的动作分析,以如图8所示的关节及角度定义(坐标原点定于两髋关节的中点),建立了如表1所示的评价指标体系。
该指标体系将五步拳分为11个子动作(招式)进行评价,针对每个招式,制定了关键的分解动作规范,进而将动作规范具象化为了质量标准并给出定量化指标。如对于招式1并步抱拳(起式),其关键的分解动作为两腿并拢、抱拳摆头、双拳抱于两侧腰间,基于此设计的质量标准为两腿大腿小腿之间角度为180°,两臂大臂小臂之间角度为90°,手部位置与腰部平齐。将其变为定量化指标为∠9=180°,∠12=180°,y4=0.5(y1+y8),y7=0.5(y1+y11)。当然,在实际运动过程中,很难保证两腿间夹角完全达到180°,因此需要根据裁判或教师的经验,确定一个阈值范围,即在180°上下浮动多少即可认为合格。对于位置指标也可以采用相同的思路。
研究选择了三名武术运动员(水平分高、中、低三档,代号分别为A、B和C)开展五步拳质量评价的应用。实验测试于河北体育学院武术馆开展,使用5台摄影级高清摄像机(ZCAM E2,分辨率:1 920 pixels×1 080 pixels,帧率60 fps,时间同步精度1 ms)对运动员动作姿态进行了采集(图9)。
对三名运动员采集图像,采集过程使用了5台相机对其运动过程进行了测量,图10展示了某视角下的图像及二维关节点识别结果。
以招式“搂手冲拳”为例对3名运动员的动作进行分析,三维测量结果如图11所示。
搂手冲拳的招数要求弓步前腿大小腿成90°,大腿达水平,后腿绷直。同时左手抱拳于腰间,右手向前冲平。基于此制定的质量标准为左腿大小腿成90°,大腿与水平面平行(0°),右腿大小腿成180°。左臂大小臂成90°,右臂大小臂之间成180°,右臂与水平面平行(0°)。
1)弓步质量评价
首先分析弓步前腿大小腿角度,根据图7的定义,其由∠13的角度表征。绘制图11所示关键帧下3名运动员∠13的角度对比图如图12所示。从图中可以发现,标准要求∠13的角度为90°,运动员A的动作最为标准,运动员C的动作最不标准。
其次,分析后腿的角度,根据图7的定义,其由∠10的角度表征。绘制图11所示关键帧下3名运动员∠10的角度对比图如图13所示。从图13可以发现,标准要求∠10的角度为180°,运动员A的动作最为标准,运动员C的动作最不标准。
最后,分析前腿大腿的角度,根据图7的定义,其水平趋势由两脚连线与大腿连线之间的角度表征。绘制图11所示关键帧下3名运动员大腿与两脚连线之间的角度对比图如图14所示。从图中可以发现,标准要求达水平时大腿与两脚连线的角度为0°,运动员A的动作最为标准,运动员B的动作最不标准。
2)上肢质量评价
对于上肢,最重要的评价指标是右臂大小臂之间成180°且右臂与水平面平行。根据图7的定义,转化为定量指标,需要考察∠3的角度。绘制图11所示关键帧下3名运动员∠3的角度对比图如图15所示。从图中可以发现,标准要求∠3的角度为180°,3名运动员在此参数上差别不大,说明仅能够注意出拳的平直。但是定量化结果表明,运动员B的动作最为标准,运动员A的动作最不标准。从图15中观察也可以发现,运动员A和运动员C出拳后,前臂与大臂之间存在一定角度。
同时对于右手向前冲平的评价,要保证右臂与水平面平行。根据图7的定义,转化为定量的指标,要考察右臂连线与两脚连线之间的角度。绘制图11所示关键帧下3名运动员该角度对比图如图16所示。从图16可以发现,标准要求该角度为0°,3名运动员在此参数下的差别不大,角度值较为接近,说明总体运动员出拳较为平直。但通过定量化分析,结果表明,运动员C的动作最为标准,运动员B的动作最不标准。
最后对于左手抱拳于腰间的评价,根据图7的定义,通过分析左手的位置与腰部位置之间的差值进行衡量,这里以关节点1和关节点8的中点作为腰部位置。绘制图11所示关键帧下3名运动员手部腰部差值对比图如图17所示。从图17可以发现,标准要求该值为0 cm,3名运动员在此参数的差别不大,差值均小于5 cm,且较为接近,说明运动员总体手部接近于腰间。但通过定量化分析,结果表明,运动员A的动作最为标准,差值为1.6 cm,运动员C的动作最不标准,差值为4.0 cm。
3)冲拳质量评价
对于搂手冲拳过程中冲拳质量的评价,根据图7的定义,通过分析右手从腰间到冲拳完成达到最远端整个过程中的平均速度进行衡量与评判,即图7中点4的速度。绘制开始冲拳到冲拳完成两个位置关键帧下3名运动员手部的速度对比图如图18所示。从图中可以发现,标准要求该值越大越好,代表该运动员冲拳速度更快,质量更高,3名运动员在此参数的差别较小,整体冲拳速度较为接近。但通过定量化分析,结果表明,运动员A和C的冲拳速度更快,为1.7 m/s,优于运动员B的冲拳速度1.1 m/s。
4)穿掌质量评价
对于提膝穿掌过程中穿掌质量的评价,根据图7的定义,通过分析右手从腰间到穿掌到最高点整个过程中的平均速度进行衡量与评判,即图7中点4的速度。绘制开始穿掌至穿掌到最高点两个位置关键帧下3名运动员手部的速度对比图如图19所示。从图19可知,标准要求该值越大越好,代表该运动员穿掌速度更快,质量更高。3名运动员在此参数下存在差别,其中B、C两名运动员差异较小,与A相比有较明显的差别。但通过定量化分析,结果表明,运动员A的穿掌速度更快,为0.8 m/s,优于运动员B和C的穿掌速度0.5 m/s。
5)踢腿质量评价
对于弹踢冲拳过程中踢腿质量的评价,根据图7的定义,通过分析右脚从地面到弹踢到最高点整个过程中的平均速度进行衡量与评判,即图7中点11的速度。绘制开始踢腿到踢腿完成两个位置关键帧下3名运动员脚部的速度对比图如图20所示。从图中可以发现,标准要求该值越大越好,代表该运动员踢腿速度更快,质量更高。3名运动员在此参数下存在差别,其中B、C两名运动员差异较小,与A相比有较明显的差别。但通过定量化分析,结果表明,运动员A的踢腿速度更快,为0.9 m/s,优于运动员B和C的踢腿速度0.6 m/s。
综合比上述动作质量的五大核心指标,可以发现运动员A的整体动作质量最好,运动员C的整体动作质量较差,还有进一步提升的空间。限于文章篇幅,本研究没有给出更多招式的定量化质量评价结果。事实上,按照本文提出的评价方法,结合表1可以对所有招式的动作质量进行定量化评价。
为解决武术项目专业运动员动作质量定量化评价的难题,提出了武术项目人体动作识别与质量评价方法,得到以下结论。
(1)将阵列式摄像测量技术与多视角几何原理结合,发展了人体参数化模型约束下的人体动作精细化识别方法。
(2)将动作质量的评价指标统一为“位置指标”和“角度指标”,建立了武术套路动作定量化评价方法体系。
(3)对五步拳项目不同水平运动员的技术动作进行了测量和评价。结果表明:本文方法能够有效地实现武术项目运动员动作质量识别与质量评价。研究结果也可以拓展至其他竞技体育运动与大众健康,为科学化训练与运动康复提供支持。
  • 国家自然科学基金(12132009)
  • 河北体育学院体育产业发展研究项目(362029)
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doi: 10.12404/j.issn.1671-1815.2407690
  • 接收时间:2024-02-23
  • 首发时间:2025-12-05
  • 出版时间:2025-01-18
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  • 收稿日期:2024-02-23
  • 修回日期:2024-11-23
基金
国家自然科学基金(12132009)
河北体育学院体育产业发展研究项目(362029)
作者信息
    1 河北体育学院武术系, 石家庄 050800
    2 北京理工大学宇航学院, 北京 100081
    3 上海交通大学船舶海洋与建筑工程学院, 上海 200240

通讯作者:

* 张晶(1985—),男,回族,河北邯郸人,硕士,副教授。研究方向:武术训练。E-mail:
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
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红菇属 Russula 17 8.13
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