Hierarchical Information Fusion for Human Upper Limb Motion Capture

Transcription

1 1h Inernaional Conference on Informaion Fusion Seale, WA, USA, July 6-9, 009 Hierarchical Informaion Fusion for Human Upper Limb Moion Capure Zhiqiang Zhang 1,, Zhipei Huang 1 and Jiankang Wu 1, 1 Graduae universiy, Chinese Academy of Sciences, Beijing, China China-Singapore Insiue of Digial Media, Singapore zhiqiang@csidm.sg Absrac Moion capure serves as a key echnology in a wide specrum of applicaions, including ineracive game and learning, animaion, film special effecs, healh-care and navigaion. The exising human moion capure echniques, which use srucured muliple high resoluion cameras in he dedicaed sudio, are complicaed and expensive. As rapid developmen of micro inerial sensors-onchip, ubiquious, real-ime, and low cos human moion capure sysem using micro-inerial-sensors (MMocap) becomes possible. This paper presens a novel moion esimaion algorihm by hierarchical fusion of sensor daa and consrains of human dynamic model for human upper limb moion capure. Our mehod represens orienaions of upper limb segmens in quaernion, which is compuaionally effecive and able o avoid singulariy problem. To address he nonlinear human body segmen moion, a paricle filer is proposed o fuse 3D acceleromeer and 3D microgyroscope sensor daa o esimae upper limb moion recursively. Drif is he mos challenging issue in moion esimaion using inerial sensors. We presen a novel soluion by modeling he geomerical consrains in elbow join and fuse hese consrains o he paricle filer process o compensae drif and improve he esimaion accuracy. The experimenal resuls have shown he feasibiliy and effeciveness of he proposed moion capure and analysis algorihm. Keywords: Moion Capure, Paricle Filer, Hierarchical Informaion Fusion 1 Inroducion Moion capure serves as he core echnology in a wide specrum of applicaions, including ineracive game and learning, animaion, film special effecs, healh-care and navigaion. The exising moion capure sysems are mainly based on video processing echniques, and referred o as Mocap, such as Vicon [1] and Qualisys []. This ype of sysems are widely used in he film indusry. However, he Mocap has cerain limiaions: 1) There is a need for muliple (usually more han 10) high speed cameras srucured and calibraed in a dedicaed sudio; ) Subjecs have o wear a ISIF 1704 ailor-made sui wih rero-reflecive or ligh emiing markers; 3) There is a huge amoun of daa o be processed, realime is very difficul; 4) The sysem is exremely expensive. Wih he rapid advances of micro-inerial sensors, human moion capure using micro-inerial sensors (mainly acceleromeers and gyroscopes) have araced a lo of ineress. Dejnabadi e al. [3] developed a mehod of measuring join angle (knee) using a combinaion of acceleromeers and gyroscopes by placing a pair of virual sensors on he adjacen segmens a he cener of roaion. Hyde e al. [4] proposed a soluion o he esimaion of upper-limb orienaion using miniaure acceleromeers and gyroscopes. They focused on he design ha minimizes he number of sensors whils delivering desired moion esimaes. Vlasic e al. [5] proposed a human moion capure sysem using combinaion of ulrasonic and inerial measuremens. Based on he kinemaic and kineic analysis, hey brough in Kalman filer o fuse he informaion from all hree ypes of sensors. Yun e al. [6] proposed an exended Kalman filer o esimae he orienaion of human limb using combinaion of inerial sensors and magneic sensors. They focused on design of Kalman filer for real-ime racking of human body moion. Xsens [7] and InerSense [8] have he similar work using hybrid inerial sensors and magneic or ulrasonic sensors. Drif is he mos challenging issue in moion esimaion using inerial sensors. Above menioned work eiher did no pay aenion o he issue, or ried o compensae inerial drif by adding more ypes of sensors such as magneic or ulrasonic sensors. Exploring he use of geomerical consrains in he esimaion algorihm has shown poenial. Hu e al. [9] proposed o use he combinaion of inerial sensors and visual sensors for he upper limb movemen measuremen. Inerial measuremens were used for kinemaic analysis, while visual sensor measuremens and he consrain of upper limb lenghs are regarded as compensaive facors o updae he kinemaic resuls. The sysem can be used o esimae simple moion paerns. Velink e al. [10] proposed a mehod for drif-free esimae of he orienaions of he wo arm segmens using inerial sensors. The anaomical elbow consrain of no adducion in elbow join was considered o

2 compensae drif and revise he orienaion esimaion. The above mehods ake only one geomery consrain ino consideraion. Considering he sae-of-he-ar of he Micro-inerial sensor human Moion Capure (referred o as MMocap here afer), we propose a novel human upper limb moion capure algorihm using micro Inerial sensors only, namely, 3D acceleromeer and 3D micro-gyroscope sensors, for drif free moion esimaion. The algorihm focus on hierarchical informaion fusion: sensor daa fusion and geomerical consrains fusion. The conribuions of he paper are: 1) Human moion model. A sensor daa-driven layered human moion model is developed as a basis for sensor allocaion, and as a ool of real-ime animaion. ) Represenaion. A human arm is represened by a skeleon srucure wih wo segmens linked by a revolue join. Each segmen s orienaion is represened by a quaernion, which is compuaionally effecive and avoids problems wih singulariies, such as gimbal lock; 3) Sensor Daa Fusion. Because of nonlinear dynamics of upper limb moion, a paricle filer is employed o fuse 3D acceleromeer and 3D micro-gyroscope sensor daa and recursively esimae he quaernion of each upper limb segmen a each ime insances; 4) Geomerical Consrains Fusion. We model geomery consrains in elbow join, and fuse hese geomerical consrains in he framework of paricle filer o he inermediae esimaion resuls from sensor daa fusion o improve he esimaion accuracy. The paricle filer framework is necessary in he fusion process given he naure of non-linear and non-gaussian for geomery consrains. The good performance of he experimenal resuls have shown he feasibiliy and effeciveness of he proposed moion capure and analysis algorihm. The res of he paper is organized as follows. Secion describes 3D human model. Sensor daa fusion and geomerical consrains fusion are presened in secion 3 and 4, respecively. Experimenal resuls are given in Secion 5. Finally, conclusions and fuure work are in Secion 6. Figure 1: The skeleon of he human model framework Figure : IMUs placemens and definiions of he segmen reference frame. 3D Human Model There is no unified 3D human model for various virual simulaion applicaions, so a sensor daa-driven layered human moion model is proposed and developed. This model is based on biomechanics, and he joins of human body are caegorized according o he moion characerisics. In order o faciliae dynamic simulaion, he bone and skinning mesh poin used for deformaion are modeled wih reference o he work in [11]. In our model, every join has a local coordinae sysem, he bones obey he Paren-Child relaionship as depiced in figure 1, which are implemened in recursive manner. We use he daa flow provided by he sensors o drive he joins and relevan bones. When he model receives he quaernion sequences provided by he hierarchical informaion fusion module, i convers he quaernion sequences ino he axisangular sequences, and hen feeds he axis-angular sequence o he channel of bones; he model is consequenly animaed 1705 in real ime. In order o model smooh skin deformaion using a nonsmooh and segmened skeleon, we allow one verex affiliaed o more han one bone. Every mesh verex has a special weigh. The weigh represens he degree of influence each bone has on he final posiion of ha verex. Each verex is muliplied by he marices of he bones from he roo bone o he curren one, hen he final posiion is compued by combining all hese calculaions wih he weighs. Consequenly, when he bone is moving or roaing, he relevan verexes are also moving or roaing ogeher wih i. In his way, he deformaion of he model can bend wihou breaking and disjoining. Based on he human model, one inerial sensor uni (IMU) is placed on he laeral side of he upper arm, while anoher IMU is placed on he venral/palm side of he forearm near he wris, as depiced in figure. The IMUs measure acceleraions and angular raes ha are expressed in he sensor coordinae frame. Afer he sensor coordinae sysem

3 calibraion, he sensor coordinae frame and body segmen coordinae frame of upper arm and forearm are consisen respecively, and only ranslaion may exis beween corresponding sensor coordinae frame and body segmen coordinae frame. 3 Moion Esimaion by Fusion of Sensor Daa A human upper limb can be represened by a skeleon srucure wih wo segmens linked by a revolue join. Each segmen s orienaion can be represened by a quaernion, which is esimaed by a quaernion-based paricle filer. In his secion, we will focus on fusion of 3D acceleromeer and 3D micro-gyroscope sensor daa o esimae he orienaion of each upper limb segmen using paricle filer. I is explained in hree subsecions: process model in Quaernion, inerial sensor measuremen model and paricle filer for moion esimaion. 3.1 Process Model We define he sae vecor X of a body segmen by using wo parameers, namely, he orienaion q and he angular velociy ω. The moion model is used o predic he evoluion of he sae a ime +1 from ime, so he sae vecor should combine he orienaion q and he angular velociy ω X = ( q T,ω T) T =(q0,q 1,q,q 3,ω x,ω y,ω z ) T (1) where q =(q 0,q 1,q,q 3 ) T, represens an orienaion afer he roaion from a given reference frame R ono he sensor frame B; and ω =(ω x,ω y,ω z ) T, represens he ri-axis insananeous angular raes. To represen an orienaion, q should saisfy q = 1, where q = (q 0 + q1 + q + q 3 ). Gyroscopes are known o be subjec o error of drif rae insabiliy. The gyroscope oupu drif rae d = (d x,d y,d z ) T is modeled as a random walk driven by a zeromean whie Gaussian sequence. The drif variable d should be incorporaed in he sae vecor X by sae vecor augmenaion X = ( q T,ω T,d T) T () here, we sill use symbol X o represen augmened sae vecor. Assume ha X saisfies firs order markov propery, and hen he general process model can be given by X = A(X 1 )+W 1 (3) where W 1 = ( (W q 1 )T,(W ω 1) T,(W d 1) T) T is assumed o be zero mean addiional gaussian whie noise wih covariance marix Q. Given he oupu drif rae d is modeled as a random walk driven by a zero-mean whie Gaussian sequence, i can be wrien Now we will derive he process model wihou considering of he process noise W 1. As he sampling rae is very shor, we can model moion wih consan angular velociy ω = ω 1, (5) and hen, he quaernion q can be wrien q = q 1 Θ(, 1) = q 1 Θ( ) (6) where Θ( )=exp{ 1 R(ω 1) } [1], and R(ω 1 )= [ ω 1 ω 1 (ω 1 ) T 0 ]. (7) ω 1 is he skew-symmeric marix ha is formed using he cross-produc operaion of he angular velociy ω 1 = ( ω x 1,ω y ) T 1 ωz 1 as given ω 1 = 0 ω z 1 ω y 1 ω z 1 0 ω x 1 ω y 1 ω x 1 0. (8) We can rewrie marix exponenial Θ( ) using is Taylor series expansion Θ( )=I R(ω 1) + 1! (1 R(ω 1) ) + 1 3! (1 R(ω 1) ) 3 + The marix R has he following properies, (9) R(ω 1 ) = ω 1 I 4 4 (10) R(ω 1 ) 3 = ω 1 R(ω 1 ) (11) R(ω 1 ) 4 = ω 1 4 I 4 4 (1) R(ω 1 ) 5 = ω 1 4 R(ω 1 ) (13) R(ω 1 ) 6 = ω 1 6 I 4 4 (14) and so on. Subsiue hese properies ino (9), and afer some calculaion, we can ge Θ( ) = cos( ω 1 ) I ω 1 sin( ω 1 ) R(ω 1 ) (15) Subsiue (15) ino (6). A close look reveals ha Θ( ) is nohing bu he muliplicaion marix associaed wih a specific quaernion, so we can rewrie he (6) as q = q 1 Θ( )=q 1 [ ω 1 ω 1 ω 1 sin( ) cos( ω 1 ) ] (16) d = d 1 + W d 1 (4) 1706 where represens quaernion muliplicaion.

4 3. Inerial Sensor Measuremen Model The measuremen model relaes he measuremen value Z o he value of he sae vecor X. The IMU provides wo ypes of measuremen: acceleraion and angular rae. The generalized form of he measuremen equaion is Z = [ (Z g ) T,(Z a ) T] T = H(X )+V (17) where V =(V g,v a ) T is assumed o be zero mean addiional gaussian whie noise wih covariance marix R, Z g and Z a are hree dimensional angular rae and acceleraion measuremens respecively. Gyroscopes measure angular velociy in he local frame of each sensor, which consiss of angular rae and drif. The angular velociy ω and drif rae d are already pars of he sae vecor, leading o a simple model ha relaes he measured angular rae o he sae Z g = ω + d + V g (18) Tri-axis acceleromeer mainly measure he graviy field vecor of B wih respec o R resolved in B. Define g = [g x,g y,g z ] T as he vecor of he graviaional field resolved in R, and hen he expeced measuremens of hese fields are given by he ransformaion of g o he local coordinae sysem B, which can be represened Z a = sub (qro(q,g)) + V a (19) where sub indicaes he vecor par of a quaernion, and qro denoes he roaion from he global coordinae frame R o he coordinae frame B, which can be calculaed qro(q,g)=q 1 g q q =(Z a (0) ) q A ime, measuremens Z becomes available and is used o updae he prior pdf via he Bayes rule as follows p(x Z 1: )= p(z X )p(x Z 1: 1 ) p(z Z 1: 1 ) (3) The denominaor p(z Z 1: 1 ) is called he evidence and i is deermined as follows: p(z Z 1: 1 )= p(z X )p(x Z 1: 1 )dx (4) () and (3) comprise he general probabilisic framework of recursive Bayesian filer. The problem is ha he above mehod is only a concepual soluion since he inegrals are no racable in mos cases. Many researchers assume linear moion model and Gaussian disribuion, and hen use Kalman filer for moion esimaion. Here we do no wan o resric ourselves o his assumpion, and for he sake of laer fusion of non-gaussian and nonlinear consrains, we resor o paricle filer approximaion for opimal Bayesian soluion [13]. A paricle filer is a sequenial Mone Carlo simulaion implemenaion of a Bayesian filer. The idea of Mone Carlo simulaion is o approximae he poserior disribuion using N idenical independenly disribued paricles {(X 1,π i 1)} i N i=1. The approximaion sars wih a se of paricles. New paricles are generaed according o a suiably chosen proposal disribuion, which may depend on he old sae and he new measuremens: X i = q(x X i 1,Z ) (5) The new imporance weighs are updaed in each ime sep wih likelihood funcion, so ha he poserior pdf can be represened using a se of samples or paricles. where g q and (Z a ) q are he corresponding quaernion vecors of g and Z a g q = [ g T,0 ] T (Z a ) q = [ (Z a ) T,0 ] T 3.3 Moion Esimaion using Paricle Filer (1) From a Bayesian perspecive, he main ask of his subsecion is o recursively esimae some degree of belief in sae vecor X a ime, given he inerial measuremens Z 1: = {Z k,k =1,,} up o ime. We usually evaluae he poserior probabiliy densiy funcion (pdf) p(x Z 1: ). Assume p(x 0 Z 0 ) p(x 0 ) is he iniial pdf of he sae vecor and is known as he prior, he poserior pdf p(x Z 1: ) is hen recursively obained using he following wo seps: 1) predicion and ) updae. Suppose ha he pdf p(x 1 Z 1: 1 ) a ime 1 is available. The predicion sage involves obaining he prior pdf of he sae a ime via he sae process model (3). The resuling predicion equaion is: p(x Z 1: 1 )= p(x X 1 )p(x 1 Z 1: 1 )dx 1 () 1707 π i π 1 i p(z X)p(x i i X 1) i q(x X i 1 i,z ) N π i =1 i (6) A common problem wih he sandard paricle filer is he degeneracy phenomenon, where afer a few ieraions, all bu one paricle will have negligible weigh. To preven from de-generaion, re-sampling is necessary. The basic idea of re-sampling is o drop hose samples wih oo small weighs, and spli hose samples wih larges weighs so ha he number of samples remains o be N. Sysemaic re-sampling is he adoped in he paper. Afer resampling, all paricle will have he same weigh 1/N, and hen he sae can be wrien X = 1 N X i (7) N i=1 The quaernion par of sae esimaion X should be normalized o represen an orienaion: q = q q (8)

5 and hen he esimaion should be updaed X = ( ( q ) T,( ω ) T,( d ) T) T. (9) Paricle filers have evolved ino many differen varieies over he pas few years. The key issue is he choice of proposal disribuion, which can bes approximae he arge poserior disribuion. Here we choose Sampling Imporance Re-sampling (SIR) for is simpliciy and effeciveness. Wih SIR, he proposal disribuion and he updae formula are reduced o he following forms: q(x X i 1,Z )=p(x X i ) π p(z X) i N π i =1 i (30) 4 Geomerical Consrains Modeling and Fusion In heory, afer applying sensor daa fusion o recursively esimae he sae vecor as in (9) for each arm segmen, he moion of upper limb is known. However, in pracice here are ineviable moion esimaion errors by using any esimaion mehod wih inerial measuremens and he errors are no zero mean Gaussian noise. Accumulaion of hese esimaion errors will resul in drif. Arm physical geomerical consrains should be aken ino accoun o validae and modify he esimaion resuls and o produce beer moion esimaion. 4.1 Geomerical Consrains Modeling The elbow of a healhy subjec can only admi wo degrees of freedom (DOF), namely, flexion/exension and pronaion/supinaion. Abducion/adducion of he elbow is nearly impossible, which means ha he adducion angle α is resriced o a very small angle. The adducion angle α is here defined as he angle beween he y-axis of he forearm and xy-plane of he upper arm. Given α is resriced o a very small angle, he y-axis of he forearm and he normal of he xy-plane of he upper arm should be almos orhogonal, which can be represened by do producion: Z 1 c,(q f,q u )=0=Y f Z u + V con1 (31) where V con1 is a Gaussian noise variable. Vecors Y f and Z u are y-axis and z-axis of forearm and upper arm coordinae sysem, which are resolved in he reference frame R: Y f = q f [0, 1, 0, 0] T q f 1 Z u = q u [0, 0, 1, 0] T q u 1 (3) where q f and q u represen he orienaion of forearm and upper-arm wih respec o he given reference frame R a ime Alhough he elbow join can admi flexion/exension movemens, he movemens are also consrained in a specific area. The flexion angle β for flexion/exension movemen can only be in a paricular range. Here we define flexion angle β as he angle beween he y-axis of he forearm and y-axis of he upper arm. I can be wrien as β 1 arccos(y f Y u ) β (33) However, (33) is only a necessary condiion bu no a sufficien condiion for flexion/exension movemens. The symmerical poin of forearm relaive o upper arm also saisfies wih (33). To be a sufficien condiion and remove he possibiliy of he symmerical poin, (33) should combine wih anoher equaion γ 1 arccos(y f X u ) γ (34) In (33) and (34), X u, Y f and Y u are calculaed similarly o (3). Consequenly, we can consruc anoher consrain measuremen equaion { Zc,(q f,q u 1 (q f,q u ) saisfies (33) and (34) )= (35) 0 oherwise There are similar consrains for pronaion/supinaion movemens. The screw angle of pronaion/supinaion movemens is also consrained in a paricular range. I can be described by he angle beween he z-axis of he forearm and z-axis of he upper arm φ 1 arccos(z f Z u ) φ (36) Similarly, we can consruc anoher measuremen equaion { Zc,(q 3 f,q u 1 (q f,q u ) saisfies (36) )= (37) 0 oherwise 4. Geomerical Consrains Fusion We have esablished hree arm physical geomerical consrain measuremens, as (31), (35) and (37). The measuremen equaions are obviously non-linear and non-gaussian; herefore, we adop he paricle filer approach o fuse he measuremens. Since a paricle filer implemens in a predicion-updae way a a ime, a random walk model is employed as a dynamical model o make up for he predicion-correcion form wih he measuremen equaions. The dynamical model is represened as x + = x + r (38) [ ] T where x = (q f ) T,(q u ) T, and r is he process noise and is assumed o be a Gaussian vecor. Afer applying sensor daa fusion, ses of paricles {(X f,i,1/n)} N i=1 and {(Xu,i,1/N)} N i=1 are used o approximae he poserior pdf of forearm sae vecors X f

6 and upper arm sae vecor X u respecively. Consrucing a new se of paricles {(x i,1/n)} N i=1, where xi = [ (q f,i of X f,i ] T ) T,(q u,i ) T, q f,i and q u,i are he quaernion pars and X u,i, respecively. x i is hen used o conduc one ime of quaernion based paricle filer ieraion. Here, SIR paricle filer is employed again. Obviously, he new paricles generaion is sraighforward, given he dynamic model is a random walk process wih Gaussian noise as 38. The difficuly of he SIR is o updae he weigh. Assume he consrains are independen wih each oher, he fusion process can be formulaed p(z 1:3 c, x +i )=p(z 1 c, x +i )p(z c, x +i )p(z 3 c, x +i ) (39) In he above equaion, p(zc, x 1 +i ) is sraighforward given he measuremen is coninuous. Here we propose he likelihood funcion for he binary measuremens Zc, and Zc, 3 p(z j c, x +i )= { 1 Z j c (x +i )=1 0 Z j c(x +i )=0 (40) (a) (c) (b) (d) where j =,3. In summary, he upper limb moion capure algorihm mainly consiss of wo pars: sensor daa fusion and geomerical consrains fusion. For each ime insance, he work-flow of our algorihm is as follows. 1. For each segmen of upper limb, run he corresponding quaernion based paricle filer o fuse among 3D acceleromeer and 3D micro-gyroscope sensors respecively.. According o he sensor daa fusion resuls of each segmen, consruc a new a se of paricles {(x i,1/n)} N i=1. 3. Based on {(x i,1/n)} N i=1, run he quaernion based paricle filer again o fuse geomerical consrains. 4. Updae sensor daa fusion resuls for nex ieraion. 5 Experimenal Resuls 5.1 Experimenal Seup Before we use he IMUs o sample inerial daa, each IMU mus be properly aached o he corresponding body segmens o reduce he orienaion difference beween sensor coordinae sysem and body segmen coordinae sysem as much as possible. For upper limb, we keep he upper limb in cerain predefined posures, and make sure 1) he z-axis of he sensor aached o upper arm is orhogonal o he laeral side of he upper arm; ) he z-axis of he sensor aached o forearm is orhogenal o he palm side of he forearm; 3) he y-axis of he forearm sensor is orhogonal o he z-axis of upper arm sensor (e) Figure 3: The upper limb moion capure resuls 5. Experimenal Demonsraion The performance of our proposed algorihm is evaluaed by exensive experimens: when he upper limb performs ypes of acions, a video camera records he acion for comparison wih our moion capure resuls, which are visualized using 3D Human Model and animaions. A subjec is asked o do he following ask: he upper limb moves inward o he body ogeher, forearm moves upward while he upper arm keeps sill, and hen reverse he movemens o recover he iniial posure. The enire acion is repeaed hree imes. Figure 3 clearly shows he human upper limb moion capure resul of one rip of he movemens, and figure 3(a) is he iniial posure while figure 3(e) shows he end posure before reversing. Figure 3(b), 3(c) and 3(d) describe he inermediae posures. As we can see from he figures, he 3D human moion animaion moves simulaneously wih ha of he subjec. I shows ha our proposed hierarchical informaion fusion algorihm can capure he upper limb moion wih reasonable moion esimaion accuracy. Figure 4 shows he adducion consrain of elbow join. In he figure, i describes he angle beween he y-axis of he forearm and he z-axis of he upper arm. The blue one indicaes he resul from he sensor daa fusion wihou geomerical consrains fusion, while he green one shows he resuls wih he consrains. I is obvious ha he resuls

7 Adducion Consrain (Deg) Time( 40ms) Figure 4: The adducion consrain of elbow join (a) Figure 5: The flexion/exension consrain of elbow join from he sensor daa fusion wihou geomerical consrains fusion break he anaomical consrain, which is unaccepable. On he oher side, he consrains fusion resuls are reasonable, and keeping he adducion movemen in a small space. Figure 5 shows he flexion/exension consrain of elbow join. Afer he enire acion repeaed hree ime, he upper limb recovers he iniial posure. Figure 5(a) indicaes he resuls from he sensor daa fusion wihou geomerical consrains fusion, while figure 5(b) shows he resuls wih he consrains. I is clear ha he resuls from he sensor daa fusion wihou geomerical consrains fusion is no reasonable, and he exension of forearm exceeds he poenial range of forearm movemens. Geomerical consrains fusion algorihm revises he errors and makes he esimaion more reasonable. The proposed upper limb moion capure algorihm has been esed exensively by various movemens, by comparing he reconsruced 3D avaar wih he video of he real arm movemens, i shows ha our proposed upper limb moion capure algorihm works well wih reasonable moion esimaion accuracy. 6 Conclusion and Fuure Work We have developed a novel hierarchical informaion fusion algorihm for human upper limb moion capure. Our mehod represens orienaions of upper limb segmens in (b) 1710 quaernion, which is compuaionally effecive and able o avoid singulariy problem. To address he nonlinear human body segmen moion, a paricle filer is proposed o fuse among 3D acceleromeer and 3D micro-gyroscope sensors o esimae upper limb moion recursively. Based on he paricle filer fusion resuls, we presen a novel soluion by modeling he geomerical consrains in elbow join and fusion of hese consrains o improve he esimaion accuracy. Hierarchical 3D human moion model are hen developed for real-ime animaion. The good experimenal resuls have shown he feasibiliy of he proposed moion capure and analysis algorihm. Our furher work will be on he more accurae sensor coordinae sysem calibraion mehod, muliple moion models o furher improve esimaion accuracy, and comprehensive experimens o evaluae he performance and accuracy of he moion capure sysem. References [1] A. Kapur, A. Kapur, N. Virji-Babul, G. Tzaneakis, and P. Driessen. Gesure-Based Affecive Compuing on Moion Capure Daa. Lecure Noes in Compuer Science, 3784:1, 005. [] L. Hamilon, R. Franklin, and N. Jeffery. Developmen of a universal measure of quadrupedal forelimbhindlimb coordinaion using digial moion capure and compuerised analysis. BMC Neuroscience, 8:77, 007. [3] H. Dejnabadi, B. Jolles, and K. Aminian. A new approach o accurae measuremen of uniaxial join angles based on a combinaion of acceleromeers and gyroscopes. Biomedical Engineering, IEEE Transacions on, 5(8): , 005. [4] R. Hyde, L. Keeringham, S. Neild, and R. Jones. Esimaion of Upper-Limb Orienaion Based on Acceleromeer and Gyroscope Measuremens. Biomedical Engineering, IEEE Transacions on, 55( Par 1): , 008. [5] D. Vlasic, R. Adelsberger, G. Vannucci, J. Barnwell, M. Gross, W. Mausik, and J. Popović. Pracical moion capure in everyday surroundings. ACM Transacions on Graphics (TOG), 6(3), 007. [6] X. Yun and E. Bachmann. Design, Implemenaion, and Experimenal Resuls of a Quaernion-Based Kalman Filer for Human Body Moion Tracking. Roboics and Auomaion, IEEE Transacions on, (6):116 17, 006. [7] B. Xsens Technologies. Moven-inerial moion capuring. [8] E. Foxlin, M. Harringon, I. Inc, and M. Burlingon. WearTrack: a self-referenced head and hand racker for wearable compuers and porable VR. Wearable Compuers, The Fourh Inernaional Symposium on. pages , 000.

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