Ana lyzing Mo to ri and Phys io lo g i a l Data in De s rib ingUppe r Extrem ity Mo veme nt in the Ag e dGaurav N. PradhanUniversity of Texas at DallasDept. of Computer S ien eRi hardson, TX 75083gauravutdallas.edu Navzer EngineerUniversity of Texas at DallasBehavioral and Brain S ien esRi hardson, TX 75083navzerutdallas.edu Mihai NadinUniversity of Texas at DallasInstitute for Resear h inAnti ipatory SystemsRi hardson, TX 75083nadinutdallas.eduBalakrishnan PrabhakaranUniversity of Texas at DallasDept. of Computer S ien eRi hardson, TX 75083prabautdallas.eduABSTRACTCognitive fun tions, motori expression, and hanges in phys-iology are often studied separately, with little attention tothe relationships, or orrelations, among these entities. Inthis study, we implement an integrated approa h by ombin-ing motion apture (a tion) and EMG (physiologi al) pa-rameters as syn hronized data streams resulting from thea tion and asso iated physiologi al data. Our experimentswere designed to measure the preparatory movement apa-bilities of the upper extremities. In parti ular, measurementof hanges in preparatory a tivity during the aging pro essare of interest to us, as the attempt is to develop means to ompensate for loss of adaptive apabilities that aging en-tails. To a hieve this goal, it is ne essary to quantify prepa-ration phases (timing and intensity). We measured motion apture and EMG parameters when subje ts raised theirarms without onstraint ( ondition one) and raised theirarms while holding a ball (se ond ondition). Furthermore,on omparing aging and young parti ipants, we onrmedthat with aging the temporal relationships between a tualmovement and the pre eding EMG signal hange.Categories and Subje t Des riptorsJ.3 [Life and Medi al S ien es℄: [Health, Medi al Infor-mation systems℄General TermsIntegration analysisPermission to make digital or hard opies of all or part of this work forpersonal or lassroom use is granted without fee provided that opies arenot made or distributed for prot or ommer ial advantage and that opiesbear this noti e and the full itation on the rst page. To opy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior spe i permission and/or a fee.Copyright 200X ACM X-XXXXX-XX-X/XX/XX ...$5.00. KeywordsEle tromyogram, motion apture, feature extra tion, multi-variate analysis of varian e, fa tor analysis1. INTRODUCTIONUntil re ently, the dominant view was that aging was as-so iated with irreversible ognitive and motor de line. Poorperforman e on ognitive and motor tasks and subsequentdiAE ulty in performing goal-dire ted behavior are prevalentamong the elderly. As the upper extremity movements arepervasive in our everyday lives, the impa t of aging on themis of spe ial relevan e. This pertains to su

essful a tionsinvolving arm movements, as well as to the possibility of a idents (abrupt hange of position an result in falls). Thus,it be omes very important to apture and analyze the quan-titative des ription of the motori behavior of the upper ex-tremity movements a ross various age groups.However, evaluating the aging ee t in upper extremity move-ments has some strong hallenges in form of variety, om-plexity, and the range of motions [13℄. Some resear hers [14,2, 7, 9℄ have studied upper extremity movements, but theyhave evaluated the movements on a " oarse-grained" basis.For apturing how aging ae ts movement, it is ne essary toget down into "ne-grained'" analysis and be ome sensitiveto spe i and minor hanges in motori expression. Thereason being, through brain plasti ity, we want to ompen-sate for the loss, and this has to be spe i .In this paper, we aim to quantitatively analyze the har-a teristi s of simple, upper extremity movement of raisingthe arms with two dierent onditions a ross young and oldparti ipants. To arry detailed and systemati analysis, wein orporated the two sophisti ated te hniques su h as: 3D motion apture that aid in mapping the omplexhuman motion in the three dimensional (3D) spa e.Here, a parti ipant wears spe ial markers that an betra ked by ameras in the 3D spa e. Ele tro-myograms that tra k the ontra tions of dier-ent mus les ausing the body joints to move. A surfa e EMG sensor monitors mus le ontra tion during bodymovements.The integrated analyses of the body motions based on 3Dmotion apture data and EMG helps in understanding the orrelation between dierent mus le a tions and the orre-sponding body joint movements a ross various age groups.Also, su h kind of database and the asso iated analyses stim-ulate several appli ations in luding: (a) designing rehabili-tation programs for patients with restri ted movements (dueto a

idents or illness, stroke, arthritis) and other neurologial populations in luding dementia, Parkinson's disease, et .(b) developing adaptive neuro-prostheti devi es that im-prove o-ordination and provide smoother and easier move-ments.However, integrated evaluation and analyses of 3D motion apture data and the asso iated EMG's pose several hal-lenges as well. First is the variation in speed and traje toryof the motions. Even though attempts an be made to on-trol the duration of a task, motion speed an vary fromparti ipant to parti ipant, as well as for the same parti i-pant. These variations an ause wide u tuations in the3D motion apture data. Ele tro-myograms an also showwide variations due to the dieren es in human physiologi al hara teristi s. Figure 1: The nal posture of normal raise arm a -tivity.2. RELATED WORKWhen young individuals raise their arms, their leg musles ontra t to ompensate for the hange in enter of grav-ity to prevent loss of balan e. In aging, this ompensationexpe ted when a person raises his/her arms diminishes andthe individual loses his/her balan e by raising the arms. Re-sear h shows that the ele tromyography (EMG) signal o urs before the a tion [1, 5℄. Studies have also do umentedthe ee ts of age on anti ipatory EMG a tivity during a va-riety of motor tasks and postural adjustments [8℄ and generalde line of adaptive apabilities [10℄. One study examined an-ti ipatory tripping behavior in young and old subje ts andfound that slightly in reased mus le a tivity was observedin tibialis anterior and soleus mus les in older subje ts [11℄. In addition to simple movements, older subje ts make useof additional ( ompensatory) limb movements to maintainbalan e during a tions su h as walking, at hing a ball orraising arms and re e t a de line in maintenan e of pos-ture and stability. In [12℄, authors revealed the performan edieren es between the three dierent age ategories by ap-plying univariate analysis of varian e and prin ipal ompo-nent analysis on the extra ted parameters from a single jointsegment and mus le using syn hronized motion apture andEMG data. While this experiment provides data for a singlejoint segment and mus le, it is likely that more informationwill be generated by integrating data from multiple jointsand mus les. Over the years, many behavioral parametershave been used to study de line in sensory-motor and og-nitive performan e. The most ommonly used measures inlude rea tion time [3, 6, 15℄, movement time and velo ityof movement [4℄. Many of these studies fo us almost ex lu-sively on the kinemati s and biophysi al aspe ts of motion.However, preparation of movement is an important ompo-nent that has been addressed in only a few aging studies.Our study addresses this proa tive omponent in aging inthe form of syn hronized motion apture and EMG datastreams during the a tion of raising the arms.3. MATERIAL ANDMETHODS 0 100 200 300 400 500 −500 0 500 1000 m m 0 100 200 300 400 500 0 1000 2000 m m /s e c 0 100 200 300 400 500 0 2 4 6 x 10 −5 V 0 100 200 300 400 500 0 0.5 1 1.5 x 10 −5 V 0 100 200 300 400 500 0 2 4 6 x 10 −5 Frame # (120 frames per second) V 0 100 200 300 400 500 0 1 2 3 x 10 −5 Frame # (120 frames per second) V Triceps (post−processed) Biceps (post−processed) Triceps (raw) Biceps (raw) X−axis Y−axis Z−axis Velocity CurveWrist Joint Wrist Joint (a) (b) (c) (d) (e) (f)Figure 2: (a), ( ), (e): Raise arm a tivity with or-responding motion apture data for right wrist jointand syn hronous EMG a tivity in mus les bi epsand tri eps, (b): Velo ity urve for the wrist joint,(d), (f): Post-pro essed EMG signals from bi epsand tri eps respe tively.3.1 Subje t sele tion30 healthy parti ipants were re ruited for this study. Theage of the subje ts ranged from 20-80 years. Data pre-sented here was analyzed from 20 subje ts due to te hni aldiAE ulties during the re ording sessions (missing markers, −15 −10 −5 0 5 10 15 20 25 30 Fra me s ( 12 0 f ram es /se co nd ) Time Lag b/w velocity of hand and EMG mucle activity −25 −20 −15 −10 −5 0 5 10 Fra me s ( 12 0 f ram es /se co nd ) Onset Diff. b/w EMG muscle and wrist joint movement 1 2 3 4 5 6 7 8 9 x 10 −3 mV 2 Energy of the EMG muscle 20 25 30 35 40 45 50 55 Fra me s ( 12 0 f ram es /se co nd ) Time diff. b/w onset of EMG and peak of velocity 18 20 22 24 26 28 30 32 34 Fra me s ( 12 0 f ram es /se co nd ) Time diff. b/w onset of EMG and first EMG peak 50 60 70 80 90 100 110 120 Hz Median Frequency Biceps Triceps Biceps Triceps Biceps Triceps Biceps Triceps Biceps Triceps Biceps Triceps Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ballFigure 3: The error-bars for extra ted features of bi eps and tri eps a ross young and old for both experi-ments.syn hronization diAE ulties, poor EMG signal/noise ratio).The per entage of females in the study was 40%. None ofthe parti ipants had overt neurologi , psy hiatri or ogni-tive dysfun tion (e.g., stroke, dementia, Parkinson's disease,et ). All measurements were re orded in the Motion Cap-ture Lab at the University of Texas at Dallas. The studywas approved by the Institutional Review Board at the Uni-versity of Texas at Dallas. Subje ts signed a onsent formbefore the start of ea h session.3.2 Motion apture a quisition and analysisMotions were aptured in the Motion Capture Lab equippedwith 16 ameras (Vi on Systems). A parti ipant wore asuit of non-re e tive material and about 44 markers wereatta hed over the body overing ea h joint. Pla ement ofmarkers orresponded to the area of interest. The data fromthe motion apture ameras were a quired in the form offrames at a speed of 120 frames per se ond. A data station(i.e., the PC with the motion apture software) ombinesthe data from all ameras into one matrix (per parti ipant).Ea h row in the matrix orresponds to 1 frame of data. Fora single human motion of, let us say 10 se onds, the ma-trix onsist of 1200 rows. Sin e human body has 19 majorsegments (head, shoulder, hand, et ) and ea h segment hastranslation (3 olumns one for ea h dimension X, Y, and Z)and rotation (3 olumns for X, Y, and Z), we have a totalof 114 olumns in the motion apture data matrix.3.3 EMG a quisition and post-pro essingEMG Ag-Cl ele trodes were used to re ord mus le a tiv-ity of limbs. From these signals, we extra ted the time ofonset, peak laten y, amplitude and other parameters from12 mus les (6 on either side). On the upper extremities,four ele trodes were pla ed on bi eps, tri eps, and forearm exor and extensor mus les. On the lower extremity, twoele trodes were pla ed on the tibialis anterior and the gas-tro nemius mus les respe tively. The EMG signal was am-plied and band-pass ltered (20-450 Hz) by the wireless system (Delsys, Boston) with a sampling rate set to 1000Hz. Further, the signal was full-wave re tied and lteredusing 4th order, 10Hz low-pass uto Butterworth lter.3.4 Integratingmotion apture and EMGdatastreamsMotion apture and EMG data streams were syn hro-nized. MATLAB (Mathworks) served as the main ontrollerthat sent a trigger to EMG and motion apture systems tostart simultaneous a quisitions via a 'trigger module' and ommuni ated with MATLAB via the Data A quisition Tool-box (Mathworks). The pro essed EMG signal was down-sampled to 120 Hz to make it uniform with the motion ap-ture system whi h aptures data at 120 samples per se ond.Figure 2 (a), ( ), and (e) shows the syn hronous 3D mo-tion apture data for the right wrist joint and orrespondingEMG a tivity in mus les bi eps and tri eps for normal, raisearm a tivity. Figure 2 (d), (f) are the post-pro essed bi epsand tri eps mus ular a tivity respe tively (as dis ussed inSe tion 3.3). Figure 2 (b) is the velo ity urve for the rightwrist joint.3.5 Experimental designSubje ts were divided into 2 groups: Old (51-80), andYoung (20-50). Subje ts performed upper extremity move-ment, in whi h they have to raise the both arms up to shoul-ders (approximately 90o) as shown in Figure 1 in response toa visual ue displayed on the s reen. For every trial, we havea initial baseline a tivity by displaying ue \Ready?" on thes reen where subje t be omes idle and pays attention to thes reen, and then after a span of 2-3 se onds follows the vi-sual ue \Raise!" where he/she starts a tivity of raising thearms. We designed preparatory time frame, to have ontrolon subje t's a tivity and to make sure he/she doesn't per-form unne essary movements that may give false positives.For ea h subje t we aptured the raise arm a tivity withtwo onditions. (1) Normal, free raise arm movement; (2)Raise arms by holding an obje t (in our ase, football (so - −15 −10 −5 0 5 10 15 20 Fra me s ( 12 0 f ram es /se co nd ) Time Lag b/w velocity of hand and EMG mucle activity −10 −8 −6 −4 −2 0 2 4 6 8 Fra me s ( 12 0 f ram es /se co nd ) Onset Diff. b/w EMG muscle and wrist joint movement 0 0.002 0.004 0.006 0.008 0.01 mV 2 Energy of the EMG muscle 25 30 35 40 45 50 55 Fra me s ( 12 0 f ram es /se co nd ) Time diff. b/w onset of EMG and peak of velocity 19 20 21 22 23 24 25 26 27 28 Fra me s ( 12 0 f ram es /se co nd ) Time diff. b/w onset of EMG and first EMG peak 50 100 150 Hz Median Frequency Flexor Extensor Flexor Extensor Flexor Extensor Flexor Extensor Flexor Extensor Flexor Extensor Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ball Young Raise−Arm Old Raise−Arm Young Raise−Arm w/ ball Old Raise−Arm w/ ballFigure 4: The error-bars for extra ted features of exor and extensor a ross young and old for both experi-ments. er) [Weight 410-450 gms℄)) with both hands. We hosenso

er ball be ause of its familiarity and dimensions ( irumferen e = 27-28 in hes), whi h makes it omfortable toraise. Moreover, its weight is ideal not only to handle byany subje t regardless of age, but also suAE ient to put anextra for e on mus ular a tivity as ompared to just raisearms. We olle ted 10 trials ea h for both onditions fromevery subje t. So, as we are analyzing data from 20 subje ts(9 young + 11 old), we have total of 400 trials or motionsperformed during our experiments.3.6 Feature Sele tion and Extra tionFor raise arm a tivity, we mainly fo us on a tive, upperextremity mus les su h as bi eps, tri eps, exor, extensoralong with the 3D-movement of the wrist joint. To quan-tify the proa tive omponents in the aging it is ne essary toidentify the temporal inter-relationships between the synhronous mus ular and physi al joint a tivities during thepreparatory phase of raising a tivity. Thus, we extra tedfollowing quantitative features from ea h EMG mus le a -tivity with respe t to the wrist joint for ea h trial of raisingthe arms as follows:1. Time lag between relative velo ity of wrist movementand EMG mus ular a tivity.2. Onset dieren e between EMG mus le and wrist jointmovement.3. Energy of the EMG mus ular a tivity.4. Time dieren e between an onset of EMG mus le andthe time at peak velo ity of wrist joint5. Time dieren e between an onset of EMG mus le andthe time at rst peak of EMG mus ular a tivity.6. Median Frequen y of the mus ular a tivity.As the raising of arm a tivity is a fun tional movement, we annot ontrol the lo al speed of the arms a ross dierent subje ts. The above rst ve parameters an be easily inter-preted from the illustrated Figure 2 (b) and (d) for bi epsand velo ity of wrist joint (i.e. hand). The last parameter isthe frequen y-domain related parameter, whi h we an mea-sure by applying Fast Fourier Transform (FFT) to the timeseries of EMG a tivity. Along with temporal parameters, itis important to study the ee t of aging on both raise arm onditions using frequen y hara teristi of the mus ular a -tivity.Figure 3 and 4 shows the error bars for the extra ted featuresfrom the EMG mus les bi eps-tri eps and exor-extensor re-spe tively. The error bar for the ea h feature of the EMGmus le indi ate the mean and standard deviation for theyoung and old parti ipants a ross both raise arm onditions,normal raise arm and raise arm with ball. As we have 9young parti ipants, in ea h group of \young raise arm" and\young raise arm with ball" we have 90 measurements forthe orresponding feature for ea h EMG mus le. Similarly,for 11 old parti ipants we have 110 measurements in ea hgroup of \old raise arm" and \old raise arm with ball".4. DATA ANALYSISThe a quisition of the raise-arm experiment with two on-ditions on two subje t ategories (i.e. young and old) leadus to four dierent kind of groups su h as (1) Young doingnormal raise arm, (2) Old doing normal raise arm, (3) Youngdoing raise arm with ball, (4) Old doing raise arm with ball.Moreover, as seen from Se tion 3.6, for every trial of anyparti ipant in any group, we have set of extra ted featuresthat gives temporal relationships between dierent mus lesand movement of the joints.To identify the dieren es between these dierent groups foranalyzing the aging ee t in upper extremity movements, weneed to perform analysis of varian e on the extra ted fea-tures a ross these four groups. And, as we have multiplefeatures for ea h trial, we form a multidimensional measure-ment spa e in whi h ea h trial is represented as a feature ve tor. Hen e, we apply multivariate analysis of varian e,where extra ted features be ome the dependent variablesand the groups be ome independent variables. The mul-tivariate analysis of varian e derives two terms in form ofmatri es as follows: sums of squares and ross-produ ts of deviation forea h trial's feature ve tor from their respe tive groupmean, in short, within-groups sum of squares and ross-produ ts matrix (E). sums of squares and ross-produ ts of deviation forgroup mean from the grand mean, in short, between-groups sum of squares and ross-produ ts matrix (H).These two matri es an be used to al ulate Wilks' lambda() as a test statisti in multivariate analysis of varian eto investigate the dieren es between the means of groupson a ombination of extra ted features.  is al ulated asfollows, Wilks0 lambda = jEjjH + Ej (1)Here, the determinant of the withinlusters sums of squaresand ross produ ts matrix E is divided by the determi-nant of the total sum of squares and ross produ ts matrixT = H + E. To investigate the data for multivariate dif-feren es, the null hypothesis that indi ates no dieren es inthe ve tor of mean features a ross groups is tested. If H islarge relative to E, then jH + Ej will be large relative tojEj and there is maximum separation between the groupsand minimum separation within the groups with respe t tothe entire set of quantitative features. Thus, we ould re-je t the null hypothesis if  is small ( lose to zero) be ausethere is a signi ant dieren e between the set of means offeatures among the groups. Also, in multivariate analysis ofvarian e,  statisti an be transformed approximately tomore familiar F -distribution whi h an represent the signif-i an e of dieren e between lusters by F -value and degreeof freedoms (df). The higher values of F indi ates greaterdieren es in the groups and reje tion of null hypothesis.Further, we derived a new set of variables alled anoni alvariables that are linear ombinations of the original depen-dent variables su h that we an a hieve maximum separa-tion between the groups and minimum separation withinthe groups. On eigen-de omposing the matrix HE 1 we get oeAE ients for the linear ombinations of the original depen-dent variables in form of eigen ve tors. On proje ting theoriginal features of trials on the eigenve tors ofHE 1 we ob-tain anoni al variables that represent the maximum sepa-ration between groups. Thus, applying multivariate analysisof varian e on the extra ted, quantitative features we ouldevaluate the aging ee t on the upper extremity movementsthrough varying onditional experiments.Now, our next stage is to ompare and analyze the re-lationship between the extra ted features for two dierent ondition of raise arm (normal and with ball). We ombinethese two sets of features into a ommon stru ture alled\ ompromise spa e" whi h is then analyzed using prin ipal omponent analysis to reveal the ommon stru ture betweenthe young and old parti ipants. Hen e, for ea h raise arm ondition, we take the average measures of all extra ted fea-tures for every parti ipant a ross orresponding trials. Thatmeans, in both raise arm onditions, every parti ipant isrepresented in form of average ve tor of extra ted features. Thus, we form two ondition matri es (Tnormal and Tball)for raise arm experiment, where in ea h matrix, rows rep-resent the parti ipants and olumn represents the averagevalue of extra ted features (i.e. T pfnormal and T pfnormal, wherep = number of parti ipants (young + old) and f = numberof features). Both matri es are post-pro essed by enteringand normalizing the olumn ve tors as they may have het-erogenous range of values, and analysis is arried further asfollows:1. Ea h matrix Tnormal and Tball denes inherently astru ture for the performan e of the young and oldparti ipants with respe t to the orresponding raisearm ondition, whi h an be derived by omputing thes alar produ ts between parti ipants. The orrespond-ing s alar produ t matri es are denoted as Snormal andSball respe tively.2. The weighted sum of both matri es gives ompromisematrix as follows,MC = 0:5 Snormal + 0:5 Sball (2)As we have only two onditions to analyze, we dis-tribute the weight uniformly among the s alar produ tmatri es.3. For analyzing the ompromise matrixMC , we use prinipal omponent analysis that explores the overall per-forman e of the parti ipant with respe t both raisearm onditions. Sin e, ompromise matrix is also as alar produ t matrix, its PCA is given as,MC = Q ^QT (3)The fa tor s ores (i.e. the proje tion of the rows onthe prin ipal omponents of the analysis of MC) areobtained as, F = Q^ 12 (4)In this matrix F , ea h row orresponds to the parti i-pant and ea h olumn orresponds to the omponent.The ompromise spa e is formed by rst few prin ipal omponents of the fa tor s ore matrix that arry totalvarian e of 85   90%. And the fa tor s ores for ea hparti ipant that are mapped in ompromise spa e rep-resent the overall performan e of the parti ipant withrespe t to both raise arm onditions.5. RESULTS AND DISCUSSIONSIn this paper, we are analyzing the aging ee t on theupper extremity movements by omparing raise arm exper-iment performed by young (20-50) and old (51-80) parti -ipants in two dierent onditions (normal and with ball).In this se tion, we will present the results of two types ofanalysis, multivariate analysis of varian e that expresses thedieren e between two age groups a ross both ondi-tions of raise arm a tivity. fa tor analysis that analyzes the fa tors that are re-sponsible for distinguishing the two age groups. Ee t  F pAge  Condition 0.11 9.64 < 0.01Age (Young or old) 0.44 2.76 < 0.01Condition(normal or Ball) 0.39 3.29 < 0.01Table 1: Result for multivariate analysis of varian efor dieren es between Age  Raise arm ondition,Age, and Raise arm Condition.5.1 Multivariate analysis of varian eIn Table 1, the rst row indi ates that there is a signiant intera tion between the aging ee t and the two raisearm onditions with multivariate F-value = 9:64. This resultis well supported, when 2-way MANOVA was ondu ted onthe trials of all parti ipants with both onditions. There wasa signi ant multivariate main ee t for age ( = 0.44, F =2.76) when both raise arm onditions were merged under ageee t. Also, there was signi ant dieren e existed withinthe raise arm onditions for all the extra ted features ( =0.39, F = 3.29). These results suggests that, the behaviorof the EMG mus le asso iated with upper extremities haverea tion on aging.In order to interpret the results of the multivariate analysis Figure 5: The proje tion of individual parameterve tor per trial for ea h parti ipant a ross two ex-periments in anoni al spa e.of varian e on the extra ted features a ross age and raisearm onditions, we derive anoni al variables as dis ussedin Se tion 4, that represent ea h trial of the parti ipant inlow-dimensional anoni al spa e. Figure 5 shows the two anoni al variables for ea h trial a ross four groups with or-responding entroids. The virtual, approximate boundariesindi ating four groups shows that there is maximum dis rim-ination between the groups in dened anoni al spa e. InFigure 5, the rst anoni al variable dierentiates betweenthe two raise-arm onditions (i.e. opposes the ee t of thenormal raise arm and raise arm with ball). While, se ond anoni al variable dierentiates a

ording to age (i.e. op-poses the ee t of young and old). Thus, aging ee t a rossboth raisearm onditions an be easily interpreted in the anoni al spa e. Also, to represent ea h individual parti ipant in anoni al Figure 6: The proje tion of mean parameter ve -tor for ea h parti ipant a ross two experiments in anoni al spa e.spa e instead of ea h trial of every parti ipant, we took themeans of all trials for every parti ipant for every raise arm ondition and applied multi-variate analysis of varian e withfour groups and ea h entry in the groups was representingthe ve tor of means of extra ted features. Figure 6, showsthe four groups, with two anoni al variables representingea h parti ipant in four dierent groups. The anoni al vari-able 2 learly dis riminates the old (positive side) and young(negative side) parti ipants. The performan e of the youngparti ipants varies more a ross anoni al variable 1 in tworaise arm onditions as ompared to old parti ipants.5.2 Fa tor analysis −4 −2 0 2 4 6 8 −4 −3 −2 −1 0 1 2 3 4 20 25 26 27 35 36 38 49 50 56 56 60 62 62 63 67 70 77 1st Principal Component (42.2 %) 2n d P ri n ci p al C o m p o n en t (1 7. 9 % ) Old Participants (Age: 51 − 80) Young Participants (Age: 20 − 50) Figure 7: The proje tion of ea h parti ipant in the ompromise spa e. (The numbers indi ate `age' ofthe parti ipants.)The two ondition matri es Tnormal and Tball ontains theaverage measures of all extra ted features for ea h youngand old parti ipants a ross respe tive trials. Figure 7 shows the ompromise spa e in rst two prin ipal omponent axesthat reveals the ommon stru ture between young and oldparti ipants. Ea h point (i.e. fa tor s ore from Equation 4)mapped in ompromise spa e represents the ombined per-forman e of the orresponding parti ipant a ross two raisearm onditions i.e. normal raise arm and raise arm withball. As seen from Figure 7, se ond prin ipal omponent(that explains 17:9% of total varian e) opposes most of theyoung parti ipants from the old parti ipants. Due to realdata sets, some parti ipants may show dierent behavior as ompared to other parti ipants in the same group.In addition, we also need to interpret the behavior of the −2 −1 0 1 2 −4 −3 −2 −1 0 1 2 3 4 20 25 35 38 49 56 60 62 62 67 70 RA20 RA25 RA35 RA38 RA49 RA56 RA60 RA62 RA62 RA67 RA70 RAW20 RAW25 RAW35 RAW38 RAW49 RAW56 RAW60 RAW62 RAW62 RAW67 RAW70 1st Principal Component (42.2 %) 2n d P ri n ci p al C o m p o n en t (1 7. 9 % ) RAi − Raise Arm for participant with age 'i' RAWi − Raise Arm with Ball for participant with age 'i' Figure 8: The proje tion of some parti ipants alongwith their proje tion of ea h performan e in bothexperiment in the ompromise spa e.every parti ipant for ea h ondition in the same spa e. This an be a hieved by proje ting the s alar produ t matri esSnormal and Sball for ea h raise arm ondition onto the om-promise spa e. Figure 8 shows the proje tion of two raisearm onditions for six older and ve younger parti ipants inthe ompromise spa e. The proje tion of the parti ipant isthe entroid for the orresponding proje tions of two raisearm onditions. To make it simple for interpretation, in Fig-ure 8, we have drawn line linking the position of ea h par-ti ipant to it's orresponding positions for both raise arm onditions in the ompromise spa e.The original extra ted features an be integrated into the ompromise analysis by omputing loadings using the stan-dard approa h similar to PCA. The loadings are the orre-lation between the original features and the fa tor s ores.Figure 9 and Figure 10 shows the ir le of orrelation ob-tained for both raise arm onditions i.e. normal and withball respe tively. For the sake of representation, in Figure9 and Figure 10, we show the orrelation of the extra tedfeatures for individual mus les separately for the respe tiveraise arm onditions. The features numbered from 1 to 6are in same order as mentioned in Se tion 3.6. Generally,any orrelation above 0.7 is onsidered signi ant, but aswe work on real-life EMG data set that is prone to noise,we an lower the signi an e level to 0.4. Now in Figure9, for bi eps, we have energy of this mus le (3) negatively orrelated with se ond prin ipal omponent. That means,the parti ipants having high bi eps energy signals will lie towards the negative dire tion of prin ipal omponent axis 2in ompromise spa e. As seen from Figure 7, mainly the oldparti ipants lie in this area. This result is onsistent withthe observation that old parti ipants put in lot of for e forthe goal-dire ted, upper extremity movements as omparedto younger ones. Similarly, we an observe the orrelationsof the dierent features for the both raise arm onditions tothe prin ipal omponent axes. Using these orrelations andposition of parti ipants in the ompromise spa e, we an in-terpret the ee t of aging on the orresponding features ofthe EMG mus les. −1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 1 2 3 4 5 6 Biceps −1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 1 2 3 45 6 Triceps −1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 12 3 4 5 6 Flexor −1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 1 2 3 4 5 6 Extensor Figure 9: The ir les of orrelation for normal, raise-arm experiment representing loadings for ea h handsensor.6. CONCLUSIONIn this paper, we evaluated the aging ee t on upper ex-tremity movements by ondu ting simple raise arm exper-iments a ross young and aged parti ipants under two dif-ferent onditions, (a) normal raise arm, and (b) raise armwith a football. We performed quantitative analysis on these onditions by extra ting the timing and intensity related fea-tures from the syn hronous data streams of motion aptureand ele tromyogram sensors. This integrated analysis of up-per extremity movements based on 3D motion apture dataand EMG gave us the knowledge of interesting orrelationsbetween dierent mus le a tions and the orresponding bodyjoint movements related to upper extremity a ross youngand old age groups.We tested the dieren es in terms of extra ted features a rosstwo age groups and also a ross two raise arm onditionsusing multivariate analysis of varian e. The results shownthat, there was a signi ant dieren e (p < 0.01) for all threekinds of ee ts su h as age, ondition for raise arm, andage  ondition. We also performed anoni al analysis, toshow the maximum dis rimination between dierent groupsby a hieving maximum separation between the groups and −1 −0.5 0 0.5 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 1 2 3 4 5 6 Biceps −1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 1 2 3 4 5 6 Triceps −1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 1 2 3 4 5 6 Flexor −1 −0.5 0 0.5 1 −1 −0.5 0 0.5 1 Correlation w/ Principal Component # 1 C o rr e la ti o n w / P ri n c ip a l C o m p o n e n t # 2 1 2 3 4 5 6 Extensor Figure 10: The ir les of orrelation for raise-armwith ball experiment representing loadings for ea hhand sensor.minimum separation within the groups. 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