Combined Vision and Wearable Sensors-based System for Movement Analysis in Rehabilitation

Transcription

1 Focus Theme Originl Articles 1 Comined Vision nd Werle Sensors-sed System for Movement Anlysis in Rehilittion Sofij Spsojević 1,2,3 ; Tihomir V. Ilić 4 ; Slđn Milnović 5 ; Veljko Potkonjk 1 ; Aleksndr Rodić 2 ; José Sntos-Victor 3 1 School of Electricl Engineering, University of Belgrde, Belgrde, Seri; 2 Mihilo Pupin Institute, University of Belgrde, Belgrde, Seri; 3 Institute for Systems nd Rootics, Instituto Superior Técnico, Universidde de Liso, Lison, Portugl; 4 Deprtment of Neurophysiology, Medicl Fculty of Militry Medicl Acdemy, University of Defense, Belgrde, Seri; 5 Institute for Medicl Reserch, Deprtment of Neurophysiology, University of Belgrde, Seri Keywords Rehilittion, movement nlysis, Kinect, werle sensors Correspondence to: Sofij Spsojević Mihilo Pupin Institute Volgin Belgrde Seri E-mil: sofij.spsojevic@pupin.rs Summry Bckground: Trditionl rehilittion ses - sions re often slow, tedious, disempowering nd non-motivtionl process, supported y clinicl ssessment tools, i.e. evlution scles tht re prone to sujective rting nd imprecise interprettion of ptient s performnce. Poor ptient motivtion nd insufficient ccurcy re thus criticl fctors tht cn e improved y new sensing / processing technologies. Ojectives: We im to develop portle nd ffordle system, suitle for home rehilittion, which comines vision-sed nd werle sensors. We introduce novel pproch for exmining nd chrcterizing the rehilittion movements, using quntittive descriptors. We propose new Movement Performnce Indictors (MPIs) tht re extrcted directly from sensor dt nd quntify the symmetry, velocity, nd ccelertion of the movement of different ody/hnd prts, nd tht cn potentilly e used y therpists for dignosis nd progress ssessment. Methods: First, set of rehilittion exercises is defined, with the supervision of neu - rologists nd therpists for the specific cse of Prkinson s disese. It comprises full-ody movements mesured with Kinect device nd fine hnd movements, cquired with Methods Inf Med 17; 56: received: Mrch 6, 16 ccepted in revised form: Octoer 22, 16 epu hed of print: Funding This work is prtilly funded y the Ministry of Eduction, Science nd Technology Development of the Repulic of Seri under the contrcts TR-353, III-48, III-44 nd # ON This work ws prtilly funded y the EU Project POETICON++ nd the Portuguese FCT Project [UID / EEA / 59 / 13]. The work is complementry supported y the Alexnder von Humoldt project Emotionlly Intelligent Roots EIroots, Contrct no. 3.4-IP-DEU / dt glove. Then, the sensor dt is used to compute 25 Movement Performnce Indictors, to ssist the dignosis nd progress monitoring (ssessing the disese stge) in Prkinson s disese. A kinemtic hnd model is developed for dt verifiction nd s n dditionl resource for extrcting supplementry movement informtion. Results: Our results show tht the proposed Movement Performnce Indictors re rele - vnt for the Prkinson s disese ssessment. This is further confirmed y correltion of the proposed indictors with clinicl tpping test nd UPDRS clinicl scle. Clssifiction results showed the potentil of these indictors to discriminte etween the ptients nd controls, s well s etween the stges tht chrcterize the evolution of the disese. Conclusions: The proposed sensor system, long with the developed pproch for re - hilittion movement nlysis hve significnt potentil to support nd dvnce trditionl rehilittion therpy. The min impct of our work is two-fold: (i) the propo - sition of n pproch for supporting the therpists during the dignosis nd monitoring evlutions y reducing sujectivity nd imprecision, nd (ii) offering the possiility of the system to e used t home for rehilittion exercises in etween sessions with doctors nd therpists. 1. Introduction Trditionl rehilittion techniques for Prkinson s disese (hereinfter, PD) [1] ssessment re sed on clinicl ssessment tools nd evlution scles, such s Hoehn nd Yhr (HY) [2] nd Unified Prkinson s Disese Rting Scle (UPDRS) [3]. Those scles re descriptive (qulittive) nd only the doctor cn ssess them. Over the pst yers, progress in dt-nlysis nd sensing technologies [4] opened new possiilities for improving conventionl rehilittion prctice. However, introducing novel technologies into medicl protocols is still Schttuer 17 Methods Inf Med 1/17

2 2 S. Spsojević et l.: Sensors-sed System for Movement Anlysis chllenging, minly due to: (i) high equipment cost; (ii) system complexity nd reliility; (iii) need for technicl support during therpy sessions; (iv) lck of correltion etween clinicl nd technicl performnce indictors nd (v) lengthy nd rdu - ous process to otin the clinicl licenses. Mrker-sed motion cpture (mocp) systems [5] re often used for movement cquisition during rehilittion sessions, ecuse of their ility to deliver ccurte mesurements, in spite of their extremely high costs. Other lterntives include the ttchment of different sensors to the ptient s ody [6, 7] or hnd (dt glove) nd, more recently, low-cost mrker-free mocp systems such s the Kinect nd Xtion [8 1]. The performnce of lower-cost systems hs een tested nd shown to possess stisfctory ccurcy for the ppliction in the rehilittion therpy [1 13]. While some exmples of Kinect-sed rehilittion systems re descried in [14 17], little ttention hs een devoted to the specific cse of PD [18, 19]. Recently, uthors in [18] hve studied the Kinect ccurcy for mesuring movements of Prkinson s ptients, ut they did not implement utomtic movement nlysis. They compred the Kinect to the VICON mocp system through set of rehilittion exercises. Their results suggest similr temporl ccurcy etween the two systems when mesuring the movement durtion nd sptil ccurcy regrding the upper ody movements. Their generl conclusion is tht the Kinect hs the potentil to e used for movement nlysis in PD nd promising ppliction in the future for home rehilittion. To rise the ptient s motivtion during therpy, some studies hve introduced virtul environments into dt cquisition nd processing procedures for PD [19, ]. The min limittions with the use of virtul environments nd rehilittion gmes re the lck of officil sfety-evidence nd proof of clinicl effectiveness. Our previous study [21] introduced n pproch for full-ody movement nlysis (git nd lrge-rnge upper ody movements) sed on Kinect dt (3D coordintes of the skeleton joints) to support dignostic evlutions in PD. However, full ssessment of the PD requires more sophisticted mesurements, such s fine hnd movements. Consequently, we hve extended our previous work with hnd movement nlysis, sed on the sensory informtion provided y dt glove, to support the monitoring of PD. In recent yers, vrious types of werle sensors hve een developed nd proposed for mesuring nd evluting hnd movements: ccelerometers [22 24], gyroscopes [25, 26], mgnetic sensors [27 29], force sensors [3, 31] nd inertil sensors [32]. These sensor systems cn only modestly contriute to the hnd movement ssessment. Specificlly, the use of one or two isolted sensors in motion cquisition limits the movement quntifiction, due to the limited mount of the collected dt. Dt gloves ddress this shortcoming y integrting multiple sensors in one single, more sophisticted, device. Most dt glove-sed systems hve wired connection etween the glove nd the PC for storing dt, which cn interfere with the ptient s motion nd degrde their comfort [33 36]. A wireless system, with five sensors emedded in the dt glove is exmined in [3]. However, tht study is very limited y the low numer of sensors for hnd movement nlysis nd omission of the finger joint motion trcking. Rehilittion studies for neurologicl disorders usully focus on the nlysis of prticulr ody functionlities, such s posturl control [19], git [37, 38], upper ody movements [39] or even the oservtion of specific joint []. Our work incorportes oth the nlysis of the fullody functionlities, nd hnd movements. After cquired sensor dt, the next chllenge consists in defining suitle fetures tht cn e used to chrcterize the movements in the different suject conditions. We denote such fetures s Movement Performnce Indictors (hereinfter, MPIs) for ssisting oth dignosis nd monitoring. The MPIs we propose uild upon dominspecific knowledge provided y doctors nd therpists s well s dt nlysis. Amongst others, we propose new MPI for upper ody rehilittion, the symmetry rtio, widely used s vlidity criterion for models in iomechnics nd motor control [41, 42]. In fct, it hs een shown tht the symmetry of kinemtic speed profiles is n exclusive result of neurologicl mechnisms [43, 44], without ny interference from chnges of conditions or vriles of the performed tsk. For the hnd movement ssessment, we hve used the wireless Cyer Glove II, device with eighteen sensors tht output joint ngulr dt [45]. Although this system is reltively costly, we hve tested it in this study s proof of concept, towrds the design of n ffordle version of this dt glove for ppliction in the rehilittion prctice. To the est of our knowledge, there re no studies using the Cyer Glove II for quntifiction of hnd movements in PD ssessment [46]. We thus propose n ffordle, relile nd portle sensor system long with n pproch for nlyzing ptient s movement, with the potentil to e used s support for the conventionl rehilittion therpy (oth during dignosis nd progress monitoring) nd home rehilittion. In ddition to symmetry rtio in upper ody movements, we propose new hnd MPIs, extrcted from the dt glove sensor signls (duction sensor dt) nd the developed hnd model (velocity nd ccelertion prmeters). 2. Methods 2.1 Proposed System Structure Figure 1 shows the lock digrm of the proposed rehilittion system. The rchitecture is generl, ut in our experiments, we hve used Kinect sensor nd dtglove for mesuring full-ody / fine-hnd movements respectively, s detiled in the following prgrphs. The Kinect is low-cost motion sensing device tht offers suitle lterntive to more expensive nd complex vision-sed motion cpture systems, used tody in the rehilittion prctice. The process of the dt cquisition is sed on the visul skeleton trcking nd collecting the 3D positions of chrcteristic joints without mrk - ers. The mximum frme rte for the Kinect is 3 frmes per second (3 Hz), ut in our cse due to dditionl processing re - quired y dt collection, the frme rte drops down to 27 Hz. The cquired dt consist of 3D positions of chrcteristic skeleton joints, long with RGB nd depth video sequences ( Figure 2). Methods Inf Med 1/17 Schttuer 17

3 S. Spsojević et l.: Sensors-sed System for Movement Anlysis Figure 1 Proposed rehilittion system structure. Sensor signls pre-processing Clirtion Filtering Movement dt collection Segmenttion Movement Performnce Indictors extrction Movement Performnce Indictors selection Dimensionlity reduction Direct pproch Hnd model Tpping test Sttisticl nlysis Ptients vs. controls (dignosis) The Cyer Glove II is wireless, lightweight dt glove, dptle for different hnd sizes nd suitle for inclusion in rehilittion protocols. The mnufcturer s technicl documenttion reports sensor dt rte up to 9 Hz nd repetility of 3 degrees. The glove hs eighteen sensors giving joint-ngle output metcrpl nd proximl sensors on ech finger, four duction sensors etween ech two consecutive fingers, wrist yw nd wrist pitch sensor plced on the hnd wrist nd sensors for mesuring thum crossover nd plm rch (see Figure 3e). UPDRS-III scle Disese stges (monitoring) The Kinect device is clirted y performing specific clirtion ody pose. The clirtion procedure for the dt glove consists of predefined set of exercises to djust initiliztion prmeters. As second stge, the sensor signls re preprocessed with low-pss filters iming t c d e f g h Figure 2 Correltion with clinicl test / scle Clssifier design RGB strem ( d) nd depth strem from Kinect with detected skeleton nd collected joints (e h). Schttuer 17 Methods Inf Med 1/17 Dt cquisition 3

4 4 S. Spsojević et l.: Sensors-sed System for Movement Anlysis reducing mesurement noise. A temporl segmenttion lgorithm is pplied to the Kinect sensor signls since the movements re collected in the sequence, ut ech movement hs to e nlyzed seprtely. The MPIs design is detiled in Section 2.3. For chrcterizing the movements, two pproches hve een developed: (i) direct extrction of MPIs from the sensors signls nd (ii) using hnd model to extrct indirectly MPIs from the model, explined in more detil in Section All proposed MPIs re sttisticlly tested in distinguishing etween groups of interest (ptients / controls nd the first three disese stges ccording to Hoehn nd Yhr (HY) [2]) in the procedure to select the MPIs. The ptients t dvnced stges of PD (IV / V modified HY scle) re not le to prticipte in the experiments or wer the sensors, due to the severe motor impirments nd functionl hndicps. In ddition, the movement quntifiction nd inclusion of sensor mesurements s support to clinicl evlutions re more of interest in the erlier disese stges. Clssifiers re designed s decision-mking systems to support dignosis nd monitoring evlutions. Finlly, correltion nlysis etween our proposed MPIs nd clinicl test/ scle hs een performed. Age (yers), men (SD) Rnge Gender, numer of ptients (%) Modified Hoehn & Yhr stge, men (SD) Rnge, 1 5 UPDRS motor score (section III), men (SD) Rnge, 18 Durtion of PD (yers), men (SD) Time on L-dop (yers), men (SD) Dily L-dop dosge (mg), men (SD) Totl dily nti-pd dosge (mg), men (SD) Performed tests, numer of ptients per test Totl smple (n = 3) (8.27) Mles Femles 2.2 (.76) (11.13) (3.95) 4.21 (3.) (185.72) (356.53) Kinect Dt glove 2.2 Experimentl Procedure Dt Acquisition The experimentl group consists of thirty PD ptients with personl nd disese chrcteristics listed in Tle 1. Ptients prticipted in one, two or ll three tests: Kinect nd dt glove-sed tests nd clinicl tpping test. The numer of ptients per tests is lso listed in Tle 1. A control group is formed y twenty-three sujects without ny history of neurologicl or movement disorder. All sujects hve een exmined under the sme conditions nd they hve performed full-ody nd hnd movements, instructed y neurologist nd therpists. The experimentl exercises ( Figures 2 nd 3) re well-known in the rehilittion prctice, wherein the hnd movement nlysis is prticulrly relevnt for the evlution of PD symptoms such s tremor, rigidity, nd rdykinesi [1, 3]. Ptients receive the dily L-dop dosge, s well s other nti-pd drugs ( Tle 1). All sensor mesurements from ptients were collected under the effect of medictions (ON stte). Following the therpist dvice, ll rehilittion exercises re designed to recover or enhnce one of the three min humn functionlities lnce, moility in the Dt glove + tpping All three tests 24 (8 %) 6 ( %) Tle 1 Min demogrphic nd clinicl chrcteristics of the ptients nd performed tests. sense of norml git nd upper ody movements [47]. The git test is firly present in the mjority of rehilittion procedures nd it cn hve different forms depending on the equipment used nd the mesured git performnce indictors / fetures [47]. In our work, the git test is crried out in ccordnce with the ville Kinect rnge [12], with the strting nd end points plced t 3.5m nd 1.5m wy from the Kinect, respectively. During the git test, ptients wlked the selected distnce of 2m six times with norml nd nturl git rhythm ( Figure 2). The rest of the tested exercises elong to group of upper ody movements: djusted shoulder ductiondduction (SAA) ( Figure 2) until mximum possile rnge of motion, shoulder flexion-extension (SFE) ( Figure 2c) nd movements of the right-left hnd etween the oundries (further, hnd oundry movements (HBM), Figure 2d). The first two exercises were repeted five times, while the HBM ws repeted ten times with ech hnd within experiment. The set of hnd exercises includes finger-tpping movement ( Figure 3), fingers flexion nd extension movement ( Figure 3), rottion of the hnd ( Figure 3c), nd fingers expnsion nd contrction movement ( Figure 3d). We investigted the correltion etween the proposed MPIs, cross ptients with different disese stges (ccording to HY scle), nd clinicl tests such s tpping test, nd UPDRS clinicl scle to ssess if such mesurements cn e used s rehilittion fetures. The clinicl mesurements (HY nd UPDRS) re collected y one experienced rter, immeditely efore the sensor mes urements. All mesurements hve een performed in the hospitl settings for outptients. The clinicin ws present during the sensor mesurements in order to monitor the ptient stte, nd to prevent si tutions in which the ptient is quickly switched from ON (the effect of mediction present) to OFF stte (the effect of mediction stopped), due to which the possile clinicl mesurement nd sensor mesurement would e crried out under different conditions. The Hoehn nd Yhr (HY) clinicl vlues (which Methods Inf Med 1/17 Schttuer 17

5 e Figure 3 Experimentl exercises ( d) nd sensor positions on the glove (e) Dt Preprocessing: Noise Filtering nd Temporl Segmenttion c d evlute the disese stge) were ssessed using the modified Hoehn nd Yhr (HY) Scle [2]. The UPDRS clinicl vlues (which evlute the motor symptoms) were ssessed using the motor prt of the Unified Prkinson s Disese Rting Scle (UPDRS) [3]. One group of ptients performed tpping test [48] tht is frequently used y neurologists to exmine hnd movements in PD ptients. The test consists of the proximl nd distl tpping tsks using specilly designed ord ( Figure 4) s the one proposed in [48]. The proximl tpping tsk refers to the lternte pressing of two lrge uttons locted cm prt with the plm of the hnd during 3 seconds. The distl tpping tsk is relted to the lternte pressing of two closely locted uttons (3 cm prt) with the index finger while the wrist is fixed on the tle during 3 seconds. Both tests re repeted twice Schttuer 17 5 S. Spsojević et l.: Sensors-sed System for Movement Anlysis for the plm nd index finger of the right hnd, wherein ech test lsts thirty seconds nd the suject tries to lterntely press the uttons s mny times s possile. Since the CyerGlove is designed for the right hnd, only ptients with the ffected right side (side on which PD symptoms re initited) hve een tested with the dt glove. In the cse of Kinect mesurements, oth, right nd left side ffected ptients hve een considered. Dt pre-processing is required for noise removl s well s for temporl segmenttion (only Kinect dt). We hve pplied Butterworth low-pss filters with cut-off frequency of 3 Hz to rw sensor signls tht proved to e effective in terms of noise removl. Sensor motion dt re collected in sequence of severl consecutive repetitions of the instructed movement. Since the MPIs for Kinect dt re extrcted from ech movement seprtely, temporl segmenttion lgorithm is pplied to divide the sequence into the corresponding movement segments. On the other hnd, the dt glove MPIs re extrcted t time for ll movements in the sequence; hence segmenttion lgorithm is pplied only to the Kinect dt. The segmenttion lgorithm is sed on the nlysis of the relevnt joint for ech specific movement nd detecting its meningful positions long the prticulr xis of Figure 4 Bord for tpping test. Methods Inf Med 1/17

6 6 S. Spsojević et l.: Sensors-sed System for Movement Anlysis Figure 5 interest. In other words, joint positions cn revel the movement s strting nd termintion frmes. Let the oserved skeleton dt e represented y: [J 1,..., J n,..., J N ] R 3K N, 1 n N (1) where N is the totl numer of frmes, K is the numer of collected joints per frme (K = 15) nd vn,1 vn, k vn, K 3K n 1 k K J [ j,..., j,..., j ] R, vnk, n n n 3 1k K, j ( x, y, z ) R k k k k (2) where J n represents the set of ll K collected v, joints per frme n nd j nk k prticulr k-th 3D-coordinte joint in the frme n. Our gol is to find set of vectors (Eq. 3) V = {[s 1, t 1 ],...,[s l, t l ],...,[s L, t L ]}, 1 l L (3) Frmes Illustrtion of the segmenttion pproch (shoulder duction movement). Tle 2 The proposed MPIs result from comintion of 4 ody movements nd 4 MPI ctegories (speed, rigidity, rnge of motion nd symmetry). Movements/MPI ctegories Git Shoulder duction-dduction (SAA) Shoulder flexion-extension (SFE) Hnd oundry movements (HBM) Speed/Speed vritions MPI 1 / MPI 2 MPI 5 MPI 8 MPI 1 Rigidity MPI 3 Rnge of Motion (ROM) MPI 4 MPI 7 Symmetry Rtio (SR) MPI 6 MPI 9 where L denotes the totl numer of movements (temporl segments) in sequence nd ech vector consists of two components: the first one represents the strting frme (s l ) nd the second one corresponds to the termintion frme (t l ) of the l-th movement. The segmenttion lgorithm is sed on the serch for peks nd vlleys in the input signl, i.e. locl mxim or minim. Input signl represents the evolution of the chosen joint in the direction (x, y or z, Figure 5) with the most expressed trnsitions during the prticulr movement. Under the git test, it is the evolution of the torso joint in the z-xis direction. As for upper ody movements, right-hnd joint in the y-xis direction ws chosen for shoulder duction-dduction ( Figure 5) nd flexion-extension movement, while the oth hnd joints in the x-xis direction represent the input signls of the segmenttion lgorithm for hnd oundry movement sequence. Segmenttion points re extrcted from the determined set of locl minim nd mxim points. Then, the ctul eginning nd end of the movements re isolted sed on the two types of threshold conditions: (i) mplitude vlue threshold (mplitude rnge in which segmenttion points lie) nd (ii) temporl position threshold (corresponding distnce in time etween the points of interest must e stisfied). Threshold vlues re estlished depending on the prticulr movement nd its temporl evolution in the selected direction. Figure 5 illustrtes the segmenttion lgorithm for the cse of shoulder duction-dduction movement sequence. Evo - lution of the right-hnd joint in the y-xis direction shows tht y vlue increses from the strting position in the first prt of the movement (when the rms go up) nd then decreses in the second prt of the movement (when the rms go down). The ctul strting nd ending points for ll six movements in the sequence re correctly determined y our segmenttion lgorithm ( Figure 5). 2.3 Proposed Approch for Movement Chrcteriztion We hve used severl MPIs tht represent the movements of the different ody prts (using the Kinect) or hnds (using the dt-glove) of suject. The choice of MPIs ws prtly resulting from discussions with doctors, therpists, nd other domin experts. In the following sections, we will detil how these MPIs were designed Full-ody Movements All together we hve used 1 different MPIs tht result from the comintion of four mesurement ctegories (speed, rigid - ity, the rnge of motion nd symmetry) pplied to 4 ctegories of full-ody movements, s illustrted in Tle 2. The MPIs we extrcted from git movements re commonly used in the rehilittion prctice nd tretment [28]. From git movements, we considered three MPIs speed of the git, vritions in the git speed, nd hnd rigidity during wlking. We hve dopted the men git speed V, Methods Inf Med 1/17 Schttuer 17

7 S. Spsojević et l.: Sensors-sed System for Movement Anlysis 7 Distnce right hnd right hip during git sequence [cm] Helthy suject Ptient Shoulder ngle ( ) Helty sujects Ptients Frme Frmes Figure 6 () The difference etween the left / right hnd-hip distnces shows the rigidity symptom. () Evolution of the shoulder ngle profiles during shoulder duction movements. Eq. (4), during ech two-meter sequence. Due to possile devitions of the strting nd end point of the git test, nd in order to improve the ccurcy, the pth length (the numertor in Eq. (4)) hs een clculted s the totl trjectory of the torso during ech git sequence, insted of setting the pth length of 2m. The totl trjectory length is otined y summing up the Eucliden distnces (d) etween the torso joint coordintes X i (x i, y i, z i ) nd X i 1 (x i 1, y i 1, z i 1 ) for consecutive frmes, i nd i 1, during the git sequence. The time durtion of the git sequence (the denomintor in Eq. (4)) is computed sed on the totl numer of frmes (m nd n denote respectively the first nd lst frme of the sequence) nd the frme rte, f = 27 Hz. V n dx (, X i i 1) im ( nm1)/ f (4) Vritions in the git speed re clculted s the differences in the men git speed etween consecutive git sequences within git test. This MPI cn e n indictor of the unlnced git if the speed vlue significntly differs from one git sequence to nother. The position of the rms during wlking cn revel rigidity, one of the min indictors of the PD [1]. In the cse of helthy sujects, the rms usully swing in certin rhythm during git ctivity, in contrst to the Prkinson s ptients. We hve computed mesure of rigidity, sed on temporl evolution of the hnd position during the git test. The rigidity symptom cn e noticed in the vrition of the distnce etween the hip nd hnd during the git sequence. For helthy sujects, the temporl evolution of these distnces is pproximtely periodic, due to norml rm swing. In contrst, for ptients with one rigid rm, the distnce etween the rigid hnd nd the closest hip does chnge significntly over time ( Figure 6). The mesure of rigidity is clculted in two steps. First, we record the difference signl etween the left nd right hnd-hip distnces, during the git movement. Then, we tke the highest vlue of the (solute) difference signl s n indictor of rigidity. For ptients with rigid rm the difference signl is lrger ecuse the helthy rm performs norml swing nd the rigid rm remins more or less sttic. Insted, helthy sujects disply lower-mplitude difference signl, due to the norml swing of oth hnds. Inspired y the well-known nd widely used rehilittion mesure for upper ody movements, we hve lso computed the rnge of motion [47] for the shoulder duction-dduction nd shoulder flexionextension exercise. The rnge of motion represents n ngle of the movement rel - tive to specific ody xis, which cn e mesured t vrious joints such s elow, shoulder, knee, etc. In our cse, we mesure the evolution of the shoulder ngle during the movement in reltion to the longitudinl ody xis. As specific MPI, we hve used the rnge of motion (mximum chieved shoulder ngle). Exmples of the shoulder ngle profiles of oth norml sujects nd ptients for the shoulder duction movement re shown in Figure 6. The rnge of motion is higher for helthy sujects (more thn 18 ) thn for ptients (142, 15 ). In ddition, the trjectory of shoulder ngle is steeper for helthy sujects, indicting higher speed of movement. We clculted the men movement speed for ll three tested upper ody exercises. The pplied procedure ws the sme for the git speed (Eq. 4), setting the pth length to the totl length of hnd trjectory during the movement. The comprison etween relevnt left/ right ody-side movement descriptors cn suggest which side or lim is more ffected y the neurologicl disorder. For helthy sujects, these differences re usully negligile, while they cn ecome quite lrge for Prkinson s ptients, depending on the disese stge. Schttuer 17 Methods Inf Med 1/17

8 8 S. Spsojević et l.: Sensors-sed System for Movement Anlysis 3 25 Helthy suject right rm Helthy suject left rm Ptient right rm Ptient left rm Anglulr velocity ( / s ) Frme Figure 7 Evolution of the shoulder ngulr velocity profiles during shoulder duction movements () nd symmetry rtio clcultion (). Importnt movement descriptors such s profiles of joint ngles ( Figure 6) nd ngulr velocity profiles ( Figure 7) cn revel the symmetry of the movements. In order to quntittively ssess the movement symmetry, we hve extrcted symmetry rtio from the shoulder ductiondduction nd shoulder flexion-extension exercises. In motor control, the symmetry Tle 3 Movements Extrcted MPIs Sensors of interest Extrcted MPIs from the collected hnd movements. Fingers flexion nd extension Joint rnge of motion Proximl: thum (MPI 11 ), index (MPI 12 ), middle (MPI 13 ), ring (MPI 14 ) Metcrpl: index (MPI 15 ), middle (MPI 16 ), ring (MPI 17 ), pinky (MPI 18 ) Figure 1 Hnd rottion Joints rnge of motion Wrist yw (MPI 23 ) Figure 2 rtio (SR) [41 44] ( Figure 7) is defined s the rtio etween ccelertion (t ACC ) nd decelertion (t DEC ) times, during one movement. Figure 7 shows tht the mximum ngulr velocity of the shoulder duction movement is higher for helthy sujects thn it is for Prkinson s ptients. In ddition, helthy sujects rech the mximum ngulr velocities of the left/ Fingers expnsion nd contrction Angulr velocity dt Aduction sensors (MPI 19, MPI, MPI 21, MPI 22 ) Figure 3 Finger tpping movement Velocity (MPI 24 ) nd ccelertion (MPI 25 ) signl prmeters Hnd model right rm movements pproximtely t the sme time s opposed to non-helthy sujects, where difference of out frmes is typicl. The consequence is unlnce in symmetry rtios etween left nd right rm for the sme movement. Thus, in our experiments, we otined lrger left-right differences of the symmetry rtios for Prkinson s ptients thn in helthy sujects. We hve descried 1 MPIs extrcted from the Kinect dt to quntify the movements of different ody prts during rehilittion session. These MPIs will e used lter on to dignose nd chrcterize the progress of the Prkinson s ptients. In the next section, we will explin how the hnd movements were lso tken into considertion for finer nlysis Hnd Movements Similrly to wht we hve done for fullody movements, we propose new set of MPIs to chrcterize the hnd movements ( Tle 3) with respect to: (1) rnge of motion of the chrcteristic hnd nd fin - ger joints (for fingers flexion nd extension movement nd rottion of the hnd); (2) velocity vlues derived from duction sensor ngulr dt (for finger expnsion nd contrction movement) nd (3) velo - city nd ccelertion prmeters etween thum nd index finger tips estimted Methods Inf Med 1/17 Schttuer 17

9 S. Spsojević et l.: Sensors-sed System for Movement Anlysis 9 from the hnd model (for finger-tpping movement). The rnge of motion (ROM) of the hnd nd fingers chrcteristic joints cn e derived directly from the sensor ngulr dt signls. It is defined s the distnce etween the ngulr sensor vlues from the initil (minimum ngulr vlue) to the finl position (mximum ngulr vlue) during ech movement in the sequence ( Figure 8). The ROM mesurement is extrcted from the fingers flexion nd extension movement nd hnd rottion movement. The fingers flexion nd extension movement is representtive in the investigtion of the tremor, dyskinesi nd the moility of the fingers. Sujects re sked to perform twenty consecutive lternting fingers flexion nd extension movements s fst s possile. For the quntifiction of this movement, we concentrte on the sensor dt collected from metcrpl (index, middle, ring nd little finger) nd proximl finger joints (thum, index, middle nd ring finger) ccording to their high ctivity during movement performnce ( Tle 3). The rottion of the hnd movement cn indicte the presence nd severity of the rigidity symptom. Under this movement s test, sujects need to rotte their hnd to the left nd right direction lterntely s fst s possile during ten second period. The relevnt sensor dt for this movement re collected from the wrist yw position ( Tle 3). The ngulr dt profiles of wrist yw joint ( Figure 8) for control sujects show the expressed periodicity nd wide rnge of motion. For ptients, the rnge of motion is sustntilly smller nd the signl clerly illustrtes the execution of slower movements ( Figure 8). The fingers expnsion nd contrction movement tests the functionlity, flexiility nd speed of finger movements; hence, it cn revel the presence of synchronous, uncoordinted motion nd dyskinesi. Sujects re sked to perform ten con - secutive fingers expnsion nd contrction movements. It is chrcterized using four duction sensors, plced etween ech two consecutive fingers. The ngulr velo - city signls re derived from processed ngulr dt since the velocity vlues hve underlined greter differences etween ex- perimentl nd control group thn rnge of motion dt. Mximum ngulr velocity vlues for ech movement in sequence of oth, expnsion ( Figure 9, circles) nd Figure 8 Clculting the rnge of motion (ROM) of finger joints () nd evolution of the wrist yw joint ngulr dt profiles during rottion of the hnd movement (). Figure 9 Evolution of the duction sensor (ringpinky position) ngulr velocity dt profiles during fingers expnsion nd contrction movement. Joint ngle [ ] Joint ngle [ ] Joint ngulr velocity [ / s] Angulr dt evolution Mximum ngulr vlues Minimum ngulr vlues contrction phse ( Figure 9, squres) re extrcted s MPIs. Evolution of the ngulr velocity profiles of ptient nd control suject for ring-pinky duction sensor is ROM Time [s] Time [s] 8 Control Ptient Time [s] Control Ptient Schttuer 17 Methods Inf Med 1/17

10 1 S. Spsojević et l.: Sensors-sed System for Movement Anlysis given in Figure 9. It cn e seen tht control suject s consecutive expnsion-contrction finger movements rech higher velocity vlues compred to the sme movements in ptients Model-sed Estimte of Hnd MPIs Finger-tpping movement is the most frequent rehilittion exercise in the PD protocol, which tests symptoms such s tremor, dyskinesi, nd rdykinesi. In our finger tpping test, sujects re direct ed to perform twenty consecutive touches etween the thum nd index finger tips s fst s possile with the elow fixed on the tle. It hs een widely studied nd some ttempts t its quntifiction re reported in [22, 28, 29, 49]. In some of these pproches, sensors re ttched t the thum nd index finger tips mking contct detections during the finger-tpping movement performnce. In [22, 49] mesurement system is composed of two ccelerometers, while in [28, 29] mgnetic sensors re used. The min drwck of these systems is the nlysis of one prticulr movement since, due to the sensor plcement, only the evlution of the fingertpping movement is fesile. Unfortuntely, the sensor glove we used does not possess sensors on the fingertips nd ville joint-ngle dt re not enough to chrcterize finger-tpping movement. To overcome this, we developed hnd model nd used the model to estimte the fingertips position informtion. The hnd model llows us to produce estimtes of different hnd-relted mes - urements (distnce, velocity, ccelertion), without using specific sensors (e.g. ccelerometers) for tht purpose. Consequently, our pproch provides comprehensive nlysis of severl hnd movements long with finger tpping movement, without excluding significnt sensor informtion. The Kinemtic hnd model with degrees of freedom is fed with the joint-ngle dt collected y the sensor glove nd rel dimensions of the suject s finger sections, mesured t the time of experiments. Bsed on this informtion nd using direct kinemtics, the positions of the fingertips cn e estimted. Every finger is treted s seril kinemtic chin, which is modeled using Denvit-Hrtenerg (D-H) representtion [5, 51]. As y-product, the kinemtic hnd model cn e used to visulize the hnd movements nd check whether the sensor dt keep trck of rel finger movements within the pproprite rnge of motion. Finger tpping movement is quntified sed on the velocity nd ccelertion signls. Those signls re otined s derivtives of the distnce informtion etween thum nd index fingertips during fingertpping movement. Concrete MPI vlues re represented y the extreme points of the velocity nd ccelertion signls. Figure 1 shows the extrcted MPIs (mrked using circles nd squres) during the movement sequence. 2.4 Internl Consistency of the Sensor Mesurements nd Reliility of the MPIs We ssessed the internl consistency of the sensor mesurements using Cronch s lph prmeter [52]. In the cse of the Kinect sensor mesurements, Cronch s lph prmeter ws investigted for four recorded movements ( Figure 2 d), fifteen collected joints ( Figure 2e h) nd three coordintes ( Figure 5) in the sense of the collected ptient s dt (12 ptients in totl). All otined Cronch s lph prmeters cross different movements, joints nd coordintes hve vlues within the rnge [.91.99]. Vlues of the Cronch s lph prmeter close to one indicte the high consistency of the Kinect sensor mesurements. Similr nlysis hs een conducted for the dt glove mesurements. The Cronch s lph prmeter ws determined for four collected hnd movements ( Figure 3 d) nd eighteen sensors plced inside the Cyer Glove ( Figure 3e). The dt set for internl consistency investigtion consists of 24 ptients. Our results cross different movements nd sensor outputs report the vlues of the Cronch s lph p rmeter within the rnge [.86.99], nd thus, confirm the high consistency of the dt glove sensor mesurements, s well. In order to test the reliility of the extrcted MPIs, the split-hlf method for 8 6 Control Ptient 15 Control Ptient Velocity [ mm / s ] 6 Accelertion [ mm / s 2 ] Time [s] Time [s] Figure 1 Estimted velocity () nd ccelertion () signls from the hnd model. Methods Inf Med 1/17 Schttuer 17

11 S. Spsojević et l.: Sensors-sed System for Movement Anlysis 11 Tle 4 ICC reliility prmeters of the extrcted Kinect nd dt glove MPIs. Kinect MPIs ICC 95 % CI Dt glove MPIs ICC 95 % CI Dt glove MPIs ICC 95 % CI [.89.97] [ ] [.882.9] [..79] [.32.82] [.92.98] [ ] [ ] [ ] [ ] [ ] [.882.9] [.93.98] [ ] [ ] [.49.87] [ ] [ ] [.62.9] [ ] [ ] [.91.98] [ ] [.15.75] [.84.96] reliility nlysis [52] hs een pplied. The split-hlf method divides the conducted tests into two prts nd correltes the scores on one-hlf of the test with scores on the other hlf of the test. Thus, the split-hlf method estimtes the reliility sed on the repetitions inside the sme tril. Reliility of the extrcted MPIs from the Kinect nd dt glove dt is ssessed using Intrclss correltion coefficient (ICC) [52]. Results re shown in the Tle 4 for oth, Kinect nd dt glove MPIs, long with the 95 % confidence intervls. The complete list of the numered MPIs is given y Figures 11 (Kinect MPIs) nd 12 (dt glove MPIs). Results of the reliility nlysis hve demonstrted the high reliility of the dt glove MPIs (ICC.9 for ll MPIs). In the cse of the Kinect MPIs, the mjority of the extrcted MPIs hve shown the high reliility, except the Vritions in the git speed MPI nd the Difference etween right nd left SR MPI (SFE movement), where the vlues of ICC re less thn Results We hve defined set of 25 MPIs (1 for the full-ody nd 15 for the hnd movements) tht cn e used oth for dignosis nd progress monitoring of PD during rehilittion. The design of these MPIs ws grounded on the informtion provided y neurologists nd therpists with the gol of delivering quntittive informtion out suject s performnce. In this section, we will show the reltionship of these MPIs with the demogrphic nd clinicl chrcteristics of sujects, how these MPIs were selected from the initil MPIs set nd how they cn e successfully used in prctice. We will ddress oth full-ody movements cptured with the Kinect sensor nd fine hnd movements mesured with the dt glove. When deling with the initil MPIs set, three importnt questions re imposed: (1) Wht is the reltionship etween the proposed MPIs nd the demogrphic nd clinicl chrcteristics of sujects? (2) Which MPIs re the more relevnt nd informtive? (3) Cn we improve clssifiction results if we design n optimized MPIs set? To nswer the first question we conducted sttisticl nlysis using mixed effect models. To investigte questions 2 3 Figure 11 MPI rnges (Kinect dt). 1. Git speed [m / s] 2. Vritions in the git speed [m / s] 3. Rigidity mesure[cm] 4. ROM (SAA) [ ] 5. Speed (SAA) [m / s] 6. Difference etween right nd left SR (SAA) 7. ROM (SFE) [ ] 8. Speed (SFE) [m / s] 9. Difference etween right nd left SR (SFE) 1. Speed (HBM) [m / s] we dopted Liner Discriminnt Anlysis (LDA) pproch [53]. 3.1 Sttisticl Evlution of the MPIs cross Demogrphic nd Clinicl Prmeters We investigted the reltionship etween the proposed MPIs nd the demogrphic nd clinicl chrcteristics of sujects ge, gender, nd clinicl group: (i) ptients/ controls nd (ii) disese stge group. In order to revel whether those chrcteristics re sttisticlly significntly correlted with the primrily proposed MPIs, we hve used mixed effect models [52]. Our initil MPIs set consisted of 3 MPIs (11 full-ody nd 19 hnd movement MPIs). Every MPI ws modeled sed on fixed nd rndom effects. As fixed effects, we in- Schttuer 17 Methods Inf Med 1/17

12 12 S. Spsojević et l.: Sensors-sed System for Movement Anlysis cluded the ge, gender, nd group effect. Intr-individul vritions in repeted mesures were modeled s the rndom effect. Sttisticl significnce of the fixed effects ws ssessed y corresponding p-vlues (5 % confidence level) fter correction using Benjmini-Hocherg procedure for multiple testing. Mixed effect model fitting ws performed for thirty initilly proposed MPIs. The key results of the sttisticl nlysis led to two min conclusions: (i) the demogrphic prmeters, ge, nd gender, did not hve significnt influence (p >>.5) on the MPIs nd (ii) in ddition, five of thirty MPIs hd no significnt correltion with the clinicl group effect (p >.5). Those MPIs represent one fullody MPI (the mesure of tremor) nd four hnd movement MPIs (ROM of thum metcrpl joint, ROM of pinky proximl joint, ROM of wrist pitch nd distnce prmeter of the hnd model). Hence, s suggested y these sttisticl studies, the susequent dt nlysis (dimensionlity reduction, clssifiction, nd correltion nlysis) ws crried out with the clinicl group in formtion only (demogrphic prmeters were not relevnt) nd using the identified 25 MPIs. Such outcomes led to the simplifiction in terms of the numer of clusters nd dt needs nd rejection of five MPIs in the susequent dt nlysis Overview of the Full-ody nd Hnd MPI Vlue Rnges Figures 11 nd 12 provide dditionl insight concerning full-ody nd hnd MPIs, dopted in the previous section nd their rnges cross ptients nd controls. Becuse of their higher vlues, MPIs 3, 4, 5, 7, 8 nd 1 were normlized, in order to llow comprtive representtion with other MPIs. The vlues of the rnge of motion nd git / movement speed re lower in the ptient group, while the left-right rm differences of the symmetry rtio, during shoulder movements, s well s vritions in the git speed, re much lrger in ptients, s expected. Figure 12 illustrtes lower vlues of finger joints rnge of motion in the ptient group, s expected. Our experiments hve shown especilly lrge differences in ngulr velocity vlues etween ptients nd controls for fingers expnsion nd contrction movement ( Figure 12, 19 22), s well s in the cse of MPIs extrcted from the hnd model ( Figure 12, 24 25). Hence, the results confirm tht our newly proposed MPIs would give significnt contriution to support the evlutions in PD. 15 Controls Ptients Figure 12 MPI rnges (sensor glove dt). 11. ROM thum proximl [ ] 12. ROM index proximl [ ] 13. ROM middle proximl [ ] 14. ROM ring proximl [ ] 15. ROM index metcrpl [ ] 16. ROM middle metcrpl [ ] 17. ROM ring metcrpl [ ] 18. ROM pinky metcrpl [ ] 19. Angulr velocities index-middle dduction [ / s]. Angulr velocities middle-ring dduction [ / s] 21. Angulr velocities ring-pinky dduction [ / s] 22. Angulr velocities thum-index dduction [ / s] 23. ROM wrist yw [ ] 24. Velocity hnd model [mm / s] 25. Accelertion hnd model [mm / s 2 ] 3.2 Dimensionlity Reduction By dopting the 25 MPIs for the tested full-ody nd hnd exercises, we otin two sets of 1-dimensionl nd 15-dimensionl feture vectors ( Figure 11 nd 12), which cn e used in clssifiction system to ssist dignosis nd monitoring. We pplied Liner Discriminnt Anlysis (LDA) [53] to determine the most relevnt MPIs for the decision-mking process sed on the clinicl group prmeter, etween ptients nd controls (dignosis support) nd etween disese stges (monitoring support). Demogrphic prmeters were not of interest ccording to the sttisticl nlysis descried in Section 3.1. Another outcome of the LDA lgorithm is the trnsformtion of the MPI dt set into new, compct, lower dimensionl spce. The LDA pproch ims to mximize the etween-clss distnce nd to minimize within-clss dissiption. The dimension of the newly creted spce is determined from the eigenvlues of the LDA criterion function, which tkes into ccount the clss covrinces. Our tests reveled tht, oth in the 1-dimensionl nd 15-dimensionl feture spces, the sum of the first two eigenvlues ws much lrger thn the sum of the remining eigenvlues (λ 1 + λ 2 >> λ λ m ), where m denotes the totl numer of fetures. Hence, oth feture sets re reduced to the new 2-dimensionl feture spce. As side-result, the LDA method rnks the originl fetures in terms of their contriution to the reduced feture spce sed on the weights (v 11...v m1 ; v 12...v m2 ) of the trnsformtion mtrix V, where m represents the totl numer of fetures, (Eq. 5). S is the mtrix of the originl dt set with n smples while the L represents the mtrix of reduced dt set to 2-dimensionl feture spce. l11 l12 s11 s1 m LS* V l l s s v * v v v m1 m2 n1 n2 n1 nm (5) Methods Inf Med 1/17 Schttuer 17

13 S. Spsojević et l.: Sensors-sed System for Movement Anlysis Informtiveness index [%] Informtiveness index [%] Dt glove MPIs Dt glove MPIs Figure 13 LDA Informtiveness index: () ptients-controls nd () disese stges dt. The modified Informtiveness Index (II(f)) sed on the weights of the trnsformtion mtrix is dopted for the first f fetures using Eq. (6): f s( vi 1 vi2 ) i1 II( f ) m, 1 f m s( v v ) i1 i1 i2 (6) where the decresing order of the sum of weights is considered: (v 11 + v 12 ) (v 21 + v 22 )... (v m1 + v m2 ). The LDA method for groups of ptients nd controls results tht, for keeping 8 % of informtion from the originl Kinect dt set, it is sufficient to select the MPIs 1, 6, 9 nd 1 from Figure 12. This result shows tht, in ddition to the speed of the git nd upper-ody movement (HBM), oth symmetry rtio MPIs re mongst the most informtive MPIs. The sme criterion, of cpturing 8 % of the informtion from the originl dt sets, is pplied to verify the most relevnt hnd MPIs. Consequently, we hve chosen first seven fetures during LDA nlysis for groups of ptients nd controls nd six fetures from the LDA procedure in the cse of disese stges ( Figure 13). MPIs 22, 18, 12, 24, 25, 13 nd 17 from Figure 12 hve the highest contriution to differentite clsses of p- tients nd helthy-sujects, while MPIs 24, 25, 18, 12, 22 nd 13 were the most representtive fetures during dimensionlity reduction ccording to disese stge clsses. This result suggests tht the MPIs extrcted from the hnd model re the most relevnt fetures in oth cses. In ddition, ROM MPIs (fingers flexion nd extension movement oth proximl nd metcrpl joints) nd ngulr velocity MPIs (fingers expnsion nd contrction movement thum-index duction sensor), re lso very importnt in the dt nlysis. The LDA method lso provides us with new synthetic fetures tht form reduceddimension feture spce. While these new synthetic fetures hve the power to differentite the different conditions in the dt, they re less efficient in terms of communiction nd understnding for the medicl doctors nd therpists, s they do not correspond to specific MPI. 3.3 Clssifiction: Dignosis nd Monitoring Evlutions So fr, we hve shown how to uild set of MPIs from the movement of ody/ hnds of Prkinson s ptients. Sttisticl nlysis using mixed effect models confirmed the significnce of the clinicl group fctor in reltion to MPIs, in contrst to demogrphic fctors tht turn out to e non- relevnt. In ddition, it hs underlined 25 MPIs out of 3 s signifi- cntly correlted with clinicl group effect. The LDA nlysis hs estlished new reduced-dimension feture spce nd determined the most relevnt MPIs. In this section, we present clssifiction pproch tht cn utomticlly identify the different suject groups (ptients / controls nd disese stges) sed on the originl nd the derived feture sets. First, we uilt clssifiction model using the trining dt. Second, we dopt the model prmeters in the cross-vlidtion procedure. Finlly, we test the model on the unseen testing dt. This procedure is performed for ll clssifiers. Using the Kinect dt, we hve tested the clssifiction etween helthy nd nonhelthy sujects in three different conditions: (i) with the originl feture set, (ii) using the four most relevnt fetures dopted in the previous section nd (iii) the two new synthetic fetures, otined from LDA. We hve compred three different clssifiers ( Figure 14): () SVM support vector mchines with RBF kernel Figure 14 Clssifiction ccurcy of Kinect dt (ptients/ controls). Schttuer 17 Methods Inf Med 1/17

14 14 S. Spsojević et l.: Sensors-sed System for Movement Anlysis (ndwidth of the RBF kernel, σ nd regulriztion prmeter, C:.1 < σ < 1,.1 < C < 1), () KNN (numer of nerest neighors, k 1, 3, 5) nd (c) neurl networks (MLP multilyer perceptron: vrious structures with different numer of hidden lyers nd nodes). The prmeters of clssifiers were chosen from ove specified rnges in vlidtion procedure in order to chieve the highest ccurcy rte. Figure 14 shows tht ll clssifiers succeed to differentite helthy from nonhelthy sujects. The SVM nd the NN- MLP hve the est results when using the originl feture set. The KNN clssifier works est for the reduced feture sets ut in generl, is the lest performing clssifier. We chieve clssifiction results close to % on the unseen testing smples, compred to the chnce level of 5 %. The Kinect dt showed poor results during clssifiction etween the disese stges. We chieved clssifiction ccurcy of out 5 %, compred to the chnce level of 33 %, which is not enough for evluting the disese stge. Our results show tht, while the Kinect MPIs hve the power to distinguish ptients from helthy sujects, the quntittive nlysis of the disese stges requires more detiled nd informtive MPIs, extrcted from the fine hnd movements. The git represents the most importnt motor tsk to revel the motor impirments. However, ptients t mild to moderte PD stges, do not experience significnt git disorders, contrrily to the more dvnced disese stges. By definition, serious git disorders re strting t the third HY stge nd ecome more importnt t fourth nd fifth HY stges. Moreover, crdinl clinicl symptoms such s rdykinesi, rigidity nd lter the hnd tremor re required for estlishment of the PD dignosis, nd those symptoms re continuously present t different disese Figure 15 stges. Hence, in the first three disese stges, hnd movement ehvior is more relevnt for PD ssessment nd monitoring thn the git nd lrge rnge upper ody movements, which our results hve confirmed. The clssifiction process for sensor glove dt ws performed etween the groups of controls nd ptients (support for dignosis) nd etween ptients with different disese stge (support for monitoring). Three different clssifiers re tested with the originl feture set, six / seven most relevnt fetures nd two new fetures otined from LDA ( Figure 15). Support vector mchines (SVMs) re designed with the RBF kernel, wherey the ndwidth of the RBF kernel, σ vries etween.1 nd 1 nd regulriztion prmeter, C vries within rnge [.1 1]. K nerest neighors clssifier (KNN) is tested for the k = 1, 3 nd 5 nerest neighors. The neurl networks clssifier is multilyer perceptron with different numer of hidden lyers nd nodes. The prmeters of clssifiers re chosen from listed rnges in vlidtion procedure in order to chieve the highest ccurcy rte. The est results on the testing set for ll clssifiers re otined with the originl 15D feture set. The clssifiction ccurcy is ove 9 % for the six/seven most relevnt feture set. The lowest clssifiction rtes re reported in the cse of new reduced feture spce, due to the significnt informtion losses during dimensionlity reduction procedure. These results confirm the higher infor - mtiveness of the sensor glove MPIs compred to the Kinect dt MPIs nd their ility to prticipte in oth, dignosis nd monitoring evlutions of PD. Such outcome is expected, due to the high importnce of hnd movement nlysis nd quntifiction for PD ssessment. Clssifiction ccurcy sensor glove dt: () ptients / controls nd () disese stges. 3.4 Correltions with Clinicl Scles We hve confirmed the potentil of the chosen MPIs to support the decision-mking systems for dignosis nd monitoring evlutions. Another importnt issue is to investigte the correltion etween the proposed MPIs nd clinicl test nd scles. This is prticulrly importnt for the possile inclusion of the proposed MPIs into rehilittion protocols. Since the clinicl scles re designed for disese stge ssessment, the correltion nlysis is performed only for the sensor glove dt MPIs. The correltion nlysis is crried out etween the proposed hnd MPIs ( Figure 12) nd tpping test [48] nd UPDRS- III clinicl scle [3]. The tpping-test is performed y ptients while UPDRS-III vlues result from the neurologist s evlution. Correltions were clculted using Person s correltion coefficient r (tkes vlues etween 1 nd for negtive correltion nd etween nd 1 for positive correltion), long with the p-vlue (testing the hypothesis if two vriles re correlted). Sctter plots in Figure 16 illustrte the correltion etween selected MPIs nd clinicl prmeters, where the line represents the regression curve. It cn e seen tht the selected MPIs hve positive correltion with the tpping test, more concretely with the numer of tps performed y the suject s right-hnd plm (procedure of the tpping test is previously explined in the Section 2.2.1). This is expected since the ptients who hve higher vlues of ROM nd ccelertion prmeter potentilly cn chieve lrger numer of tps within defined period (3 seconds). On the other side, our MPIs hve neg - tive correltion with the UPDRS-III scle, since the lower vlues of our MPIs nd higher vlues on this scle indicte more severe stte of the ptient i.e. higher disese stge. Results of the correltion nlysis hve shown tht some MPIs re highly correlted with oth clinicl prmeters (11, 12, 13, 14, 24, 25 from Figure 12, r >.5/ r <.5, p <.5) nd those MPIs represent ROM of the proximl finger joints (11 14) nd velocity nd ccelertion prmeters derived from the hnd model (24, 25). Methods Inf Med 1/17 Schttuer 17