An Unobtrusive Semantic Health-Monitoring Medium

Transcription

1 An Unobrusive Semanic Healh-Monioring Medium Bogdan Pogorelc 1,2, Majaž Gams 1,2 Jožef Sefan Insiue, Ljubljana, Slovenia Špica Inernaional d.o.o., Ljubljana, Slovenia {bogdan.pogorelc, Absrac. We presen a generalized daa mining approach o he deecion of healh problems and falls in he elderly for he purpose of prolonging heir auonomous living. The inpu for he daa mining algorihm is he oupu of he moion-capure sysem. The approach is general since i uses a k-nearesneighbor algorihm and dynamic ime warping wih he ime series of all he measurable join angles for he aribues insead of a more specific approach wih medically defined aribues. Even hough he presened approach is more general and can be used o differeniae oher ypes of aciviies or healh problems, i achieves very high classificaion accuracies, similar o he more specific approaches described in he lieraure. Keywords: healh problems, aciviies, falls, elderly, machine learning, daa mining. 1 Inroducion The amoun of elderly people in he developed counries is high and is increasing [20]. In many cases elderly fear being unable o obain help if hey are injured or ill. In recen decades his fear has resuled in research aemps o find assisive echnologies o make he living of elderly people easier and more independen. The aim of his sudy is o provide ambien assisive-living services o improve he qualiy of life of older aduls living a home. We propose a generalized approach o an inelligen and ubiquious care sysem o recognize a few of he mos common and imporan healh problems in he elderly, which can be deeced by observing and analyzing he characerisics of heir movemen. I is wo-sep approach as shown in Figure 1. In he firs sep i classifies he person's aciviies ino five aciviies, including wo ypes of falls. In he second sep i classifies classified walking insances from he firs sep ino five differen healh saes: one healhy (N) and four unhealhy. The aciviies are: fall (F), unconscious fall (UF), walking (W), sanding/siing (SS), lying down/lying (L). The ypes of abnormal healh saes are: hemiplegia (usually he resul of a sroke, H), Parkinson s disease (P), pain in he leg (L), pain in he back (B). The movemen of he user is capured wih a moion-capure sysem, which consiss of ags aached o he body, whose coordinaes are acquired by sensors lo- adfa, p. 1, Springer-Verlag Berlin Heidelberg

2 caed in he aparmen. The oupu ime series of he coordinaes are modeled wih he proposed daa-mining approach in order o recognize he specific aciviy or healh problem. The archiecure of he sysem is presened in Figure 1. Oher (SS, L) Do nohing Normal healhy sae (N) elderly Moion capure Time series Aciviy/fall recogniion sysem walking Healh problem recogniion sysem Healh problem (H,P,L,B) Fall (F, UF) Call for medical help Fig. 1. Archiecure of he sysem 1.1 Relevance of he presened work for he semanic ambien media Ambien-assised living can be combined wih inelligen sysems ino a new sub-area of semanic ambien media healh-monioring media which is he area of he presened paper. In line wih he definiion of media, he healh monioring medium is a ransmission ool used o deliver informaion (conen) from a physical world o a digial world and vice versa. I uses sensors o capure daa from he environmen; i.e., body movemen. Then, he machine learning and daa mining [24] are used o model he underlying processes ha have generaed he colleced daa. The models explain he daa and hey are used o recognize healh problems of elderly, which is informaion delivered o a digial world. This informaion is ransmied back o a physical world hrough is represenaion o a user, i.e., presenaion of he diagnosis o a medical exper. 1.2 Organisaion of he sudy The presened sudy is organized as follows. The second secion provides a review of he relaed work in he field. The hird secion gives a descripion of he daa and he mehods ha were used in he sudy. The fourh secion presens he experimens and resuls, suppored by ables. The las secion concludes he sudy and provides ideas for furher work. 2 Relaed work In relaed sudies he moion is normally capured wih inerial sensors [19, 1], compuer vision and also wih a specific sensor for measuring he angle of join deflecion [15] or wih elecromyography [21]. In our sudy an infra-red (IR) sensor sysem wih ags aached o he body [5] was used. 30

3 We do no only address he recogniion of aciviies of daily living, such as walking, siing, lying, ec. and he deecion of falling, which has been addressed many imes [3, 10], bu also he recogniion of healh problems based on moion daa. Using a similar moion-capure sysem o ha in our approach, he auomaic disincion beween healh problems such as hemiplegia and diplegia is presened [9]. However, a much more common approach o he recogniion of healh problems is he capuring of movemen ha is laer manually examined by medical expers [15, 4, 12]. Such an approach has a major drawback in comparison o ours, because i needs o be consanly moniored by medical professionals. The paper [11] presened a review of assisive echnologies for care of he elderly. The firs echnology consiss of a se of alarm sysems insalled a people s homes. The sysem includes a device in he form of a mobile phone, a pendan or a chainle ha has an alarm buon. They are used o aler and communicae wih a warden. When he warden is no available, he aler is sen o he conrol cener. However, such devices are efficien only if he person recognizes he emergency and has he physical and menal capaciy o press he alarm buon. The second echnology presened in [11] is video-monioring. The audiovideo communicaion is done in real ime over an ordinary elephone line. The video can be viewed on a monior or domesic elevision. The problems of he presened soluion are ehical issues, since elderly users do no wan o be moniored by video [3]. Moreover, such an approach requires he consan aenion of he emergency cener. Miskelly [11] also presened a echnology based on healh moniors. The healh monior is worn on he wris and coninuously moniors he pulse, skin emperaure and movemen. A he beginning of he sysem s use, he paern for he user is learned. Aferwards, any deviaions are deeced and alarms are sen o he emergency cener. Such a sysem deecs collapses, fains, blackous, ec. Anoher presened echnology is he group of fall deecors. They measure he acceleraions of he person using ags worn around he wais or he upper ches. If he acceleraions exceed a hreshold during a ime period, an alarm is raised and sen o he communiy alarm service. Bourke e al. [2] presened he acceleraion daa produced during he aciviies of daily living and when a person falls. The daa was acquired by monioring young subjecs performing simulaed falls. In addiion, elderly people performed he aciviies of daily living. Then, by defining he appropriae hreshold i is possible o disinguish beween he acceleraions during falls and he acceleraions produced during he normal aciviies of daily living. In his way acceleromeers wih a hreshold can be used o monior elderly people and recognize falls. However, hreshold-based algorihms produce misakes, for insance, quickly sanding up from or siing down on a chair could resul in crossing he hreshold, which is erroneously recognized as a fall. Rudel [16] proposed he archiecure of a sysem ha enables he conrol of users in heir homes. I consiss of hree levels. The firs level represens he ill people in heir homes equipped wih communicaion and measuremen devices. The second level is he informaion and communicaion echnology ha enables he communicaion wih he main server. The hird level is he elemedicine cener, including he duy operaor, docors and echnical suppor, he cener for he implemenaion of direc assisance a home, and he eam of expers for implemening he elemedicine 31

4 services. Such a sysem does no provide any auomaic deecion of unusual behavior bu insead requires consan observaion by he medical cener. Perolle e al. [13] described an elderly-care sysem ha consiss of a mobile module worn by he user all he ime ha is able o locae he user, deec falls and monior he user s aciviy. In addiion, his device is conneced o a call cener, where he daa is colleced, analyzed, and emergency siuaions are managed. The mobile module is worn on a bel. I produces an alarm, provides he possibiliy o cancel i, shows he baery saus, ec. In addiion, i moniors he user aciviy and gives i hree classificaions: low, medium and high. Once a day, he daa is sen o he call cener for analysis. The user is locaed wih a GPS, for when i is necessary o respond o alarms and o locae he user if he/she ges los. The mobile module also provides bidirecional voice communicaion beween he user and he call cener in order o communicae criical informaion immediaely. The sudies [14, 22] differeniae beween he same five healh saes as presened in his sudy, bu are more specific due o he use of 13 medically defined aribues. The currenly presened sudy insead uses very general aribues of he angles beween body pars, allowing he sysem o use he same aribues and he same classificaion mehods for differeniaing beween five aciviies and beween five healh saes. The aim of his sudy is o realize an auomaic classifier ha is able o suppor he auonomous living of he elderly by deecing falls and healh problems ha are recognizable hrough movemen. Earlier works (e.g., [7]) describe machinelearning echniques employed o analyze aciviies based on he saic posiions and recognized posures of he users. Alhough hese kinds of approaches can leverage a wealh of machine-learning echniques, hey fail o ake ino accoun he dynamics of he movemen. The presen work has insead he aim o recognize movemens by observing he ime series of he movemens of he users. Beer aciviy-recogniion performance can be achieved by using paern-maching echniques, which ake ino accoun all of he sensors readings, in parallel, considering heir ime course. 3 Maerials and mehods 3.1 Targeed aciviies and healh problems for deecion The proposed sysem uses a wo-sep approach for he recogniion of imporan siuaions. All he siuaions ha we are recognizing were suggesed by he collaboraing medical exper on he basis of occurrence in he elderly aged over 65, he medical significance and he feasibiliy of heir recogniion from movemens. Thus, in he firs sep we are recognizing five aciviies: accidenal fall, unconscious fall, walking, sanding/siing, lying down/lying. We are focusing on differeniaing beween "accidenal fall" and "unconscious fall": Accidenal fall: as he name suggess i happens due o an acciden. The ypes of accidenal falls are, e.g., sumbling and slipping. If he person does no hur him/herself afer i, he/she does no need medical aenion. 32

5 Unconscious fall: his happens due o an illness or a shor loss of consciousness. In mos cases he person who falls in his way needs medical aenion. The oher hree aciviies of ineres are common aciviies a home, also known as he aciviies of daily living (ADL). In he second sep we focused on four healh problems and normal walking as a reference in accordance wih he suggesions received from he collaboraing medical exper. The following four healh problems were chosen as he mos appropriae [4]: Parkinson s disease: a degeneraive disease of he brain (cenral nervous sysem) ha ofen impairs moor skills, speech, and oher funcions. The sympoms are frequenly remor, rigidiy and posural insabiliy. The rae of he remor is approximaely 4 6 Hz. The remor is presen when he involved par(s), usually he arms or neck, are a res. I is absen or diminished wih sleep, sedaion, and when performing skilled acs. Hemiplegia: is he paralysis of he arm, leg and orso on he same side of he body. I is usually he resul of a sroke, alhough diseases affecing he spinal cord and he brain are also capable of producing his sae. The paralysis hampers movemen, especially walking, and can hus cause falls. Pain in he leg: resembles hemiplegia in ha he sep wih one leg is differen from he sep wih he oher. In he elderly his usually means pain in he hip or in he knee. Pain in he back: his is similar o hemiplegia and pain in he leg in erms of he inequaliy of seps; however, he inequaliy is no as pronounced as in walking wih pain in he leg. The classificaion ino five aciviies and ino five healh problems was made using he k-neares-neighbor machine-learning algorihm and dynamic ime warping for he similariy measure. 3.2 Aribues for daa mining The recordings consised of he posiion coordinaes for he 12 ags ha were worn on he shoulders, he elbows, he wriss, he hips, he knees and he ankles, sampled a 10 Hz. The ag coordinaes were acquired wih a Smar IR moion-capure sysem wih a 0.5-mm sandard deviaion of noise. From he moion-capure sysem we obain he posiion of each ag in x-y-z coordinaes. Achieving he appropriae represenaion of he user s behavior aciviy was a challenging par of our research. The behavior needs o be represened by simple and general aribues, so ha he classifier using hese aribues will also be general and work well on behaviors ha are differen from hose in our recordings. I is no difficul o design aribues specific o our recordings; such aribues would work well on hem. However, since our recordings capured only a small par of he whole range of human behavior, overly specific aribues would likely fail on general behavior. 33

6 Considering he above menioned, we designed aribues such as he angles beween adjacen body pars. The angles beween body pars ha roae in more han one direcion are expressed wih quaernions: q SL and ime HL TU EL q SR HR... lef and righ shoulder angles wih respec o he upper orso a q and q... lef and righ hip angles wih respec o he lower orso TL q and q... he angle (orienaion) of he upper and of he lower orso ER,, angles. KL and KR... lef and righ elbow angles, lef and righ knee 3.3 Dynamic Time Warping We will presen dynamic ime warping (DTW) as a robus echnique o measure he disance beween wo ime series [8]. Dynamic Time Warping aligns wo ime series in such a way ha some disance measure is minimized (usually he Euclidean disance is used). Opimal alignmen (minimum disance warp pah) is obained by allowing he assignmen of muliple successive values of one ime series o a single value of he oher ime series and herefore he DTW can also be calculaed on ime series of differen lenghs. The ime series have similar shapes, bu are no aligned in ime. While he Euclidean disance measure does no align he ime series, he DTW does address he problem of ime difference. By using DTW an opimal alignmen is found among several differen warp pahs. This can be easily represened if wo ime series A = (a 1, a 2,..., a n ) and B = (b 1, b 2,..., b m ), a, b R are arranged o form a n-by-m grid. Each i j a grid poin corresponds o an alignmen beween he elemens i A b and j B. A W w warp pah 1, w2,..., wk,... wk is a sequence of grid poins where each wk corresponds o a poin ( i, j ) k he warp pah W maps elemens of sequences A and B. From all possible warp pahs he DTW finds he opimal one [23]: (, ) min K DTW A B W d( w k) k 1 The d(wk) is he disance beween he elemens of he ime series. The purpose of DTW is o find he minimum disance warp pah beween wo ime series. Dynamic programming can be used for his ask. Insead of solving he enire problem all a once, soluions o sub-problems (sub-series) are found and used o repeaedly find he soluion o a slighly larger problem. Le DTW(A, B) be he disance of he opimal warp pah beween ime series A = (a 1, a 2,..., a n ) and B = (b 1, b 2,..., 34

7 b m ) and le D(i, j) = DTW (A, B ) be he disance of he opimal warp pah beween he prefixes of he ime series A and B: D(0, 0) 0 A' ( a, a,..., a ), B ' ( b, b,..., b ) 1 2 i i n,0 j m j DTW(A, B) can be calculaed using he following recursive equaions: D(0, 0) 0 D( i, j) min( D( i 1, j), D( i, j 1), D( i 1, j 1)) i j d( a, b ), The disance beween wo values of he wo ime series (e.g. he Euclidean disance) is d(a i, b j ). The mos common way of calculaing DTW(A, B) is o consruc a n*m cos marix M, where each cell corresponds o he disance of he minimum disance warp pah beween he prefixes of he ime series A and B: M ( i, j) D( i, j) 1 i n,1 j m Procedure sars by calculaing all he fields wih small indexes and hen progressively coninues o calculae he fields wih higher indexes: for i = 1...n for j = 1...m M(i,j) = min(m(i-1,j), M(i,j-1), M(i,j)) + ds(a i,b j ) The value in he cell of a marix M wih he highes indexes M(n,m) is he disance corresponding o he minimum disance warp pah. A minimum disance warp pah can be obained by following cells wih he smalles values from M(n,m) o M(1, 1). Many aemps o speed up DTWs have been proposed [18]; hese can be caegorized as consrains. Consrains limi he minimum disance warp pah search space by reducing he allowed warp along he ime axis. The wo mos commonly used consrains are he Sakoe-Chiba Band [17] and Iakura Parallelogram [6]. 3.4 Modificaion of he algorihm for mulidimensional classificaion The DTW algorihm commonly described in he lieraure is suiable for aligning onedimensional ime series. This work employed a modificaion of he DTW, which makes i suiable for mulidimensional classificaion. Firs, each ime poin of he capured ime series consising of he posiions of he 12 ags coming ou of he moion-capure sysem is ransformed ino angle 35

8 aribue space, as defined before. The classificaion is hen performed in he ransformed space. To align an inpu recording wih a emplae recording (on which he classifier was rained), we firs have o compue he marix of local disances, d(i,j), in which each elemen (i, j) represens he local disance beween he i-h ime poin of he emplae and he inpu a he ime j. Le C js be a generic aribue-vecor elemen relaive o a emplae recording, and Q is be he aribue-vecor elemen relaive o a new inpu recording o recognize, where 1s N is he considered aribue. For he definiion of disance he Euclidean disance was used, defined as follows: 2 N Euc s1 js is d C Q The value of he minimum global disance for he complee alignmen of he DTW procedure, i.e., he final algorihm oupu, is found in he las column and row, D(Tr, Tr). The opimal alignmen can also be efficienly found by back racing hrough he marix: he alignmen pah sars from D(Tr, Tr), hen i proceeds, a each sep, by selecing he cell ha conains he minimum cumulaive disance beween hose cells allowed by he alignmen pah consrains unil D(1, 1) is reached. 4 Experimens and resuls The DTW algorihm aemps o srech and compress an inpu ime series in order o minimize a suiably chosen disance measure from a given emplae. We used a neares-neighbor classifier based on his disance measure o design he algorihm as a fall deecor and a disease classifier. The classificaion process considers one inpu ime series, comparing i wih he whole se of emplaes, compuing he minimum global disance for each alignmen and assuming ha he inpu recording is in he same class of he emplae wih which he alignmen gives he smalles minimum global disance (analogous o insance-based learning). The proposed algorihms were esed wih he mehodology and he daa se described in he sudy. The 10-fold cross-validaion for he 5-neares-neighbor classifier resuled in a classificaion accuracy of 97.5 % and 97.6 % for he aciviies and healh problems, respecively. The resuls show ha in he proposed approach false posiives/negaives are very rare, i.e., hey would no cause many unnecessary ambulance coss. Since he mehod accuraely classified mos real healh problems, i represens high confidence and safey for is poenial use in he care of he elderly. 5 Conclusion This sudy presened an unobrusive semanic healh-monioring medium for he discovery of healh problems and falls in he elderly for he purpose of prolonging he auonomous living of he elderly using ime-series daa mining. I is general in he sense ha i does no use specific medically defined aribues bu he general ap- 36

9 proach of a combined k-neares-neighbor algorihm wih mulidimensional dynamic ime warping. I is a wo-sep approach. In he firs sep i classifies he person's aciviies ino five aciviies, including differen ypes of falls. In he second sep i classifies walking paerns ino five differen healh saes: one healhy and four unhealhy. Even hough he new approach is more general and can also be used o classify oher ypes of aciviies or healh problems, i sill achieves high classificaion accuracies, similar o he more specific kind of approach. Acknowledgemens The operaion leading o his sudy is parially financed by he European Union, European Social Fund. The auhors hank Marin Tomšič, Bojan Nemec and Leon Žlajpah for heir help wih daa acquisiion and Anon Gradišek for lending his medical experise. References 1. Bourke A.K e al. An opimum acceleromeer configuraion and simple algorihm for accuraely deecing falls. In Proc. BioMed 2006 (2006), Bourke A K, O'Brien J V, Lyons G M. Evaluaion of a hreshold-based ri-axial acceleromeer fall deecion algorihm, Gai&Posure 2007; 26: Confidence Consorium. Ubiquious Care Sysem o Suppor Independen Living. hp:// 4. Craik R. and Oais C. Gai Analysis: Theory and Applicaion. Mosby-Year Book (1995). 5. emoion. Smar moion capure sysem. hp:// 6. Iakura, F Minimum predicion residual principle applied o speech recogniion. Acousics, Speech and Signal Processing, IEEE Transacions on 23(1): B. Kaluza, V. Mirchevska, E. Dovgan, M. Lusrek, M. Gams, An Agen-based Approach o Care in Independen Living, Inernaional Join Conference on Ambien Inelligence (AmI-10), Malaga, Spain 8. E. Keogh and C. A. Raanamahaana, Exac indexing of dynamic ime warping, Knowl. Inf. Sys., vol. 7, no. 3, pp , H. Lakany, Exracing a diagnosic gai signaure. Pa. recogniion 41(2008), Lušrek, M., and Kaluža, B. Fall deecion and aciviy recogniion wih machine learning. Informaica 33, 2 (2009). 11. Miskelly F G. Assisive echnology in elderly care. Age and Ageing 2001; 30: Moore ST, e al., Long-erm monioring of gai in Parkinson s disease, Gai Posure (2006). 13. Perolle G, Fraisse P, Mavros M, Exeberria L. Auomaic fall deecion and aciviy monioring for elderly. COOP HEBE Cooperaive Research Projec- CRAFT. Luxembourg Pogorelc B, Bosnić Z, Gams M (2011) Auomaic recogniion of gai-relaed healh problems in he elderly using machine learning. Mulimed Tools Appl. doi: /s Ribarič S., Rozman J., Sensors for measuremen of remor ype join movemens, MIDEM 37(2007)2, pp Rudel D. Zdravje na domu na daljavo za sare osebe. Infor Med Slov 2008; 13(2):