A Falling Detection System with wireless sensor for the Elderly People Based on Ergnomics

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1 Vol.8, o.1 (14), pp A Fallng Detecton System wth wreless sensor for the Elderly People Based on Ergnomcs Zhenhe e 1, ng L, Qaoxang Zhao and Xue Lu 3 1 College of Mechancal and Electrcal Engneerng, Agrcultural Unversty of HeBe, Baodng, HeBe, 71, Chna College of Arts, Hebe ormal Unversty of Scence & Technology, Qnhuangdao, 66, Chna 3 School of Computer Scence, Harbn Unversty of Scence and Technology, Harbn, HeLongJang, 158, Chna yezhenhe@163.com Abstract Fall detecton s an mportant problem n the applcaton research of wreless sensor. The paper presents wreless sensor archtecture based human fallng detecton system especally for elderly people. The fallng detecton system s mplemented usng 3-axs acceleraton sensor to measures and collects the elderly people actvtes acceleraton and transfer data by zgbee-3g network to remote medcal montorng system platform, whch makes a preprocessng method that suspected data s acqured based on one -class SVM classfcaton algorthm. The algorthm analyzed dfferent acton whch expended dfferent threshold ranges of energy to udgment, and then analyzed the specal temporal speed, dsplacement and angle as an auxlary crteron for udgment. The experments show that the applcaton can offer a new guarantee for the elderly health. Keywords: fall detecton, tr-axal accelerometer, one-class SVM, zgbee 1. Introducton Currently, the world faces more agng problem. Chna s also agng populaton problem, wth more and more old people and quck rsng rate. Accordng to statstcs, Chna's elderly populaton aged over 6 was more than 1.86 bllon n 1,t made up14.3 percent of Chnese populaton. Snce 198, Chna's elderly populaton aged over 6 wth an average annual rate of 3.3 % growth. By 5 socety wll enter an advanced stage of ageng. Chna's populaton s agng at unprecedented levels. The fallng s qute common among the elderly. Accordng to one estmate, fallng had happened on 1 /3 person aged over 65 every year, whch wll ncrease along wth the ncrement of age. Fallng s the leadng cause of elderly accdental nury. And t s lkely to lead to fall when accdental And t s lke]y to lead to fall when accdental slp or some dsease attack suddenly and deteroraton. If not promptly tmely rescue, delay salvage tmng when the fall happened wll be lfe-threatenng. Therefore, the development desgned for elderly servce produets-elderly fall montorng system whch can accurately dfferentate between fall events and actve daly lfe events, and can tmely alarm and contact ambulance n the cases of not affect older people s normal lfe, t has mportant role n mprovng the lfe qualty of elderly and promotng the stablty of our socety [1-4]. owdays, we can classfy the fall detecton technology n three ways from the dfferent sgnal samplng: A fall detecton technology based vdeo technology, has the weak scrpt of lmted detecton range and t may dsclose the personal prvacy; A fall detecton technology ISS: IJSH Copyrght c 14 SERSC

2 Vol.8, o.1 (14) based sound technology wth lttle accuracy, s closely related wth dfferent ground and floor, A fall detecton technology based wearable sensor technology s the wearable devce wth mcrosensors, such as glasses, hats, shoes, dress and so on. It can be real-tme montorng of human actvtes. When body movement parameters changed, t may determne f the elderly fallng down. Fall detector s more sutable for fall detecton system based on wearable sensor because t not beng subect to the lmtatons of the detecton locaton [5-6]. Ths study desgned a new devce, whch could be attached to the wast of the users, for detectng the fallng of the elderly people based on the data acquston functon of the acceleraton sensor. For the stage of data preprocessng, a classfcaton algorthm based on 1- class SVM was proposed. The fnal fallng udgment was made accordng to the dfference of the greatest energy consumpton of the human body (the dfference of the threshold range) n dfferent actons. To ensure the accuracy, three addtonal Egen values of the human body (velocty, shft and ttle angle) n the specfc tme doman were also ncluded as an auxlary udgment bass.. The Desgn of the Fallng Detecton Module.1. The Overall Structure The overall structure of the fallng detecton system for the elderly people based on Zgbee s shown n the followng fgure [7, 8]. It conssts of three components: the portable module contanng Zgbee nodes for fallng detecton, the Zgbee-3G network and the remote medcal montorng system platform. The detaled network structure s as follows: the acceleraton collectng unt of the portable fallng detecton module collects the raw acceleraton data; the mcroprocessor unt preprocesses the sgnal; the suspcous data captured durng the preprocessng stage s transmtted va the wreless sensor network of the remote medcal montorng devce (the wreless sensor network consttutes of the nodes of the wreless Zgbee sensor, the mult-level routng nodes and the concentrator) [9]; the concentrator ntegrates the Zgbee-3G network and sends the data to the remote fallng detecton system; the data processng system of the remote fallng detecton system analyzes the relevant data comprehensvely. If a fallng acton s confrmed, the system wll automatcally trgger the alarm [1, 11]. The nteractve unt manly conssts of the functon keys, the LED ndcators and the buzzer. The functon keys enable the users to actvate an alarm or cancel a false alarm. The LED ndcators are manly used to dsplay the connecton status of the communcaton network. The buzzer can obtan a feedback alarm sgnal when the system detects a fallng. Portable remote medcal montorng devce (hurryng acquston & preprocessng) Zgbee-3G etwork (Data transmsson) The remote fallng detecton system (Data processng and analyss) Fgure 1. The Overall Structure.. The Zgbee etwork The wreless sensor network s also known as the Zgbee network. It has a seres of unque features, such as short dstance, low complexty, self-organzaton, low power consumpton, low data rate and low cost. The Zgbee network s manly sutable for the applcaton felds of 188 Copyrght c 14 SERSC

3 Vol.8, o.1 (14) automatc control and remote control. It can be embedded nto a varety of devces. From the frst verson ZgBee 4 whch was released n February 4 to ZgBee 7 (ZgBee PRO) whch was released n 7, ths system has been constantly optmzed n terms of network relablty, network capacty, power consumpton, nterference resstance and many other aspects. Comparng wth the prevous versons, ZgBee 7/pro has acheved the followng techncal mprovements: 1) ZgBee 7/pro uses the ndustral and commercal grade protocol; ) ZgBee 7/pro s sutable for large-scale networks (large-scale wreless sensor networks contanng more than 1 nodes); 3) ZgBee 7/pro apples an enhanced routng method. The routng has become more relable, and the routng table can save more memory; 4) ZgBee 7/pro s equpped wth the hgh-level frequency hoppng technology, so t has a strong ant-nterference ablty; 5) ZgBee 7/pro can transmt large-sze data packet va multple packages; 6) ZgBee 7/pro provdes the commercal grade encrypton communcaton..3. The 3-axs Acceleraton Sensor Ths study appled ADXL345 whch s manufactured by ADI Company as the fallng detecton equpment. ADXL345 s a 3-axs acceleraton sensor wth dgtal outputs based on MEMS technology. Its man features are as follows: multple measurng ranges ncludng +/-, +/-4, +/-8 and +/-16g; hghest resoluton: 13bt; fxed senstvty: 4mg/LSB; super low power consumpton: 4-145uA; standard IC or SPI dgtal nterface; Level-3 FIFO storage; multple moton state detecton; and flexble termnaton mode. These features can greatly smplfy the fallng detecton algorthm, so ADXL345 s very sutable for fallng detecton. Its pn dagram s shown below:.4. Sgnal Preprocessng Fgure. Pn Dagram of ADXL345 Frstly, the 13-step medan flter s used to remove the nose of the sample data obtaned by the acceleraton acqurng module. Then, a hgh pass flter wth the cutoff frequency of.5hz and a.8s non-overlappng wndow superposton s adopted to elmnate the gravty factor [1, 14]. The output s the dynamc acceleraton sgnal to be used n the next step. After the flterng process, the classfcaton algorthm based on 1-class SVM s adopted to extract the suspcous data. 1-class SVM algorthm s an extended verson of the SVM algorthm. It uses Copyrght c 14 SERSC 189

4 Vol.8, o.1 (14) the kernel functon to map all the samples nto a hgh-dmensonal egenspace to acheve the purpose of classfcaton. In the egenspace, 1-class SVM attempts to determne the mnmum hyper sphercal surface contanng all the target data. Ths surface wll be the classfer. A set of slack varables s used to control the radus of the hyper sphere and the number of samples that are outsde the super sphere. Ths algorthm s able to extract most of the fallng samples (postve samples). d X { x, 1,,, }, For the postve sample set l x R, to determne the hyper sphere (wth the center of vector a and the radus of R ) whch contans as many samples as possble through nonlnear mappng nto the hgh-dmensonal egenspace s actually an optmzaton problem that can be represented by equaton (1). mn R 1/ d RR, R, af Where, vl (1) ( ),, 1, x a R l () Here, F s an egenspace; s a slack varable; 1/vl determnes the volume of the sphere and the number of classfcatons that the samples outsde the sphere can be classfed; v(,1) ; l represents the number of samples. Based on the KKT condton, the followng kernel functon s ntroduced: K( x, y) ( x) ( y) (3) That s, the optmzaton equaton (1) can be expressed as: mn K( x, x ) K( x, y ), (4) 1, 1 Where, vl, the center of the hyper sphere s: a( x) (5) After tranng, a set of support vectors can be obtaned. Then, Equaton (6) s used to calculate the radus R. R K x x K xs xs K x xs, (, ) (, ) (, ) x Here, s an arbtrary support vector. Its decson functon s: f ( x) sgn R K( x, x ) K( x, x) K( x, x), (7) For all the samples, f f( x), then ths sample s a postve sample; f f( x), then ths sample s a non-postve sample. Generally, RBF s used as the kernel functon. (6) 19 Copyrght c 14 SERSC

5 Vol.8, o.1 (14) K x z x z (, ) exp( / ) It has been proved that t s very effcent to dentfy the fallng actons from low-ntensty daly actvtes, but when the actvty ntensty s hgh, t wll be relatvely dffcult to dentfy the fallng actons. Therefore, ths algorthm s only used durng the data preprocessng stage n order to extract suspcous data from the orgnal data. By adustng the slack varable n the experment, t can ensure that more than 97% of the real fallng sample data can be extracted. 3. The Fallng Detecton Algorthm 3.1. The Establshment of the Acton Model When human body s n a fallng process, the acceleraton, velocty and shft of the obect n all drectons wll change. In fact, t s very dffcult to comprehensvely dentfy the fallng acton only n accordance wth the acceleraton change. The velocty () v can be obtaned by conductng one tme of ntegral of the acceleraton n the tme doman and the shft () s can be obtaned by conductng two tmes of ntegral. Ths s to mprove the system accuracy. The acceleraton data obtaned by the acceleraton sensor always contans two parts: the acceleraton caused by gravty and the acceleraton caused by human moton. The 3D acton model s bult based on the acceleraton caused by human moton, and accordng to the three orthogonal measurng drectons of the acceleraton sensor, the 3D coordnate system can be establshed as shown n Fgure 3. Fgure 3. The Wearng Poston of Fall Detecton Devce and Coordnate System Copyrght c 14 SERSC 191

6 Vol.8, o.1 (14) If the devce s correctly attached to the users and when the obect s at rest or n unform moton, the acceleraton n drecton should be the acceleraton of gravty (g) and the acceleraton n the horzontal drecton should be. When the obect s fallng, f only consderng the acceleraton change between the ntal and the fnal state, the change n the vertcal drecton should be from 1g to g, whle the change n the horzontal drecton (x or z) should be from g to 1g. Based on the coordnates, vector A A ( a,, ) can be expressed as x ay az A u, where, A s the module of vector A ; u s the unt vector n the same drecton. Then, the tlt angle of the human body (the angle wth the drecton of the acceleraton of gravty.) s: arccos( a/ g), where, a s the statc output n each axs and g s the acceleraton of gravty. 3.. The Algorthm Desgn Accordng to the dfference of energy consumpton when the human body s n walkng, standng and fallng state, the threshold range of the energy consumpton of dfferent movements can be obtaned through experments. Hence, ths study proposed the method to analyze the background data usng energy consumpton. The energy consumpton s the ntegral of the square of the dynamc acceleraton n the specfc tme doman, whch can be expressed by Equaton (9): EnergyExpe (9) ndture ala a x dt y dt z dt ( ) In ths paper, a 1 ; the calculaton of the energy consumpton consders each samplng wndow as the unt. Accordng to the actual lvng condtons of the elderly people, the correspondng fallng detecton algorthm s desgned. Fgure 3 shows the flow chart of the overall algorthm, whch descrbes the entre fallng detecton process. In ths algorthm, the process from the begnnng to data transmsson s acheved at the user termnal. The whole sgnal preprocessng takes one samplng wndow as the basc unt. If suspcous data s detected durng a samplng wndow, then the data of ths unt wll be transmtted to the background for further confrmaton; otherwse, ths data segment wll be dscarded and the system wll contnue wth the next sgnal. In the background data processng, the udgment s made manly based on the analyss of energy consumpton. Meanwhle, n order to mprove the detecton accuracy, the nformaton of v s of the human body n the specfc tme doman s also ncluded as the supported data as shown n Fgure 4 [15, 17]. 19 Copyrght c 14 SERSC

7 Vol.8, o.1 (14) 4. Experments and Result Analyss Fgure 4. Algorthm Flow 1 elderly people wth the age over 6 were selected for a tral to verfy the accuracy of the algorthm, but of the safety reason, they were not nvolved n the fallng experment. Instead, some students were nvted to smulate the fallng (on a protecton mat). The modes of fallng n the experments are: fallng forward/backward wthout lyng down, fallng forward/backward followed by lyng down, fallng leftward/rghtward. Accordng to these modes, a set of actons as shown n Table 1 was desgned. In each experment, the partcpant was requred to choose some actons from ths table randomly and combne wth the real fallng acton to form a complete set of expermental acton. The system frstly collected the sample data at the samplng frequency of 45Hz, and then, processed the data by usng the proposed algorthm. Each partcpant was requred to conduct 5 sets of experment, and n each experment, they would choose an acton combnaton randomly from the above actons. In total, 5 sets of experment were completed. The experment results are shown n Table 1. It can be seen that the proposed fallng detecton alg8orthm has acheved a hgh accuracy. It can dentfy most of the fallng actons, but for the fallng actons wthout lyng down and the slppng actons wth a quck recovery to the balance state, there were some false udgments. Copyrght c 14 SERSC 193

8 Vol.8, o.1 (14) Table 1. Experment Data Acton Standng Steady walkng Stumblng forward wth a quck recovery to the balance state Fallng forward followed by lyng down Clmbng up the stars Slppng backward wth a quck recovery to the balance state Fallng backward followed by lyng down Clmbng down the stars Fallng forward wthout lyng down Fallng backward wthout lyng down Fallng rghtward Fallng leftward Beddng eed to trgger alarm? o. of experment o. of alarm o. of non-alarm Accuracy % Conclusons Ths study establshed a fallng detecton module based on the 3D acceleraton sensor, the mcroprocessor and the wreless communcaton technology. Its valdty for dstngushng daly actvtes and fallng actons has been proved through experments. In the stage of data preprocessng, the data classfcaton algorthm based on 1-class SVM was adopted to extract the suspcous data. Meanwhle, a creatve method whch s based on the dfference of energy consumpton n dfferent human actons was proposed to make the fnal fallng udgment. In addton, n order to enhance the system accuracy, the analyss about the human body posture (analyss about the velocty, shft and the tlt angle of the human body) n a certan tme doman was also ncluded. However, at the user termnal, the 1-class SVM algorthm should be further optmzed n terms of orgnal data classfcaton (as shown n Fgure 3). References [1] H. Hongwe, Z. Hongke and. Xu, Fall Detecton Usng Rado Sgnals of Home Wreless Sensor etworks, Acta Electronca Snca, vol. 39, no. 1, (11), pp []. Hu, Z. Xaoyue, Z. Lxn and C. uzhen, Elderly fall montorng and remote assstance system, Computer Engneerng and Applcatons, vol. 47, no. 35, (11), pp [3] X. uan and G. Xangyang, A Desgn for Fall Detecton Montorng System Based on Informaton Fuson of Mult-sensor, Journal of Wuhan Unversty of Technology(Informaton & Management Engneerng), vol. 33, no. 5, (11), pp [4] V. Chan, P. Ray and. Parameswaran, Moble e-health montorng: an agent-based approach, IET Commun, vol., no., (8), pp [5] M. Huo, M. Wu and H. Hou, Research on Fall Detecton and Daly Actvty Montorng Technologes for Older People, Internatonal Journal of Dgtal Content Technology and ts Applcatons, vol. 6, no. 16, (1), pp [6] S.-W. Lee and S.-H. Song, A Montorng System for Assessng Lfe Pattern of the Elderly Lvng Alone, Advances n Informaton Scences and Servce Scences, vol. 3, no. 7, (11), pp Copyrght c 14 SERSC

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