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Proceedings of the Second Joint EMBSBMESConferenceHouston, TX, USA October 23-26,2002

Distributed intelligent architecture for falling detectionand physical activity analysis in the elderlyM. Prado, J. Reina-Tosina, L. RoaBiomedical Engineering Group, University of Sev ille, Seville, Spain

Absmct - A novel approach for the detection of falls, theanalysis of body postures, mobility and metabolic enerDexpenditure of elderly people has been developed. It is based ona distributed intelligence architecture, supported by a wirelesspersonal area network W A N ) which allows a full 24-hoursupervision of the user, both indoor and outdoor home. Anopen design methodology lets the addition of new sensors forthe on-line monitorization of other biosignals. In this papergeneral guidelines and design issues are reported, with specialemphasis on the Intelligent Accelerometer Unit (IAU), based on'a four-axis acceleiometer, the sign als of which are transmittedto the WPAN server (PSE) for on-line processing. Theavailability of three axis in the median plane provides aninclination measuiament with high sensibility. The IAU can beworn like a patch, fixed to the back, at the height of the sacrum.A prototype of the IA U is cunxn tly under validation phase, inolde r to optimize signal transmission between M U nd PSE.Keywords- F a h , four-axis accelerometers, personal areanetworks, telehealthmre, intelligent sensors, signal processing,ekierly.I. INTRODUCTION

Falls in elderly people cause serious problems, with ademo nstrated -relation to morbidity and mortality with in thispopulation group. Besides, they represent an importantburden for the public health system expenditure. Present-daytechnical advances in MEMS, communications and micro-controllerdDSPs, allow the design of low cos t portablesolutions for the monitorization of movement and on-linefalling detection. During the last years many attempts todesign portable devices offering falling detection have beenpropos ed in the area of hom e telehealthcare. The initialresults confirm the suitability of current technolog ies to givesolutions to this problem.Different studies have shown the possibility of detectingtransitions among postural states together with kinetic ormetabolic parameters, from acceleration records acquiredwith a sensor located on the waist or breast, by applyingdifferent analysis techniques [ I , 21. However, most of thepapers followin g this app roach are based on the po st-analysisof data recorded in a portable datalogger. The paper ofMath ie et al. [3] shows one of the few systems designed forthe on-line measurement of posture, mobility, gait andmetabolic energy expenditure. This approach uses a triaxialpiezoresistive accelerom eter fixed to a belt, above theanterior superior iliac spine. However the design does notallow the monitorization of the user neither in the bathtub,taking a show er, nor during the time intervals when theindividual is in the bedroom with the belt unfastened, and

Contract grant spon sor: Instituto de Salud Carlos I11 (Spain).Contract grant number: FIS 01/0072-01

furtherm ore, it is useless out of hom e. All these arc situationswith a high fall risk [4,5].In this paper we presen t the arc hitecture of a sy stem forfalling detection and metabolic energy expendituremeasurement, with capability to perform posture andmovement analysis, designed for the full-day monitorizationof individuals at home and outside home.11.METHODOLOGY

According to the principle of operation there are threemain classes of fall detectors. The simplest sensors areexclusively based on the inclin ation of the user: This w aythere is no need to introduce accelerometers or performcomplex signal analysis. A representative example is theambulatory fall monitor of Tamura et al. [GI. The secondclass of devices adds an impact sensor (generallypiezoelectric) as a previous stage to falling detection,followed by the detection of inclination. The smrt fallingsensor of Williams et al. [7] is an example of these devices,which have demonstrated a better reliability than the formerclass. The third type of fall detectors can be disting uished forperforming a real-time analysis of static and dynamicaccelerations measured on the body. These detectors mayrecord kinetic and gait analysis during a normal activity.This capability allows research on the cau ses of falls, as wellas detection of modifications in the daily activity of theindividual related to a deterioration of his he r health state.The developed falling detection s y s t em belongs to thethird class discussed above. Its aim is to obtain on-lineposture, kinetic and physical activity parameters, anddetection of falls, in a similar fashion to the approach ofMathie et al. [3]. However, we have included a full % hourmonitorization capacity as an additional requirement, athome and outside home. Up to our knowledge none of theactual systems fulfill this requirement. To ache ve this goalour approach is based on a W A N [8]. This way it ispossible to move the sensor away from peripheral elements,.like the emergency button. The sensor can be fixed to theskin by means of an imperm eable patch, located at the heightof the sacrum, as several recent studies recommend [2, 31.Fig. 1 shows a simple diagram of the anatomical medianplane with the accelerometer asis z (vertical), x (horizontalpertaining to the plane) and m (equidistant o s and z). Theyaxis is orthogon al to the m edian plane.From the architecture point of view, the ac cess of theuser to the PSE , is required to be as easy as possible. Thisunit includes peripheral modules like an emergency button,a display to show the state of the IAU and optic/acousticsignals that c o n f i i the transmission of eventdalarms to thetele-healthcare center. Conn ection is established by mean s ofa Remote Access Unit (RAU) plugged to a PSTN, if the useris at hish er home, or by a WPAN additional client, with aserial link to a mobile phone. The autonomy of the IAU isabout a couple of months, and requires minimum

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maintenance. The IAU detects any hardware failure, and inthis case either informs periodically the PSE with a master-slave protocol or resets itself If the failure impedescommunication with the PSE, this unit warns about thisevent after a time-out of several minutes.With regard to the analyzed signal, several studies haveshown the feasibility to perform posture and kinetic analysisin elderly people by acquiring accelerations in a 5 Hzbandwidth and an amplitude of f g, whenever the sensor islocated near the body gravity center. Taking intoconsideration the intelligence distribution of the system, asample frequency of 40 Hz er channel has been selected forthe IAU. This signal is analyzed in a very simple way by alow cost microcontroller within the sam e IAU, to detect highfrequency accelerations.As the IAU is fixed to the skin, theacceleration components due jolting and bouncing of thesensor are minimized, therefore high frequency componentsmay indicate an impact with a high probability. The IAUfour accelerometer channels are reduced subsequently toonly three channels with a sample frequency of 10 Hz. verysecond, these channels are transmitted to the PSE, where adigital signal processing is performed in a DSP. Theminimum resolution of the IAU has been established in a 5% of g, a value compatible to the noise level of thecapacitive sensors used in the design and which offersenough precision for the determination of the parameters.Every sampled value is encoded in a byte word.

Median Plane! / - AFig. 1:Diagra m of the anatom ical media n plane illustrating thesensor elements together with their axis.111RESULTSA prototype for the IAU has been prepared, following themethodology explained above. In order to measureaccelerations, two ADXL202E units of Analog Devices havebeen used. These modu les are arranged as shown in Fig. 1,with a 3-volt supply. The four-channel duty cycle modulated(DCh4) output of the ADX L202E devices are sampled every20 ms by ports RB7-RB4, of the MicrochipTM IC16LCG6 8-bit CMOS microcontroller. Both ADXL202E devices arerestarted every 18 ms by the microcontroller with a turn-ontime of 1.9 ms. S ignal acquisition for the 4 channels takesonly 0.5 ms, because of an improvement in the decodingalgorithm proposed by the m anufacturer. This algorithm isbased on the interruptions generated by the m icrocontrollerwhenever a rising o r falling edge is detected in any of the 4R B x ports. The microcontroller stays most of the time insleep mode, and is awakened every 18 ms by a WatchDogmodule. This way, mean power consumption of the

microcontroller together with the accelerators, can bereduced below 0.35 mA. With this strategy, the IAU offersautonomy of nearly 2 months working 24 hours daily, with a500mAh Li battery. Its size is approx . 5 cm diameter and 5mm thickness.IV . DISCUSSIONND CONCLUSION

A nov el system has been developed for falling detectionand the an alysis of posture and movement of elderly people.The main advantage of our proposal compared to otherrecently published devices is the capacity to perform dailyon-line discreet monitorization during a full day, indoor andoutdoor home. A prototype of the IAU is currently undervalidation phase in laboratory, in order to optimize signaltransmission between the IAU and the PSE. Althoughseveral alternatives have been studied for wirelesscommunications at 400 MHz and 2.5 GH z bands, powerconsumption of the RF transceivers is still high and M e rwork is being done to achieve the maximum autonomy byresearching on the most appropriate transmission system.Likewise the material that covers the patch is also to beimproved.REFERENCES[ l] B. Najafi, K. A minian, F. Loew, Y. Blanc, and P. Robert, "A nambulatory system for physical activity monitoring in elderly,"presented at 1st Annual International IEEE EMBS Special TopicConference on Microtechnologies in M edicine and Biology., Lyon,France,2000.[2] C. V. C . Bouten, K. T . M. Koekkoek, M. Verduin, R. Kodde,and J. D. Janssen, "A Triaxial Accelerometer and Portable DataProcessing Unit for the Assessment of Daily Physical Activity,"IEEE Tran sactions on BiomedicalEngineering, vol. 44, pp. 13647,1997.[3] M . .I. athie, J. Basilakis, and B. G . Celler, "A System forMonitoring Posture and Physical Activity Using A ccelerometers,"presented at 23rd Annual International Conference of the IEEEEngineering in Medicine and Biology Society., Istanbul, Turkey,2001.[4 ] F. Padilla-Ruiz, A. Bueno-Cavanillas, C. Peinado-Alonso, M .Espigares-Garcia, and R . Galvez-Vargas, "Fr equency ,characteristics and consequences of falls in a cohort ofinstitutionalized elderly patients," Atencion Primaria, vol. 21 , pp.43742,445,1998.[ 5 ] J. H. Downton and K. Andrews, "Prevalence, characteristicsand factors associated with falls among the elderly living at home.,"[6] T. Tamura, T. Yoshimura, F. Horiuchi, Y. Higashi, and T.Fujimoto, "An ambulatory fall monito r for the elderly," presented atProceedings of the 22nd Annual International Conference of theIEEE, hicago, Illinois, USA, 200 0.[7] G. Williams, K. Doughty, K. Cameron, and D. A. Bradley, "Asmart fall and activity monitor for telecare applications," presentedat Proceedings of the 20nd Annual International Conference of th eI EEE, ong Kong, 1998.[SI E. Jovanov, J. Price, D. Raskovic, K. Kavi, T. Martin, and R.Adhami, "W ireles Personal Area Networks in TelemedicalEnvironment," presented at Proceedings of the E E E EMBSInternational Conference on Information Technology Applicationsin Biomedicine, 2000.

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