Sborník vědeckých prací Vysoké školy báňské - Technické univerzity Ostrava číslo 2, rok 2007, ročník LIII, řada strojní článek č.

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1 Sborník vědeckých prací Vysoké školy báňské - echnické univerzity Ostrava číslo rok 007 ročník LIII řada stroní článek č. 9 Viliam DOLINAY * Marek LAMPAR ** Vladimír VAŠEK *** PRENAAL CARE SIGNAL FILERING FILRACE SIGNÁLU V PRENAÁLNÍ PÉČI Abstract his contribution describes methods of processing signals measured by special body belt which is used to monitor pregnant women. As a part of preous research a belt dedicated to clasp belly of pregnant women with potential problems during the gradity was designed. he task is to monitor movement of the belly with focus on catching the moment and length of contraction. Output data are values of belt tension measured by strain gages placed at the ends of belt. hese raw data has to be filtered first and than analyzed to find the contractions. For this task three simple filter algorithms were proposed. After their testing it was found that best results are obtained from their mutual combination. After filtering is done the data are processed by special algorithm to determine whether we found contraction and eventually in what period the contractions are repeating. All above mentioned algorithms are implemented in an experimental program which also performs reading data from belt memory and simple sualization. his paper aimed to describe methods of data filtering algorithm. Abstrakt ento příspěvek popisue metody zpracování signálu získaného při měření pomocí speciálního tělního pásu který e určen k monitorování žen v průběhu rizikového těhotenství. ento pás byl navržen a zkonstruován v rámci dřívěšího výzkumu realizovaným externí organizací. Úkolem pásu e monitorovat pohyb břicha těhotné ženy u nichž se předpokládaí problémy během těhotenství. Měření e zaměřeno na získání informace o počtu kontrakcí a eich periodě opakování během dne. Výstupními daty pásu e hodnota napnutí pásu která e získána z tenzometru spouící konce pásu. ato hrubá data před další analýzou musí být nedříve filtrována a tomu se právě věnue tento příspěvek. Pro tento úkol byly navrženy tři ednoduché filtrační metody. Po testech v reálné situaci vyplynulo že nevhodněší průběh získáme eich vzáemnou kombinací. Zmiňované algoritmy sou implementovány v testovacím programu který rovněž zapouzdřue schopnosti čtení z pásu zualizaci a základní analýzu zaměřenou na rozpoznání kontrakcí. * Ing. Ústav automatizace a řídicí techniky Fakulta Aplikované Informatiky UB ve Zlíně Nad Stráněmi 70 0 Zlín tel. (0) vdolinay@fai.utb.cz ** RNDr. Ph.D. Mathematical Institute in Opava Silesian University in Opava Na Rybníčku 7 0 Opava tel. (0) Marek.Lampart@math.slu.cz *** prof. Ing. CSc. Ústav automatizace a řídicí techniky Fakulta Aplikované Informatiky UB ve Zlíně Nad Stráněmi 70 0 Zlín tel. (0) vasek@fai.utb.cz

2 INRODUCION he invented special body belt monitors pregnant women during risky section of pregnancy. Stream of relative pressure values produced by belt must be filtered before final analyse and sualization. In this case quick motion and breathing of examined woman is typical source of noise. SOLUION here were arranged three easy methods which will construct from a group of initial values one new value this value will approximate real situation of the up to now process. Each of three methods comes from the same kernel process. he kernel of designed algorithm is the procedure of finding a centre of graty of a triangle in the plane and comparing such a center of graty with the following values. Firstly let us denote D as a set of initial data. One can imagine this set as a sequence {[ t ]} k i v i i D : = = () ti are times in which we measure values. All the measuring process is passing in finite time interval so we can assume that the sequence contains k elements. Moreover the measuring process proceeds in constant time intervals we have t t = c () for every =...( k ). Now we will define algorithms (maps) and whose progressing application (compositions) we will receive the final algorithm.. Algorithm Algorithm is arranged in such a way that it assigned to a successive triple of points from D the value of graty of the triangle arranged by this triple of vertexes. he output is a new set of data which contains one third of points with respect to the initial set of data D. Hence is a map from to define by the formula: = e o r () r : D D is defined by the rule

3 e ({ [ t ]} ) [ ] i = t 0 i 0 () ( ) = k mod k i =... ; [[ t i ]] = 0 denotes ordered triple of points [ t i ] t v ] v ] by analogy we will denote ordered senary of points; and is [ i i defined by: [ ti i e : D D e = ([ ti ] ) = ti () = 0 Fig. Algorithm. Algorithm Algorithm is arranged in such a way that it assign to each triple of points from D (resp. D) the value of graty of the triangle arranged by this triple of vertices and then it compares this value of graty with the following two values. If both this values are greater or smaller then the value of the value of graty then it replaces the second one with the value of graty. If one of these values is greater and second one smaller then it replaces the foregoing triple by new value of graty. he output is a new set of data. Consequently is a map from to D defined by: D D = es o s () s : D D is defined by

4 i ( ) k mod k... s ({ [ ti ]} ) = [ ti ] (7) = 0 = 0 = and : D D is defined by es [ t te ] [ t te ] i i i i if < es ([ ti ] ) = or v > = 0 i (8) [ ti ][ ti ] otherwise te i =. Fig. Algorithm. Algorithm Algorithm D is arranged in such a way that it assign to each triple of points from D (resp. or D) the value of graty of the triangle arranged by this triple of vertices and then it compares the foregoing triple of values with the value of graty. If every point from the triple has the value greater or smaller then the value of graty then it replaces the third one with the value of graty. If one value of the triple is smaller (resp. greater) and the last values of the triple are greater (resp. smaller) then it replaces this triple by a new value of graty. he output is a new set of data.. Now is a map from to defined by: D D D

5 = ee o s (9) ee : D D is defined by [ t te ][ t te ] i i i i if < ee ([ ti ] ) = or v > = 0 i (0) [ ti ][ ti ] otherwise te i =. Fig. Algorithm RESULING ALGORIH he resulting algorithm used for filtration of the initial data is the composition of the foregoing algorithms and exactly in this order. Denote it by A. Finally A is defined as a map A : D D by the rule A : o o.

6 Fig. Algorithm A he output depiction is made in a primitive way (i.e. by linear oining of neighbour points from the final set of data). CONCLUSIONS he designed algorithm gives surprisingly first rate results. Its best advantage is its easy way to programme it and its unpretentiousness for the hardware. All the algorithm or its parts can be used for filtrating of similar data of other process. Finally it was made a faithful to the algorithm to be able to work on real time. It was achieved and it is possible to use this algorithm for a mobile hardware. he output graph is a piecewise linear map which is possibly make as a smooth curve by standard methods or by new one. ACKNOWLEDGMENS he work was performed with financial support of grant reg. No 0/0/0 and research proect MSM. REFERENCES [] Rektorys Karel. Přehled užité matematiky. 7. vyd. - Praha : Prometheus 000. ISBN [] Adams R. A. Single Variable Calculus (paperback). (Edition: ) Pearson Education Canada 00. ISBN: Reewer: prof. Ing. Jiří ůma CSc. VŠB - echnical University of Ostrava

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