e c o l o g i c a l m o d e l l i n g 1 9 5 ( 2 0 0 6 ) 61–71

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Implementation of wavelets and artificial neural networksto detection of toxic response behavior of chironomids(Chironomidae: Diptera) for water quality monitoring

Cheol-Ki Kima, Inn-Sil Kwakb, Eui-Young Chac, Tae-Soo Chond,∗

a School of Computer Engineering, Miryang National University, Miryang 627-702, Republic of Koreab Bioresources Utilization Program, Yosu National University, Yosu 550-749, Republic of Koreac Division of Computer Engineering, Pusan National University, Busan (Pusan) 609-735, Republic of Koread Division of Biological Sciences, Pusan National University, Busan (Pusan) 609-735, Republic of Korea

a r t i c l e i n f o a b s t r a c t

Article history:

Published on line 7 February 2006

Keywords:

Behavioral monitoring

Wavelet

Artificial neural network

Chironomid larvae

Carbofuran

Movement behavior of Chironomus samoensis larvae was observed in response to the treat-

ments of carbofuran, an anticholinesterase insecticide, at a low concentration (0.1 mg/l) in

semi-natural conditions. Two typical movement patterns were selected before and after the

treatments, and the variables characterizing movement tracks in two dimensions were ana-

lyzed by discrete wavelet transform (DWT) with Daubechie’s 4 functions. The variables were

selected based on the feature coefficients of DWT and were subsequently used as input for

training with the multi-layer perceptron network. The trained network efficiently detected

changes in movement patterns before and after the treatments. We demonstrated that the

combined use of the wavelets and artificial neural networks would be a useful tool for auto-

matic behavioral monitoring for water quality assessment.

© 2005 Elsevier B.V. All rights reserved.

1. Introduction

Recently, movement behavior of animals has been the focusof bio-monitoring with regard to risk assessment in eco-toxicology. Response behavior has been reported to indicatethe presence of various chemical pollutants in environment(e.g, Lemly and Smith, 1986). Dutta et al. (1992) suggestedthat a behavioral bioassay would be more sensitive than othertypes of testing. In recent years, research on low concentrationeffects of toxic chemicals on behavior has been rapidly accu-mulating in various taxa, including crustaceans (Abgrall et al.,2000; Roast et al., 2000), snails (Ibrahim et al., 1992), fish (Grayet al., 1999) and insects (Chon et al., 1998a,b). However, mostresearch has been based on observation of single or combi-nations of single behaviors with qualitative descriptions. Notmany accounts of research have been conducted on computa-

∗ Corresponding author. Tel.: +82 51 510 2261; fax: +82 51 512 2262.E-mail address: tschon@pusan.ac.kr (T.-S. Chon).

tional analysis of behavioral changes in spatial and temporaldomain.

Regarding mathematical analysis of the movement tracks,correlation function was used for modeling circling paths ofgametes or meander searches by isopods (Alt, 1990) and forrevealing directional autocorrelation in paths of carabid bee-tles (Scharstein, 1990). Additionally, measurement analysesand determination of non-linear dimension were investigated.Tourtellot et al. (1991) analyzed the move length and turn def-inition in orientation of cockroach. Johnson et al. (1992) andWeins et al. (1993, 1995) attempted to quantify insect move-ments and suggested that fractal dimensions of pathwaysmay provide insights not available through absolute mea-sures of pathway configurations. Recently, experiments onrats’ movement have been conducted on dynamic perspec-tives (Tchernichovski et al., 1998a,b), and statistical discrim-

0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2005.11.010

62 e c o l o g i c a l m o d e l l i n g 1 9 5 ( 2 0 0 6 ) 61–71

ination of motion (Drai et al., 2000) has been subsequentlyrevealed in exploration behavior.

These mathematical analyses effectively extract infor-mation out of the movement data. However, only a limitednumber of variables were used, for example, correlationcoefficients (Alt, 1990; Scharstein, 1989) or fractal dimensions(Wiens et al., 1993, 1995). In these cases, information conveyedby the mathematical variables was highly compressed. In realsituations, however, various local changes in the movementtracks’ shape may also be critical in determining behavioralstates of animals. The compressed information in variables isusually not sufficient to characterize the overall and detailedmovements at the same time. In this study, we utilized thefeasibility of wavelets and artificial neural networks (ANNs)as an adaptive filter to efficiently extract local informationin the movement tracks without compressing greatly themovement data. Wavelets are effective in extracting localinformation in time development and are especially usefulfor characterizing or identifying changes in shapes of thecurves (Daubechie, 1990; Rioul and Vetterli, 1991). Addition-ally, ANNs have been demonstrated in patterning complexrelationships between input and output data, and havebeen utilized in revealing various environment–organismrelationships in ecological sciences (see Lek and Guegan,2000). We initially utilized wavelet’s feasibility in filter-ing local information residing in the movement tracksof chironomid larvae. Subsequently, the coefficients from

demonstrated with the trained network before and after thetreatments.

2. Materials and methods

2.1. Movement data

The specimens of Chironomus samoensis were collected from anurbanized stream in the Suyong River, Busan (Pusan), Korea.The stock populations of the larvae were maintained in aplastic container under the light regime of L10:D14 at tem-perature of 18 ± 2 ◦C. Before observation, tap water in the testaquarium was sufficiently dechlorinated by adding Na2S2O3

(0.3 mg/10 l) as well as by bubbling air under sunlight for 2 or3 days (Yamanoto, 1967). Fourth instar larvae of C. samoensiswere placed individually in a polyethylene observation cage(volume of water; 6 cm × 7 cm × 2.5 cm), and were observedfrom the top view at 0.25 s intervals using a CCTV cam-era (Kukjae Electronics Co., Ltd.; IVC-841®). The specimenswere acclimated approximately for 1 day before the test, andwere observed for 3–4 days for the treatments. The chem-ical was treated in the middle of the observation period.Carbofuran, an organophosphate known as highly toxic toaquatic invertebrates (Barabas, 1998; Gupta, 1994; Smith,1993), was applied directly into the cage at concentration of0.1 mg/l.

wavelet analysis were used as input data for training withANNs. Automatic detection of the movement patterns was

Fig. 1 – Movement tracks of Chironomus samoensis. Pattern SG: shpattern BS: short time (c) and long time (d) segments.

The analog data captured by the camera were digitizedby using a video overlay board (Dooin Electronics Co., Ltd.;

ort time (30 s) (a) and long time (25 min) (b) segments, and

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OSCARIII®) and were sent to the image recognition systemto locate the target organism in spatial and time domain.The center of moving image was detected, and its two-dimensional location was automatically recorded in digitaldata in a sequence. The software for detecting movementof the test specimens was developed in Neural Networksand Real-World Applications Laboratory, Division of ComputerEngineering, Pusan National University.

In total, 12 specimens were observed. After checking varia-tions in movement behavior and noises in the recorded data,we selected nine specimens showing stable activity patternsbased on strength of swimming activity and duration of move-ment. Among nine specimens, we randomly chose four spec-imens and used their data for training.

Fig. 1a shows a typical movement sequence in 30 s selectedto represent the normal state. The fish fully stretched theirbody to advance forward in a longer range. We called suchmovement as “stretching-and-gliding (pattern SG)”. Pattern SGrepresented continuous movement in the swimming phaseof chironomid larvae. In contrast to pattern SG, Fig. 1c showsthe movement pattern that was typically observed from thespecimens in stress. The tested specimens repeatedly bentand stretched their bodies in a limited range in the observa-tion aquarium. We called such movement as “bending-and-stretching (pattern BS)”. Patterns SG and BS were representedmore characteristically in a long time duration. Fig. 1b showsan example of the long time segment in 25 min, where thetStdpv

tsmmivwTat

wiaAmtf(

2

WF(v

Fig. 2 – Flow-chart for using the wavelet transform andANNs for selecting and training the variables of themovement tracks of Chironomus samoensis.

types of basic functions: good time detection and low fre-quency resolution for the data of high frequencies, and lowtime detection and high frequency for the data of low fre-quencies. Consequently, the wavelet transformation retainslocation information. More particularly, the discrete wavelettransform (DWT) has been recently used as a computationaltechnique for extracting information about non-stationarysignals (Daubechie, 1990).

By controlling scaling and shifting, the signal can be exam-ined at different resolutions, an ability called multi-resolution(Mallat, 1989). DWT is defined by the following equation:

W(j, k) =∑

j

∑

k

x(k)2−j/2� (2−jn − k) (1)

where � (t) is a time function called the mother wavelet withfinite energy and fast decay, j a scaling factor, k a shifting fac-tor, x(k) the original signal, n a non-dimensional variable andW(j, k) represents a transformed signal by DWT. DWT has beenutilized in various disciplines, including image processing,geophysics and medicine, in the fields where local tempo-ral information plays an important role in covering time axis(Daubechie, 1990). One of the first applications in ecology canbe found in Gao and Li (1993) in analyzing coherent structureexisted between atmosphere and forest, but implementation

rack consisted mostly of the short time segments of patternG. The movement tracks moved around in a wider range inhe observation aquarium. Fig. 1d shows a long time segmentominantly consisting of pattern BS. The movement tracksroduced strongly curved contours in small scale in the obser-ation aquarium.

According to visual judgment by experienced observers,he movement tracks (30 s) showing patterns SG and BS wereelected from the data for four specimens, and ten 30 s seg-ents were randomly chosen from the data pool of theovement tracks for each pattern. The variables character-

zing the movement tracks for the 10 segments were pro-ided to wavelet analyses (Fig. 2). The obtained coefficients ofavelet analyses in average were used for selecting variables.he coefficients of wavelet analyses for the selected vari-bles were subsequently provided to ANNs as input data forraining.

After training, the data sets for the rest five specimensere used for validation. In this case, the segmented data

n 30 s were provided in a sequence to the trained network,nd the pattern was detected in every 30 s correspondingly.dditionally, we accumulated the count of detection of theovement patterns in 30 s in the period of every 25 min. Detec-

ion rates (percents) in 30 min were subsequently checkedor each movement pattern in the long period of observation3–4 days).

.2. Wavelet transform

avelet analysis is an improved version of the “short timeourier transform” to discover temporal characteristics in dataDaubechie, 1990; Rioul and Vetterli, 1991). The wavelets pro-ide selective resolutions in time, corresponding to different

64 e c o l o g i c a l m o d e l l i n g 1 9 5 ( 2 0 0 6 ) 61–71

of wavelets to movement behavior of animals has not beenreported so far to the authors’ knowledge.

In this study, DWT was utilized to select variables for featur-ing behavioral data. Daubechie’s 4 was used as the base func-tion. As the candidate variables from the movement trackswere given to DWT, the amplitudes corresponding to the basefunction was calculated for the total term and detail coef-ficients in different levels. The amplitudes coefficients wereused for selecting variables (Fig. 2).

The variables characterizing the movement tracks wereused as input data for wavelet analyses. Based on experienceof behaviors of the test specimens and the variables suggestedby other research on analyzing movement tracks (e.g., Schal etal., 1983; Collins et al., 1994), we initially chose 19 candidate

variables, which will be explained later. Data sets for each vari-able were decomposed up to four levels in scaling (A3, D1,D2 and D3) through DWT (Fig. 3). While A3 represents lowfrequency, D1–D3 express detail level in high frequency. Thenumbers followed by D represents sharpness in frequency. Asthe number was decreased, frequency would be correspond-ingly increased; if the number is too low it would becomenoise. With preliminary experiments we chose D2 level forexpressing characters of the movement tracks in this study.The variables showing higher amplitude terms in D2 wereselected for characterizing the movement tracks. With prelim-inary tests, we chose five variables for optimally characterizingthe movement tracks and used them for training with ANNs(Fig. 2).

Fig. 3 – Amplitudes of the total and detail coefficients in differencharacterizing the movement tracks of Chironomus samoensis. Me

t levels of DWT (Daubechie’s 4) applied to the variablesander (a), angular acceleration (b) and locomotory rate (c).

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Fig. 4 – Amplitudes of detailed coefficients for differentvariables analyzed by the three computational methods byDWT (a), DFT (b) and PCA (c). (A–D, number of movement infour directions (up, down, right, left); E, speed; F,acceleration; G–I, angular speed in three directions(turn-right, turn-left, turn-backward); J, angularacceleration; K, location on x-coordinate in average; L,location on y-coordinate in average; M, stop duration; N,stop number; O, maximum distance; P, locomotory rate; Q,number of backward movement; R, meander and S,maximum distance on y-coordinate).

2.3. Training and validation procedure

The feature coefficients of DWT were obtained from the vari-ables of the movement tracks in 30 s segment. Five variableswere selected based on amplitudes of feature coefficients at D2level from wavelet analysis and were subsequently provided to

an ANN, the multi-layer perceptron with the back-propagationalgorithm (five input nodes) (Fig. 2). The binary informationeither for pattern SG or pattern BS was used for matchingoutput (i.e., pattern SG = 0 and pattern BS = 1). The value of acti-vation function coefficients used in this study was 1.0, whilethe learning coefficient was set to 0.01. The level of error toler-ance was 1.0, while the threshold for determining the binarylevel for the activation function was 0.5. Network pruning wasnot required during the training processes in this study. Detailsof the multi-layer perceptron and the back-propagation algo-rithm could be found in Rumelhart et al. (1986), Lippmann(1987), Zurada (1992) and Haykin (1994).

After training with the variables with the multi-layer per-ceptron, new validation data sets were prepared in 30 s win-dows in a sequence during the whole observation period. Thepattern was detected in each window by the trained network.The data for each specimen were initially provided to DWTto produce feature coefficients of the variables at D2 level foreach 30 s segment in a sequence. The feature coefficients werethen given as input to the trained ANN for validation.

In addition to pattern detection in each short time periodof 30 s, we also collectively obtained detection rates (percents)in every 25 min for the purpose of validation and for issu-ing alarm signals in field conditions. A longer time durationranging 1–2 h may be more suitable in decision making formanagers of aquatic ecosystems. For this purpose, once themovement pattern was decided in every 30 s, we reevaluated

behavioral states in 25 min duration by summing the shorttime detections for different movement patterns. In each longtime segment of 25 min, 15 s overlap was allowed in this study.Consequently, the last frame started at 24 min (1475 s) and cov-ered an extra 15 s to end at 1515 s. Detection rates in 100 minsegments were observed in a sequence during the observationperiod.

2.4. Evaluation with other computational methods

In order to evaluate feasibility in data selection by waveletanalysis, we used additional methods for information extrac-tion. Discrete Fourier transform (DFT; Sophocles, 1996) wasapplied to the variables, and the variables showing the high-est values in the coefficients of the Fourier transform wereselected for characterizing the movement tracks. Similarly, thevariables were analyzed with principle component analysis(PCA; Tim, 2000). The variables showing the highest eigenval-ues for the first principal component were selected for evalu-ation. For each test of DFT and PCA, five variables were chosenfor training with ANNs.

3. Results

3.1. Variable selection

As stated above, the five variables showing higher amplitudeterms in D2 were selected for characterizing the movementtracks among the 19 candidate variables. Fig. 3 shows ampli-tudes of coefficients in average of DWT on some selected vari-ables: meander (total abstract angle changes divided by thetotal path length; rad/mm), angular acceleration (average of

66 e c o l o g i c a l m o d e l l i n g 1 9 5 ( 2 0 0 6 ) 61–71

Fig. 5 – Examples of the detected movement tracks (30 s) of Chironomus samoensis. Pattern SG (a–c) and pattern BS (d–f).

velocity differences in magnitude term during the observationtime; rad/s2) and locomotory rate (the path length divided bythe cumulated duration of movement excluding the stop dura-tion; mm/s) of an example of pattern BS. The left-hand sidefigures show values in the total amplitude for 10 segments ofthe movement tracks in 120 s duration in a sequence, while theright-hand side figures present levels of approximation anddifferent details in DWT. Meander (Fig. 3a) and angular accel-eration (Fig. 3b) showed high levels in the total amplitudesranging 0.7–1.0, while the total amplitude of locomotory rate(Fig. 3c) was low around 0.2. Consequently, angular acceler-ation and meander, which showed higher amplitude values,were selected as the variables for characterizing the move-ment tracks.

Similarly, the amplitudes were measured with the addi-tional 16 variables such as stop duration (the total durationin which the specimen moved a distance less than 0.1 cm pereach sampling time (0.25 s); s), maximum distance (maximumdifference in locations during the observation time; min), thenumber of backward movements, speed (mm/s), acceleration(mm/s2), angular speed (turn-right, turn-left, turn-backward;rad), location on x-coordinate in average (mm), location ony-coordinate in average (mm), number of stops in 30 s seg-ment, maximum distance on y-coordinate (mm) and numberof movement in four directions (left, right, up and down; num-ber). Fig. 4a shows the total amplitudes for different variablesin average with DWT. For training with ANNs, we selected thevariables with the amplitudes higher than 0.65. In addition

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Fig. 6 – Detected segments (bold line) of the movementpatterns in the long time duration (25 min). Pattern SG (a)and pattern BS (b).

to meander and angular acceleration, three more variables,stop duration, maximum distance and the number of back-ward movements were chosen for training with ANNs.

3.2. Detection of movement patterns

Fig. 5 shows examples of the movement tracks in 30 s detectedby the trained multi-layer perceptron. The detected patternsappear to be in accordance with the shape of template pat-terns: the movement tracks shown in Fig. 5a–c were recog-nized as pattern SG (see Fig. 1a), while the tracks presentedin Fig. 5d–f were identified as pattern BS (see Fig. 1c). Detec-

tion of the patterns SG and BS by the trained network wasalso presented in a longer time segment (25 min) as shown inFig. 6a and b, respectively. The detected segments of move-ment in dark color were, again, similar to pattern SG (Fig. 1a)for Fig. 6a, and to pattern BS (Fig. 1c) for Fig. 6b.

The wavelet–ANN system was capable of detecting pat-terns in the short time segments given in a sequence. Fig. 7shows an example of signaling pattern SG or pattern BS incontinuous data of 30 s window by the trained network. Thedecrease in pattern SG (symbol: ×) and the increase in pat-tern BS (symbol: �) was observable after the treatments. Thechanges in angle acceleration were also shown in Fig. 7 asan example variable. The curve patterns of angle accelerationwere changed before and after the treatments.

Expression of accuracy in validation was difficult in thisstudy, regarding that voluminous data were produced as newinput data for validation (e.g., data segments in 30 s contin-uously recorded for each specimen for the whole observa-tion period). We checked validation of the results from thetrained network by having the network continuously detectthe movement patterns in 30 s windows given in a sequencefor a long period, and compared overall changes in detec-tion rates of the movement patterns before and after thetreatments. Changes in detection of the movement patternswere clearly revealed in the long time period. Fig. 8a showschanges in detection rates (percents) in windows of 1515 s in asequence.

Fig. 7 – Continuous detection of the movement patterns in 30 s safter the treatments of carbofuran.

After the treatments, detection rates of pattern BS weredistinctively increased while detection rates of pattern SGwere correspondingly decreased. Table 1 shows changes infrequency of detection rates of the movement patterns beforeand after the treatments.

In this case, the number of detection of patterns SG and BSin 30 s segments was cumulatively expressed in percents ineach period of 1515 s. Detection rates of pattern SG were signif-icantly higher before the treatments in the range of 67.2–74.5%while it decreased to 55.7–64.2% after the treatments. In con-trast, detection rates of pattern BS were low in 11.1–22.9%before the treatments while it increased to 21.2–32.3% afterthe treatments. Detection rates of patterns SG and BS werestatistically different before and after the treatments accord-ing to Student’s T-test (p < 0.0005).

3.3. Comparison with other methods

We evaluated performance of pattern detection by waveletanalyses with other computational methods of DFT and PCA.Fig. 4b and c shows normalized amplitudes in DFT and nor-malized eigenvalues for the first principal component in PCA,respectively. In DFT, the number of upward movement (A;

egments and changes in angular acceleration before and

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Fig. 8 – Changes in detection rates of the movementpatterns by ANNs based on different computationalmethods before and after the treatments in a long period.DWT (a), DFT (b) and PCA (c).

Fig. 4b) appeared to be dominant in characterizing the move-ment tracks compared with the other variables. In PCA, how-ever, many variables were relatively higher in normalizedvalues. As stated above, we selected the five variables show-ing the highest amplitudes for each method: up-direction,down-direction, right-direction, meander and maximum dis-tance on y-coordinate for DFT, and speed, acceleration, angu-lar speed (turn-right), angular acceleration and stop durationfor PCA.

Fig. 8b and c show changes in detection rates in 1515 s in asequence according to DFT and PCA, respectively. Based on thevariables selected by DFT (Fig. 8b), changes in detection rateswere not as distinctive as shown in the case of wavelet analy-sis (Fig. 8a). Pattern BS was initially very low and was increasedto some higher levels after the treatments. For the case of pat-tern SG, however, detection rates remained at extremely lowlevels during the whole observation period. Consequently, thechanges in detection rates were not detected as clearly as thecase with DWT before and after the treatments. For the caseof PCA (Fig. 8c), changes in detection rates were not observedfor both patterns SG and BS before and after the treatments.Detection rates appeared to be continuously high for patternBS with some fluctuation, while detection rates of patternSG remained consistently low during the whole observationperiod.

In both cases for DFT and PCA, detection was not clearlydemonstrated in two patterns. Pattern BS was relatively morerecognized. Based on these results, the movement patternsappeared to be more efficiently detected by the variablesselected with DWT.

4. Discussion

This study demonstrated that the combinational use ofwavelets and ANNs was effective in selection of variables char-

acterizing the movement tracks and detection of behavioralpatterns of specimens in response to toxic substances. A diffi-culty in behavioral monitoring usually lies in handling a largeamount of data. In this study, movement behavior of eachspecimen was observed in every 0.25 s continuously for 3–4days. Consequently, the observation system produced volumi-nous amount of data. It was demonstrated in this study thatthe proposed model efficiently detected the movement pat-terns in the continuous data given in a sequence (Figs. 6–8). Asshown in Table 1, detection rates of the movement patternswere statistically different before and after the treatments.The variables of the movement tracks were also comparedbetween patterns SG and BS (Table 2). All the variables werestatistically different (p < 0.0005).

Additionally, the use of wavelet–ANN system would beeffective in continuous pattern recognition of the short timemovement tracks given in a sequence (Fig. 7). This short-termpattern detection suggests that the developed method couldbe an alternative tool for water quality assessment on the real-time basis.

Overall, the combined model of wavelets and ANNsobjectively detected the different movement patterns, andconfirmed selection of the two patterns SG and BS byhuman observers in preliminary experiments (Tables 1 and 2;Figs. 7 and 8). However, judgment by humans may not bealways guaranteed. Due to variation in observation skill,fatigue and other disturbances to the observers, consistencycould not be attained if detection of the movement pat-tern is required on a large-scale data. If judgment of detec-tion has to be continuously made in a short time (in thiscase, 30 s) sequence for a long period (see Figs. 7 and 8), itwould be almost impossible for monitoring by humans. Inthis regard, the computational methods could be a useful

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Table 1 – Changes in detection rates (%) of different movement patterns of chironomid larvae before and after thetreatments with carbofuran (Student’s T-test, n1 = n2 = 10)

Aa Ba Ca Da Ea

SGb BSb SG BS SG BS SG BS SG BS

Before treatmentMean 74.50 10.59 70.71 11.09 67.74 22.58 68.71 17.91 67.24 22.96S.D. 8.08 6.67 10.63 9.25 7.57 6.80 9.78 7.37 7.35 6.5

After treatmentMean 55.67 31.01 64.20 22.20 61.47 32.32 60.74 21.17 59.57 29.57S.D. 5.52 8.34 12.60 13.40 8.02 7.53 15.14 8.53 15.4 9.38

T 104.64 108.02 27.68 47.84 32.54 52.73 32.72 16.76 32.96 33.99Probability (p) <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005

a Specimens.b Patterns.

machine-intelligent tool for objective and automatic detec-tion of the movement patterns of animals for water qualitymonitoring (e.g., on-line water quality assessment).

The movement tracks in 30 s were generally suitable forexpressing the local movement patterns of chironomid larvaein this study. In reality, however, variations were high in themovement tracks observed from individual specimens with orwithout the chemical treatments. In previous study, types oflocomotion of chironomid larvae have been described such asswimming (“figure-of-eight” shape), crawling and respiratoryundulation (Brackenbury, 2000). Under real conditions, how-ever, these locomotory modes were greatly modified due tonumerous internal and external noises. Consequently, it wasnot easy to accurately train the whole behavioral procedurelisted in the locomotory modes with the methods for pat-tern detection. Training was especially difficult if the “line”movements of chironomid larvae had to be patterned. Basedon this reasoning, we chose locomotory sequences that arestereotypic for expressing behavioral states and could be sim-ply analyzed with image processors and computation meth-ods for pattern recognition. With preliminary experiments, weselected the two most distinctively observed movement pat-terns that would represent clearly the normal and affectedstates of the specimens. For simplicity of modeling, we tracedthe “center point” of the moving line of the chironomid lar-vae in each 0.25 s sequence, rather than the whole line ofbody; the center point was easier for tracking with image pro-cto

vae (Brackenbury, 2000). In contrast to pattern SG, pattern BS(Fig. 1c) was not particularly matched to any sequences listedin the locomotory modes by Brackenbury (2000), which mainlydescribed normal states in movement behavior of chironomidlarvae.

An ANN has been used to detect response behaviors ofmedaka (Kwak et al., 2002). The multi-layer perceptron wasimplemented to learn the variables characterizing movementtracks in this case, and detection of movement patterns withnew data sets was possible with the trained network. In thisstudy, however, selection of the variables was pre-determinedmainly by experience of the observers; no quantitative evalua-tion was conducted for selecting the variables. Consequently,input variables were more dependent upon the observers’subjective judgment. Through wavelet analyses, however, thevariables’ contribution to characterization of the movementpatterns could be revealed through amplitudes of the coeffi-cients (in this case >0.65 in D2; Figs. 3 and 4). This demon-strated the efficient use of the wavelet transform for selectingappropriate variables.

The time detection by the wavelet transform, however, wasnot fully addressed in this study. For simplicity of training withthe multi-layer perceptron, a fixed time frame was used fortraining and validating the movement tracks. Within a win-dow of the pre-determined duration, the overall pattern of themovement tracks was tested in this study (e.g., Figs. 5 and 6).If a variable time frame is used in input data, and the model is

t pa

tern

essors, and the tracks’ shape was more contrasted betweenhe two patterns. Pattern SG (Fig. 1a) represented the statef continuous movement in swimming of chironomid lar-

Table 2 – Comparison of variables characterizing movemen

Parameters Pat

SG

Mean S.D.

No. of backward movement 59.1 19.09Stop duration (s) 85.3 11.12Meander (rad/s) 0.36 0.14Angular acceleration (rad/s2) 0.44 0.22Max. distance (mm) 9.69 8.94

designed to directly determine the changes in the curve pat-tern of variables in time domain, the wavelets could be usefulfor effectively pin-pointing the time for behavioral changes.

tterns of Chironomid larvae (Student’s T-test, n1 = n2 = 10)

s T Probability (p)

BS

Mean S.D.

161 35.41 130.942 <0.000519.9 17.36 116.264 <0.00050.68 0.13 5.787 <0.00051.35 0.32 11.701 <0.00056.70 2.23 8.486 <0.0005

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However, patterning the movement tracks in a variable timeframe is not a simple task. Further study is needed in this field,regarding development of algorithms for learning temporaldevelopment of movement patterns and requirement of thelong computation time with the time-variable input/outputdata.

In this study, both patterns SG and BS were observed beforeand after the treatments (Table 1; Figs. 7 and 8). This suggestedthat the movement patterns used in this study were not deter-ministically present either before or after the treatments. Theoccurrence of the movement patterns appears to be proba-bilistic depending upon the specimen’s internal and externalconditions. The stochastic occurrence of patterns needs to becomputationally elaborated in validation of the trained ANNsbefore and after the treatments. Additionally physiologicalcausality on producing this type of probabilistic occurrenceof the movement patterns should be further studied in thefuture.

5. Conclusions

In this paper, we elucidated feasibility of wavelet analysis inextracting local information of movement data. The use ofcoefficients in DWT was effective in variable selection regard-ing characterization of the movement patterns. Subsequently,the coefficients were efficiently trained with ANNs. We pro-

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Acknowledgment

This paper was supported by Korea Science and EngineeringFoundation (KOSEF; Project Number R01-2004-000-11036-0).

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