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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

MultiǦstep Prediction of Pathological Tremor With Adaptive Neuro Fuzzy Inference System (ANFIS)

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Sivanagaraja Tatinati and Kalyana C. Veluvolu School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea

AbstractǦ In this paper, to overcome the inevitable phase delay due to preǦfiltering techniques and electromechanical delay in the procedure of pathological tremor compensation, a machine learning technique based on fuzzy logic and neural networks (adaptive neuro fuzzy inference system (ANFIS)) is employed. Experimental evaluation on tremor data is performed with data acquired from eight patients. Results show that ANFIS provides good performance in comparison with existing methods.

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I. Introduction

Pathological tremor is defined as an involuntary and approximately rhythmic oscillations of a body part [1]. Pathological is one of the serious disabling forms of tremors and often accompanies with aging. Although pathological tremor is not a lifeǦthreatening disorder, but it hampers the ability of individual to perform daily living activities like drinking water with a glass, unlock the door with a key etc. Further, the inability due to tremor leads to social embarrassment [1]. To compensate the functional disability (tremor) thereby to improve the quality of life, several pharmaceutical and surgical therapies are developed. The methods have their limitations and side effects. So, an effective treatment that can compensate tremor accurately yet to be developed.

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Over the last decade, considerable research has been focussed on developing active pathological tremor compensation techniques in realǦtime [3], [4], [6], [7]. One such approach is compensating pathological tremor by stimulating the relevant muscle groups with appropriate electric pulses thru functional electrical stimulator (FES) [3]. For example, consider hand supinationǦpronation muscle group, if tremor is due to the supination muscle group necessary electric pulses will be provided to pronation muscle group to counteract the involuntary movement, and viceversa. To compensate the tremor, a wearable device named as WOTAS (wearable orthosis for tremor assessment and suppression) was developed by using a feedback controlled FES. The efficacy of the wearable devices depends on the accurate and zeroǦphase delay filtering of tremulous motion from the whole motion. In the tremor compensation procedure with FES, a delay of approximately 80 ms was identified. The major source for the delay is electromechanical delay (EMD), defined as the delay between the onset of activation of muscle and the detection of movement [2]. As a result of several studies, the EMD was identified in the range of 50Ǧ60 ms [2]. The other source for delay is the preǦfiltering stage which is used to separate the voluntary motion and tremulous motion from the whole sensed motion [7]. On a whole, a latency of 80 ms was inevitable in the procedure and this delay adversely affects the tremor compensation accuracy. To overcome the phase delay limitation, multiǦstep prediction with harmonic method and Autoregressive method was proposed in [4]. The developed methods yield accurate prediction with periodic signals. Owing to the quasiǦperiodic nature of the tremor signals, prediction accuracy with the above methods is rather limited. To further enhance the prediction capabilities, in this work, we employed a machine learning technique, adaptive neuro fuzzy inference system (ANFIS). ANFIS has been recently popular as an effective method for time series prediction, function estimation, system identification and classification problems.

NCMMI '14

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

The goall of ANFIS is to find a mod del or mapping that will c correctly asso ociate the inp puts (initial vvalues) with the targeet (predicted values). Thee fuzzy infereence system (FIS) is a kn nowledge representation w where each fuzzy ru ule describes a local behaaviour of the system. Th he network sstructure thaat implemen nts FIS and employs hybridÇŚlearn ning rules to train is callled ANFIS. T The paper is organized ass follows: In section II, tremor d data collection n, delay in th he procedure and the metthod employeed are discusssed. In the laater section results ob btained with ANFIS are presented. Thee last section n presents thee conclusionss.

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II. Method ds

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In this ssection, we first discusss about the collection of pathological tremor daata. Later th he detailed descriptiion regarding g the delay asssociated in t tremor compeensation proccedure and aa brief descrip ption about ANFIS arre presented.

Fig. 1: Subject #1, A Action tremor (a) Parkin nsons patientt tremor wh hile executing g voluntary alternating Zoomed portiion of the wh hole motion ( (0 â&#x20AC;&#x201C; 5 s); (c) pronation and supinaation movemeents of the leeft hand; (b) Z he whole mottion FFT of th

NCMMI '14

ISBN : 978-81-925233-4-8

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

A. Patho ological Tre emor data

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Tremor is acquired w with a portaable Motus ssystem comp prises a gyrosscope placed d on the patiient’s wrist without o objecting freee flow of any y motion [8]. A All the subjects sign the i nformed con nsent forms approved by the Ethiics Committee of local hospital. Th he sampling rate employed is 100 deg/sec. Thee collected patholog gical tremor d data set conttains posturaal tremor, paarkinsonian tremor t and eessential trem mor. In this work forr analysis, fou ur subjects’ aaction tremor (tremulouss motion colllected while performing aa voluntary motion) and four sub bjects posturaal tremor are employed. F For an illustraation, action tremor colleccted from a parkinso onian patient while volun ntarily perform ming pronatiion and supiination moveement with leeft hand as shown in n Fig. 1. FFT o of the signal i s also shown in Fig. 1(c).

B B. Latency i in Tremor C Compensattion Proced dure With F FES

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The phasse delay in weearable devicces with FES i is due to the two sources: preǦfiltering stage and EM MD. In preǦ filtering stage, to fiilter the treemulous mottion from w whole sensed d motion, a Butterworth h lowǦpass filter/Buttterworth bandǦpass filterr was employyed. From the phase response, a averaage delay of 3 300 ms was identified d for fifth ord der Butterwo orth lowǦpasss filter, wheree as with fifth h order butteerworth band dǦpass filter with pass band 2Ǧ20 Hz a delay o of 20ms was i identified [7]]. In the pressence of eitheer delays the e prediction accuracyy was adverseely effected. E EMD, in geneeral a delay off 80§20 ms exxists between n peak musclle force and the appaarent peak in n EMG [2]. In n [2], a delay y of 40Ǧ60 mss was identifi fied between surface EMG G and force acquired d form the flexxion and exteension musclle group in fo orearm.

Fig. 2: D Delay in trem mor suppressio on proceduree with FES

NCMMI '14

ISBN : 978-81-925233-4-8

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

Thus a ttotal delay o of 60Ǧ80 ms is inevitablee in the proccedure, as sh hown in Fig.. 2. With thee sampling frequenccy, 100Hz, forr a signal with h frequency r ranges from 6Ǧ12 Hz, the delay of 6 to o 8 samples corresponds to aboutt 90± phasee difference. With this phase differrence tremorr suppressio on ability is drastically decreased. To overcom me this delayy, multiǦstep prediction baased on ANFIIS is developeed in this pap per.

C. Adapttive Neruo F Fuzzy Inferrence Syste em (ANFIS)

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A neuroǦǦFuzzy system m is inspired from the sup pervisory hum man brain’s f feed forward activities theerefore this intelligen nt hybrid systtem enjoys both the metrics of neural system and f fuzzy logic ap pproaches.

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A brief description of ANFIS iss provided b below. For detailed desscription of the algorith hm and its implemeentation referr to [5].

Fig g. 3: Sugeno ttype fuzzy mo odel illustratiion and ANFIIS architecturre [5]

In generaal, ANFIS stru ucture follow ws the takagiǦǦsugeno fuzzyy sets with seet of rules (co ombination off numerical and lingu uistic variablles) and mulltiǦlayers (5 layers), l as sh hown in Fig. 3. In this co onnected stru ucture, the input nodes represen nts the trainin ng values and d output nodees representss predicted vaalues, and in the hidden layers no odes function ning as memb bership functtions (MF’s) and rules. Alll nodes in different layerrs of ANFIS are eitheer fixed or ad daptive, as sh hown in Fig. 3 [5]. Differeent layers and their correesponding no odes can be described d as below: x

Layer 1: All n L nodes in this l layer are adaaptive. Furtheer, parameterrs in this layeer are named as premise p parameters.

NCMMI '14

ISBN : 978-81-925233-4-8

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

x x x

Layer 2: All n L nodes in this layer are labeelled as ¦ and d are fixed. Th he output obttained from t this layer is t the product o of all the inpu uts. Further, the firing streength of each h rule is repreesented by eaach node in t this layer. L Layer 3: Like layer 2, each h node in thiis layer is also o fixed and la abelled as N.. In this layerr, I th node c computes the e ratio of firiing strengthss of ith rules. Thereby, thee outputs of this layer aree named as n normalized fi firing strength hs. L Layer 4: In th his layer, all nodes are ad daptive. The p parameters c corresponds t to all nodes i in the layer a are referred a as consequen nt parameterss. L Layer 5: This s layer contaiins only one node and thaat node is alsso fixed and labelled as P P. The node c computes the e overall outp put of the sysstem by summ mations of alll inputs to the node.

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The paraameters of AN NFIS are opttimized with combination n of gradientt descent and d least squares method. LeastǦsqu uares method d is employeed to update the parametters iterativelly in the forw ward pass un ntil layer 4. The grad dient descen nt method iss to update the premise parameters with the ob btained erro or signal in backward d pass. The e error measuree to train the aboveǦmentiioned ANFIS S is E = Pn k=11(fk¡ ^ fk)2 wh here fk and ^ fk are the k th dessired and esttimated outp put, respectivvely, and n is the total n number of paairs (inputs outputs) of data in th he training seet.Due to thee implication of two updaate algorithm ms, the converrgence of is faster an nd reduce thee search spacce dimensions due to back k propagation algorithm. The overall output can be expresssed as a lineear combinatiion of the con nsequent parrameters, as s shown in Fig. 3.

D. Multistep p prediction n of patholo ogical trem mor with AN NFIS

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The proccedure emplo oyed to perforrm multiǦstep p prediction w with ANFIS iis shown in F Fig. 4. To perfform multiǦ step pred diction, a two oǦdimensional matrix of dimensions 11000£5 (each h row being rreferred to ass an epoch) was consstructed as sh hown in (1). D Data points i n each row w were chosen t to be six step ps apart and e each epoch containeed one data po oints:

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ANFIS w was trained w with the first 5 500 epochs. W With the traiining epochss, weights of n neural netwo ork and the membersship function ns will be op ptimized for accurate preediction (witth cost functtion as minim mum mean square error). The op ptimized parrameters obtaained with th he training eepochs are em mployed for the testing o perform thee multiǦstep prediction. epochs to

Fig. 4: Multtistep predicttion of pathological tremo or with ANFIS S

NCMMI '14

ISBN : 978-81-925233-4-8

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

III. Resultts In this section, we analyze thee multiǦstep ahead trem mor predictio on performaance with A ANFIS thru simulatio ons. To quanttify the perfo ormance, we e employ %Acccuracy, defineed as:

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m is the nu umber of sam mples, sk is thee input signaal at instant

MS(s) = where RM k and e iss the predictiion error.

A. Perforrmance An nalysis

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Fiig. 5: Multtistep predicction with A ANFIS (a) Wh hole motion ( (Subject #1, acction trem mor); (b) Tremulous m motion obtaained after bandpass filltering ; (c) Predicttion error ob btained due t to 80 ms delaay together w with multisttep predicttion error ob btained with ANFIS

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Th he proceduree employed to perform m multiǦstep preediction with h ANFIS is sh hown in Fig. 4. First 500 samples of th he collected d tremor data was prrovided for o offline trainin ng of ANFIS to o obtain the optimized w weights for neeural netwo ork and m membership fu unctions. Forr multiǦstep prediction, th he whole motion sen nsed with gyyroscope is provided as an n input to a fiffth order Butterworth bandpass fillter with pass band 2Ǧ20 Hz to filter th he tremulous motion from m the whole seensed motio on. Further, multiǦstep prrediction for 6 samples (6 60 ms) and 8 samples (80 0ms) is perfo ormed with LS SǦSVM to overcome the delay asssociated wiith bandpasss filtering an nd EMD. T To validate multǦistep prrediction w with ANFIS S, filtered tremulou us motion forrm a zeroǦph hase bandpasss filter with s same specifications is emp ployed, as shown in Fig. 4.

f 80 ms and the predictio on error obttained after For illustration, the prediction errror due to tthe delay of n for the sam me delay for ssubject #1 aree shown in F Fig. 5. In Fig g. 5 (a), the performiing multiǦsteep prediction whole m motion is show wn, where as in Fig. 5 (b), filtered trem mulous motion n obtained affter bandpasss filtering is shown. I n Fig. 5(c), prediction errror obtained d due to 80 mss is shown tog gether with t the multistep p prediction

NCMMI '14

ISBN : 978-81-925233-4-8

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

error obtained with ANFIS for comparison. The prediction accuracy obtained for subject #1 is 49:21 § 1:21%. The average prediction accuracy obtained over all subjects and trials for 60 ms prediction length is 50 § 2% and 80 ms prediction length is 45:25 § 5%.

IV. Conclusions

References

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R. J. Elbe and W. C. Koller, Tremor, U. MD, Ed. John Hopkins University Press: Baltimore, MD, USA, 1985. P. R. Cavanagh, and P. V. Komi, “Electromechanical delay in human skeletal muscle under concentric and eccentric contractions,” Eur. J. Appl. Physiol., vol. 42, pp. 159–163, 1979. F. Widjaja, C. Y. Shee, W. L. Au, P. Poignet, and W. T. Ang, “ Using electromechanical delay for realǦtime antiǦphase tremor attenuation system using functional electrical stimulation,” Proc. Int. Conf. IEEE on Robo. Auto., Shanghai, 2011. A. P. L. Bo, P. Poignet, F. Widjaja, and W. T. Ang, “Online pathological tremor characterization using extended kalman filtering,” in Proc. 30th Annu. Int. Conf. IEEE Eng. in Med. Bio. Soc., Vancouver, Canada, 2008, pp. 1753–1756. K. Manish, H. Nystrom, L. R. Aarup, T. J. Nottrup, and D. R. Olsen, “Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS),” in Phy. in Med. Bio., vol. 50, pp. 4721Ǧ 4728, 2005. S. Tatinati, K. C. Veluvolu, S. M. Hong, W. T. Latt, and W. T. Ang, “Physiological tremor estimation with autoregressive (AR) model and Kalman filter for robotic applications,” in IEEE Sensors J., vol. 13, pp. 4977Ǧ4985, 2013. K. C. Veluvolu, S. Tatinati, S. M. Hong and W. T. Ang, ” Multistep prediction of physiological tremor for surgical robotic applications,” in IEEE Tran. on Biomed. Eng., vol.60, pp. 3074Ǧ3082 ,2013. G. N. Reeke, Modeling in the Neurosciences, www.MotusBioengineering.com.

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In this paper, ANFIS is employed for multiǦstep prediction of pathological tremor to overcome the phased delay and to improve the performance. The performance of ANFIS was experimentally assessed with the tremor data collected from eight patients. An average prediciton accuracy of 50§2% is obtained with the multiǦstep prediction with ANFIS for 60 ms ahead prediction and an average accuracy of 45:25 § 5% is obtained for prediction length 80 ms.

NCMMI '14

ISBN : 978-81-925233-4-8