L. Atallah Department of Computing Imperial College London, United Kingdom +44 (0)

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1 An H Framework for Optmal ensor electon wth Applcatons to BN ensor love Desgn R.. Kng Department of omputng +44 (0) L. Atallah Department of omputng +44 (0) A. Darz Faculty of edcne +44 (0) Yang Department of omputng +44 (0) ABTRAT Laparoscopc surgcal tranng s a challengng task due to the complexty of nstrument control and demand on manual dexterty and hand-eye coordnaton. urrently tranng and assessng surgeons for ther laparoscopc sklls rely manly on subectve assessment. Ths paper presents a Body ensor Network (BN) sensor glove for laparoscopc gesture recognton and obectve assessment of surgcal sklls. An H framework s proposed for the selecton of sensors to maxmze the senstvty and specfcty of gesture recognton for a gven set of laparoscopc tasks. Wth the proposed framework the optmal locaton as well as the number of the sensors can be determned. The sensors used n ths study nclude accelerometers and fber optc bend sensors. Expermental data s collected by partcpants wearng the glove whle performng smple laparoscopc tasks. By usng the proposed H framework sensor correlaton and relevance to task recognton can be determned thus allowng a reducton n the number of sensors used. Results have shown that t s possble to establsh the ntrnsc correlaton of the sensors and determne whch sensors are most relevant to specfc gestures based on the proposed method. Keywords Body ensor Networks laparoscopy sensor selecton Hdden arkov odels.. INTRODUTION Over the last decade nmally Invasve urgery (I) has replaced many open procedures n surgery. I has a number of establshed benefts to the patent whch nclude reduced pan faster recovery tmes and less scarrng. For healthcare provders faster recovery allows patents to be dscharged shortly after surgery thus freeng up valuable bed space and nursng resources. One form of I s Laparoscopc surgery whch s prmarly performed n the abdomnal cavty. For ths procedure the abdomen s frst nflated and small ncsons are made through the Permsson to make dgtal or hard copes of all or part of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes bear ths notce and the full ctaton on the frst page. To copy otherwse or republsh to post on servers or to redstrbute to lsts requres pror specfc permsson and/or a fee. EmNets'07 June ork Ireland opyrght 2007 A IBN /07/06...{5}.00 abdomnal wall. An endoscopc camera and surgcal nstruments can then be nserted to perform the operaton wthn the abdomen. The operatng scene s typcally vewed from a 2D montor away from the patent. Despte the sgnfcant benefts of I the technque mposes many challenges on the operatng surgeon. For I a 3D envronment has to be navgated usng a 2D dsplay. Rotaton of the camera can make navgaton a challenge. Furthermore the fulcrum effect (when movng the hand one way the tool tp moves n the opposte drecton) s counter ntutve thus requrng extensve hand-eye coordnaton and manual dexterty. The poor ergonomcs of the tools can also lead to faster surgcal fatgue than that of open surgery []. These challenges have hghlghted the need of greater emphass on the tranng and montorng of surgcal sklls for laparoscopc surgery. Increased pressure from the publc and the medcal communty has called for the need for more obectve measures of surgcal competence and sklls [2]. urrent methods for measurng surgcal sklls and competency vary from usng metrcs gathered from vrtual realty smulators to usng sensors placed on I nstruments [3]. Another method s to gather data drectly from the surgeons' hands usng a sensor glove. any studes examnng hand gestures relatng to laparoscopc surgery have been conducted usng sensors ether on the tool or mounted on the surgeon s hands and body. Rosen et al [4] used arkov models to evaluate surgcal sklls based on tssue/tool nteracton and force/torque sgnatures measured by sensors mounted on the laparoscopc tool and vdeo. tudes have also used E (electromyography) to measure muscle actvaton durng laparoscopc tasks. Berguer et al [5] nvestgated muscle workload and physcal stress on the surgeon Quck et al [6] compared dfferent tools for ergonomc propertes whereas atern et al [7] examned muscle stran whle usng dfferent tools. everal sensors were used n these papers for the collecton of data relatng to the motons of the surgeon durng laparoscopc surgery however the postonng of sensors was not thoroughly nvestgated. Optmal sensor selecton can be appled to many applcatons nvolvng the placement of sensors. Lal et al [8] employ the use of support vector machnes (V) to select electroencephalogram (EE) channels to measurng bran actvty Worden et al use combnatoral optmzaton methods such as genetc algorthms (A) and smulated annealng to optmze sensors for fault detecton [9].

2 Based on the current advances of BN [0] ths paper nvestgates optmal sensor placements for the effectve desgn of wearable wreless sensor glove for the recognton of I gestures for surgcal sklls assessment. ensor selecton and feature dentfcaton both share common technques n feature extracton. However the fnal set of features selected reles heavly on the methods used to extract them. In ths paper we propose a method that s less relant on the feature extracton phase and can be used for sequences of dfferent lengths. To ths end an H based framework s used to study sensor relevance per gesture n laparoscopc surgery and determne the most optmal poston of the sensors n order to acheve enhanced gesture recognton. By usng the combned results from each gesture a fnal sensor glove desgn s proposed. The rest of ths paper wll be structured as follows. Frst a bref ntroducton wll be provded for the H framework and sensor selecton. The BN ensor love (B) wll then be ntroduced whch s followed by an expermental set up valdatng the proposed feature selecton and sensor placement method. 2. H ENOR ELETION FRAEWORK & BN ENOR LOVE 2. Hdden arkov odel ensor electon Framework Hdden arkov odels are often used for gesture recognton applcatons such as Human omputer Interfaces (HI) and sgn language recognton due to ther ablty to cope wth spatotemporal features. These propertes make them deal for clusterng temporal gesture sensor data and as descrbed n ths paper sensor selecton. An H can be defned as λ= ( ΑΒ π) where Α s the state transton matrx Β s the observaton symbol probablty dstrbuton and π s the ntal state dstrbuton []. An H framework can be used for clusterng sequental data by transformng a set of sequences of dfferent lengths to a feature space where clusterng can subsequently be performed. In ths feature space each sequence (or sgnal) s represented as a vector of dstances to Hs traned on a representatve set of sequences [2]. In ths paper the above method s adapted to nvestgate the mportance of a sensor's output to a gven gesture and the correlatons between sensors. Instead of comparng sensor outputs drectly as tme seres of dfferent lengths each sensor output s represented by ts dstance to Hs traned for the whole set of sensor outputs. A sensor's dstance to a H refers to how lkely t s to be predcted by that H and s descrbed by the log lkelhood. If a sensor s output s hghly correlated to the traned H the values of the assocated log lkelhood would also be hgh. ensors that show lttle or no correlaton would have low log lkelhood values. The frst step of the framework descrbed n ths paper s as follows. For each gesture a smlarty matrx R s generated. Ths s done ndvdually based on the followng steps for each gesture: For each of then sensors N an H λ N s traned on that sensor's data usng all nstances of that gesture. For each nstance of the gesture a dstance matrx d s made by calculatng the dstance of each sensor's data from each traned H λ where N. The dstance s defned by the ablty of λ to predct n terms of the resultng log lkelhood. The hgher the log lkelhood the better the H s at predctng that sensor's data. Fnally smlarty matrces R are constructed for each gesture. The average of each correspondng cell n the dstance matrces for that gesture s taken: R m d m= = N () Each element of R represents the average dstance n terms of log lkelhood of a sensor to the traned H λ for gesture. Based on R log lkelhood can be used to udge f a sensor s provdng useful data by observng how well the gesture data from that sensor can be predcted. Relatonshp between sensors can be nferred from the smlarty matrces by observng the log lkelhood of an H traned on one sensor's data predctng data from another sensor. By usng the smlarty matrces an analyss of a sensor's ablty to produce consstent and relevant data can be made. The smlarty matrces not only provde nformaton regardng whch sensors are the most relevant to a gven gesture but also the degree of correlaton between the sensors. The correlaton between the sensors can be used to reduce the amount of redundant data. Identfyng and analysng the correlaton between sensors s the next step n the sensor selecton framework. A weghted feed-forward selecton process s appled to the smlarty matrces of each gesture ndvdually. Ths takes nto account sensor correlaton as well as mportance for creatng a set of sensors for each gesture as follows: The frst sensor to be selected for sensor set K k where k = s the maxmum value of R.e. along the smlarty matrx dagonal where ( N ). The matrx dagonal represents the dstance of each sensor from the H traned usng that sensor's data. Each subsequent sensor for K s chosen as follows:. The correlaton factor s calculated for each remanng sensor. s equal to the sum of the smlarty matrx dstances of sensor tested on the Hs of the prevously selected sensors λ k. 2. ensor 's mportance Q s calculated by usng the followng equaton: Q = wr w N k (2) s c

3 Based on weghtngs w and w where w weghs how much the dagonal smlarty matrx dstances are taken nto account and w weghs how much the correlaton factor s taken nto account. The next sensor to be added to the sensor set s the sensor wth the hghest value ofq. 2.2 BN ensor love The BN ensor glove (B) was desgned based on the BN Node developed by [0] whch ncorporates a modular desgn ncludng a prototype board makng t an deal platform for developng systems. Wreless rado communcaton allows t to transmt data drectly from the sensors to a recevng node for post processng. Ths frees the glove from tralng wres allowng t to be hghly portable and non ntrusve to the user. Due to the subtle gesture nvolved n laparoscopc surgery both 3D and 2D accelerometers (Analog Devces ADXL330 and ADXL202E respectvely) were used for the glove desgn. These were postoned on the glove as shown n Fgure. A easurand (Fredercton NB anada) 270-hapeensor fbre optc bend sensor was postoned across the palm. The B requres 4 BN nodes to accommodate the 20 sensor channels whch are powered by 3V lthum battery. 0: x-axs : y-axs 2: z-axs 7: x-axs 8: y-axs 9: z-axs 3: x-axs 4: y-axs 5: z-axs 6: x-axs 7: y-axs 8: x-axs 9: y-axs : x-axs 2: y-axs 3: z-axs 4: x-axs 5: y-axs 6: z-axs 20: Bend sensor Fgure. ensor confguraton for the BN love. 3. EXPERIENTAL ET UP 3. Expermental etup The experment was carred out usng a laparoscopc box traner whch are commonly used for tranng novce surgeons. A standard laparoscopc grasper wth a rng grp handle was used to perform the gestures wthn the box traner. Fve partcpants took part n the experment and were asked to perform a seres of gestures usng the tool n the box traner wearng the B on the rght hand. A descrpton of these motons s gven n Table. Each of the gestures was performed fve tmes by every partcpant. These gestures were chosen after observng vdeos of the procedures beng performed. Albet beng smple they form basc manpulatons requred to perform laparoscopc procedures [3]. For example cuttng and manpulatng tssues ncorporates estures 2 and estures and 3 respectvely. Three male and two female subects took part n ths experment. Both sexes were used so as to take nto consderaton factors such as the hand sze. They were each shown how to hold the tool and the sequence of gestures to follow. All subects were novces as the am of ths experment was to collected data from strctly prescrbed gestures and any dfferences n the data between a novce and an expert would be neglgble. Data was collected from the sensors at a samplng rate of 33Hz and transmtted from the B to a laptop for off lne postprocessng. A medan flter was used to remove spurous peaks n the data. Each of the 25 nstances of a gesture was normalzed before the H framework as descrbed n ecton 2 could then be appled. Table. Laparoscopc estures Performed by B esture REULT 4. mlarty atrx Descrpton Left/rght traverse of the tool tp Openng and closng of the tool tp Up/down traverse of the tool tp Rotatng the rotculator of the tool Ant-clockwse rotaton of the tool Followng the applcaton of the H framework smlarty matrces R where N were generated for each of the gestures. The dagonal elements of the smlarty matrces were plotted over the 20 sensors for each of the gestures as shown n Fgure 2(a). From ths graph the sensors that are predcted most successfully by the H traned on ts data can be seen. These well predcted sensors for each laparoscopc gesture gve an ndcaton of the sensor's relevance. Fgure 2(b) shows the smlarty matrx for esture. The darker the cell the better sensor 's data can be predcted by the traned H. From Fgure 2(a) t can be seen that for esture ensors 0 6 and 7 have the hghest rankngs whch ndcate they have the best descrptve power for ths gesture. By examnng Fgure 2(b) the relatonshps between these sensors and the rest of the sensors can be seen. The H traned for ensor 0 also manages to predct ensor 7's data and vce versa ndcatng the data s hghly correlated. Ths suggests that data from both sensors may be redundant. From Fgure the poston of ensors 7 and 0 are both on the x-axs of 3D accelerometers on the ndex and mddle fngers. However ensor 6 does not

4 (a) (b) (c) Fgure 2. (a) Plot of the dagonal elements for matrx R. (b) mlarty matrx R for esture the darker the square the better the sensor 's data can be predcted by H. (c) esture classfcaton based on sets selected by usng the weghted feed forward algorthm. correlate closely to any other sensors ndcatng the data provded by ths sensor s unque. any other nstances of sensor correlaton can also be seen n Fgure 2(b) showng the mportance of takng such correlaton nto account when selectng sensors. 4.2 ensor orrelaton As can be seen n the prevous secton there s sgnfcant sensor correlaton leadng to redundancy. The weghted feed-forward algorthm was appled to the smlarty matrx of each gesture usng permutatons of weghtngs for the matrx dagonal dstances w and correlaton factor w. The weghtngs were chosen so as to place dfferent emphases on how descrptve the sensors are based on the matrx dagonal and how much the correlaton of the sensors s taken nto account. Ranked sensor sets were created for each gesture. Each set s classfcaton power was tested by usng H to classfy the target gesture usng dfferent numbers of the ranked sensor. By usng esture as an example Fgure 2(c) shows the classfcaton rates based on these sensor sets. It has been found that the sensor set that gave the best classfcaton performance was produced wth weghtngs 0.3 and 0.7 for w and w respectvely. It has also been observed that 00% classfcaton accuracy can be acheved by usng 9 of the top rankng sensors based on the weghted feed forward algorthm. Ths s an ncrease of 28% from 72% classfcaton when usng sensors that were ranked based on dstances from the dagonal of the smlarty matrx. The above process was appled to all of the fve gestures. Fgure 3(a) shows the classfcaton of each gesture based on the dscrmnatve power of a sensor as determned by rankng the dstances of the dagonal for each gestures smlarty matrx.e. no sensor correlaton s taken nto account. Fgure 3(b) shows the most optmal classfcaton for each gesture based on the ndvdual sensor sets generated usng the weghted feed forward algorthm. For each class t can be seen that by reducng the amount of correlated data sgnfcant mprovement n classfcaton s acheved usng fewer sensors. (a) (b) (c) Fgure 3. (a) lassfcaton for each gesture based on the ranked dagonal of each sensors smlarty matrx. (b) lassfcaton for each gesture based on optmal ndvdual sensor selecton. (c) Fnal classfcaton of gestures usng the fnal optmal sensors selected.

5 4.3 ensor electon and Valdaton Thus far each gesture has been treated ndvdually to determne the maxmum dscrmnatve power of the sensors. In order to create the fnal sensor selecton for the B a combnaton of the sensor selecton results for all gestures s requred. Ths was acheved by takng the sensor set provdng the maxmum classfcaton rates possble for each gesture or class followed by assgnng each sensor a weght dependng on ts rankng n the data set. These were then summed and averaged to provde a fnal ranked sensor set. Based on ths the H was once agan traned usng dfferent numbers of the ranked sensors. Fgure 3(c) shows the classfcaton rates gven optmal sensor selecton for all classes. Based on Fgure 3(c) the ntersecton between the classfcaton performance of all the gestures and the number of sensors requred can be determned. It can be seen that there s an ncrease n the classfcaton of esture 2 of 8% whereas for estures and 3 there s a drop of 8% and 2% respectvely. The drop n classfcaton accuracy s most lkely due to the combnaton of the fve sets of sensors. As only the top 2 ranked sensors are chosen based on the fve gestures sensors that are mportant for one specfc class are not guaranteed to be ncluded n the fnal set f they do not contrbute sgnfcantly to the overall system accuracy. From the orgnal 20 sensors a reducton of 8 sensors has been acheved wth only a mnor loss of classfcaton accuracy and n one case an ncrease n accuracy. The 2 fnal sensors selected n descendng order are: and 5 as shown n Fgure ONLUION Ths paper proposes an H sensor selecton framework for the B wth applcatons to laparoscopc tranng and sklls assessment. The method provdes an effectve means of observng the propertes of a sensor and ts relatonshp n terms of correlaton to other sensors. It has been shown that by treatng gestures ndvdually the applcaton of a feed forward algorthm can be used to determne a sensor's rank dependng on the qualty of the gesture data and ts correlaton to other sensors. Based on ths nformaton for all the gestures consdered the most relevant sensors can be combned thus leadng to a reducton n the number of sensors used whle mantanng the overall classfcaton accuracy Fgure 4. Fnal ensor electon. An extenson to ths work would be to nclude the sensors locaton on a devce such that the framework ncludes correlaton self-predcton as well as sensor locaton. All these would lead to further mprovements of the B desgn. 6. REFERENE [] Vereczkel A. Bubb H. and Feussner H. Laparoscopc surgery and ergonomcs: It's tme to thnk of ourselves as well. urgcal Endoscopy (0): [2] Darz A. mth. and Taffnder N. Assessng Operatve kll. BJ : [3] allagher A.. Rche K. clure N. cugan J. Obectve Psychomotor klls Assessment of Experenced Junor and Novce Laparoscopsts wth Vrtual Realty. World Journal of urgery (): [4] Rosen J. Hannaford B. Rchards.. nanan.n. arkov modelng of mnmally nvasve surgery based on tool/tssue nteracton and force/torque sgnatures for evaluatng surgcal sklls. Bomedcal Engneerng IEEE Transactons on (5): [5] Berguer R. Forkey D.L. and mth W.D. Ergonomc problems assocated wth laparoscopc surgery. urgcal Endoscopy (5): [6] Quck N.E. llette J.. hapro R. Adrales.L. erlach D. Park A.E. The effect of usng laparoscopc nstruments on muscle actvaton patterns durng mnmally nvasve surgcal tranng procedures. urgcal Endoscopy (3): [7] atern U. Kuttler. ebmeyer. Waller P. Fast. Ergonomc aspects of fve dfferent types of laparoscopc nstrument handles under dynamc condtons wth respect to specfc laparoscopc tasks: An electromyographc-based study. urgcal Endoscopy (8): [8] Lal T.N. chröder. Hnterberger T. Weston J. Bogdan. Brbaumer N. chölkopf B. upport Vector hannel electon on BI. IEEE Transactons on Bomedcal Engneerng (6): [9] Worden K. and Burrows A.P. Optmal sensor placement for fault detecton. Engneerng tructures 23 (200) [0] Yang.-Z. Body ensor Networks ed..-z. Yang London: prnger-verlag [] Rabner L.R (989) A tutoral on hdden arkov models and selected applcatons n speech recognton. Proceedngs of the IEEE (2): [2] Bcego. urno V. and Fgueredo.A.T. mlartybased classfcaton of sequences usng hdden arkov models. Pattern Recognton (2): [3] Henrchs W.L. rvastava. ontgomery K. Dev P. The fundamental manpulatons of surgery: a structured vocabulary for desgnng surgcal currcula and smulators. The Journal of the Amercan Assocaton of ynecologc Laparoscopsts 2004:

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