Multisensor Data Fusion for Prosthetic Control

Transcription

1 Multsensor Data Fuson for Prosthetc Control Henry W. Zheng Centennal Hgh School Maryland, U.S.A. Abstract A fully neurally ntegrated hgh degree of freedom prosthetc lmb system s the goal of the Revolutonzng Prosthetcs program, a large-scale Defense Advanced Research Projects Agency (DARPA) project. Mult-Sensor Data Fuson (MSDF) for prosthetc control s one of the research areas. Ths paper proposes a novel approach of applyng MSDF technology to prosthetcs. The paper proposes a prosthetc data fuson framework, analyzes characterstcs of MSDF technques, and suggests a new algorthm for motle fuson. Specfcally, ths study compares the fdelty of sngleclassfer decsons wth mult-classfer decson fusons on smulated sometrc bcep contractons. It reveals some nterestng fndngs and nsghts and provdes a better understandng of MSDF applcatons n the medcal feld. The results confrm the envsoned potentals of applyng MSDF for prosthetc control. Keywords: classfer, data fuson, MSDF, motle fuson, prosthetc, prosthess. Introducton For decades, research has been done to make prostheses anthropomorphc and bommetc. Scentsts are stll tryng to understand the mechansms of how a human bran controls muscles, and then take ths understandng and develop a prosthetc lmb system. A fully neurally ntegrated hgh degree of freedom lmb system s the goal of the Revolutonzng Prosthetcs program (RP), a large-scale DARPA project []. Dfferent parts of the bran specalze n specfc cortcal tasks. Each area contans dstnct nformaton that other areas do not have []. For example, a recordng of cortcal sgnals from the prmary motor cortex (M), the prmary control center of the bran for moton, contans nformaton causng an arm to move toward a desred locaton, but the sgnal qualty s not very hgh [3]. On the other hand, an electrode recordng of efferent sgnals from motor neurons n the shoulder contans fnely detaled nformaton regardng the ampltude and duraton of the force that would be appled by the arm, but lacks locaton nformaton that would allow a prosthetc arm to place these otherwse arbtrary commands n context [4],[5]. Therefore, nformaton from dfferent areas can be complementary whle sgnals wth dfferent levels of detals from one area can be supplementary. Research has shown that acton potentals (spkes) from the M have very dfferent patterns n terms of frequency and tmng, compared to the spkes emtted by perpheral motor neurons [3], [6], [7], [8]. Thus, an optmal soluton would be to utlze both complementary and supplementary nformaton from the bran and the motor neurons to control the prosthess. The mathematcal process of ncorporatng outputs of multple classfers ntellgently to create a more complete set of nformaton s called data fuson. Prosthetc control could be a prme area for the applcaton of data fuson. In general, data fuson takes two or more classfers outputs, called decsons, and mathematcally syntheszes them together to generate a new decson that encompasses as much of the classfers nformaton as possble [9]. Data fuson can reduce the vulnerablty of temporary, aberrant sgnal devatons that would affect a sngle-classfer framework [0]. It can also mprove processng speed because fewer redundances cut down computaton complextes. Ths study resulted n the followng contrbutons: It proposes a novel approach and a framework of applyng MSDF technology to prosthetcs; It also proposes a novel motle fuson algorthm called Jont Bayesan Decson (JBD); It compares the fdelty of sngle-classfer decsons wth mult-classfer decson fusons on smulated sometrc bcep contractons and shows that MSDF mproves sgnal fdelty. It also provdes a better understandng of MSDF n the medcal feld. The rest of the paper s organzed as the followngs: Secton two brefly ntroduces some pertnent background knowledge and related works. Secton three presents the proposed MSDF prosthetc control framework, the smulaton modules, and the detals of the data fuson algorthms. Secton four provdes detals for a model and smulaton of a motle fuson system for prosthetc control. Consequently, secton fve presents the smulaton results and shares our frst hand observatons and experences learned from the study. Secton sx concludes the paper and provdes future drectons of the study.. Background and Related Works Ths secton wll explan the concepts of prosthetcs control, MSDF, and motle fuson. It also explans why motle fuson wll be a good tool for prosthetc control and how to buld connectons between motle fuson and prosthetc control. 85

2 . Prosthetcs Control Prosthetc control s the converson of sgnals acqured from neurons to commands for the movement of an eletromechancal prosthetc lmb. Ths requres the acquston, nterpretaton, and executon of the sgnal n order to successfully drect the arm to move as an amputee requres t to move. Prosthetc control contans several specal problems that are not addressed by conventonal MSDF. The frst s the ssue of usng a dscrete number of classes to cover a contnuous space. The errors from groupng contnuous values nto dscrete categores may cause jerky movements. Secondly, cortcal plastcty requres dynamc retranng of classfers and data fuson algorthms, a problem that needs to be studed n future work [], [].. Classfers In mathematcs, a classfer s a mappng from a feature space X to a dscrete set of labels Y [3]. Classfer technques have a broad range of applcatons ncludng medcne, fnance, computer vson, and voce recognton [4]. Researchers are usng classfers to extract varous characterstcs of neural sgnals, whch can be used by a decson-makng devce for controllng a computer to execute ntended actons..3 Multsensor Data Fuson Multsensor Data Fuson (MSDF) s a mathematcal approach to combnng several classfers together, leadng to mproved results (greater accuracy, faster computaton tme, etc.) [0]. MSDF has many areas of applcaton, ncludng land mappng, bometrc analyss, automated target trackng, handwrtng analyss, etc. MSDF yelds superor decsons comparng to sngle-classfer schemes because there s more nformaton from multple sensors that collect dfferent characterstcs. For example, n land mappng, radar detecton of elevaton and vsble-lght detecton of vegetaton are two dfferent condtons of terran dffculty. ether sensor could ndvdually contan all the detals necessary to descrbe the terran dffculty. Thus, data fuson could be used to assess the terran by takng the nput from both sensors nto consderaton. In summary, potental advantages of MSDF are: ncreasng system performance, mprovng robustness, reducng sensor redundancy, extendng spatal and temporal coverage, shortenng response tme, and reducng communcaton and computng complextes..4 Motle Fuson Motle fuson s a new category of data fuson that s proposed and developed n ths paper. It s the applcaton of data fuson specfcally talored to the demands and characterstcs of prosthetc control. Motle fuson s a subset of data fuson, but s dfferent from most other applcatons of data fuson n that the motle fuson addresses a dynamc decson (e.g. a movng prosthess). Ths s opposed to mage processng data fuson applcatons, whch analyzes statc pctures. Motle fuson also does not have a bas preference e.g. should confusons exst, t cannot gve preference to a safer technque. Ths s opposed to some mltary data fuson applcatons that would gve preference towards cauton f a large number of sensors dsagree about the outcome [5]..5 General Technques n Motle Fuson Weghted Average Vote (WAV): Each classfer places a confdence level value for the sgnal for each class. Each classfer may be weghted dfferently. The class wth the hghest value s selected as the decson class. Behavor Knowledge Space (BKS): Each of classfers s plotted on an -dmensonal space wth values representng the classes. Sgnals are plotted as ponts on ths space, and the decson of greatest frequency assocated wth that partcular classfer output combnaton s selected as the decson. Bayesan Combnaton: Takes a tranng sequence to calculate pror probabltes. Unknown sgnal s nputted and class wth hghest probablty s chosen. Table Comparson of Decson Fuson Technques [9] Fuson Algorthm Weghted Average Vote Behavor Knowledge Space Bayesan Combnaton Pros Takes nto consderatons varyng confdence levels of classfers Smple and versatle Versatle relatonshp between classfers; does not requre dsjont classes; can establsh expert classfers for dfferent classes 3. Motle Fuson Applcaton n For Prosthetcs Control Compared to the data fuson technologes that are used for target trackng, topology, and magng, data fuson for prosthetcs wll face dfferent challenges: It requres real-tme decson-makng; It usually uses less sensors; It usually has more decson ponts. Cons Works best wth a large number of classfers; requres dsjont classes Requres large amount of tranng data Large number of classfers may be computatonally tedous 853

3 All of the above wll ncrease computatonal and communcaton complexty, whch needs to be consdered n a desgn. 3. Proposed Data Fuson Framework To fulfll Revolutonzng Prosthetcs program s objectve, a framework has been proposed for the frst tme by the author to use motle fuson technques for prosthetcs control. The motle fuson framework s shown n Fgure. The framework conssts of three major components: Multple decsons classfers Each classfer s used to analyze dfferent sgnals that dctate dfferent attrbutes assocated wth the acton of the prosthess. Motle fuson engne - It has multple data fuson algorthms and nference logcs The fuson engne serves to ntegrate several fuson technques together and apples fuson technques onto fuson algorthms to reach a meta-decson that wll be executed as a command by the prosthess. Decson feedback channels - It bulds a closed loop to add one more dmenson of nformaton for more precse control by adjustng decsons through real-tme acton feedback. Data Tracks T T T m Classfer C Fuson Algorthm Inner-loop feedback Classfer C Fuson Algorthm Fuson Engne Meta Decson To Prosthess Classfer Cm Fuson Algorthm K Outer-loop feedback Fgure : Proposed Prosthetc Data Fuson Framework 3. Motle Fuson Modelng and Smulaton In nvestgatng motle fuson technques MATLAB was used to mplement data fuson algorthms of the proposed framework, whch ncludes the mplementatons of wellknown fuson algorthms such as Weghted Average Votng (WAV) and Behavor Knowledge Space (BKS), n addton to the Jont Bayesan Decsons (JBD) proposed by ths study. The modelng and smulaton modules that have been mplemented are shown n Fgure. Sngle Classfer Data Set S T T T... T Classfer Smulaton C ( n) = S( n) + E( n) Fgure : Motle Fuson Smulaton Dagram 3.. Classfer Smulaton MSDF Data Set S T T T... T Classfer Smulaton C ( n) = S ( n) E ( n) + WAV D D D Performance Index Calculaton rms ( Dˆ S ) Based on exstng research and concepts, we created multple neuron-spke sequences and random nose to smulate behavors of a classfer. Specfcally, wthout usng the data fuson (see the left half of Fgure ), a decson sequence D (also the output of a classfer C ) s represented as an addton of an orgnal neuron-spke sequence and a random nose functon shown n equaton (): C ( t) = S ( t) + E (), where C s the output of the th classfer; S s the th neuron-spke nput sgnal sequence or tranng set, whch also serves as the deal output or decson sequence for comparng the fdelty of a fuson algorthm; and E s the nose functon used to smulate noses for the th classfer. In the smulaton, E was generated usng a MATLAB random number generator wth dfferent seeds, whch s added on or subtracted from the orgnal neuron-spke sequence. Usng the data fuson (see the rght half of Fgure ), C s fed nto data fuson algorthms that are mplemented n the fuson engne; then D s the th metadecson generated by a data fuson algorthm. D represents the ntenton of an orgnal neuron-spke sgnal and can be used by a computer to execute a muscle movement. An n-depth study on classfers s beyond the scope of ths paper. = = BKS D JBD 854

4 3.. Weghted Average Vote (WAV) One data fuson algorthm used n ths study s called the Weghted Average Vote (WAV). In WAV, as shown n Fgure 3, each classfer places a confdence-level value on each sgnal n a decson set. Each classfer may be weghted dfferently because some classfers may be more accurate or have more relevant nformaton than other classfers. The sgnal wth the hghest confdence value s selected as the fnal decson. The WAV uses the followng formula to calculate the maxmum confdence: K Q ( t) = arg max w y j = K =, j ( t ) (). Where, Q s the output at any tme t; w s the weght of the th classfer; and y,j s the j th class of the th classfer. It frst calculates the weghted average confdence levels of each of K classfers w y ( ), and then chooses the class, j x wth the hghest confdence value among classes. The weght for the th classfer s calculated from: w K j= ( C, j = K K ( C ) k = j= k, j ) c, c, K (3), where C = (4). c K, ck, K 3..3 Behavor Knowledge Space (BKS) Another method used s called the Behavor Knowledge Space (BKS) as shown n Fgure 4. It operates by takng known tranng ponts and comparng them to the nput sgnals to fnd a match, whch generates a decson. In BKS, each of n classfer s plotted on an n-dmensonal space. Each classfer presents all of ts avalable decson ponts on ts dmenson. For example, a BKS wth two classfers, one for cortcal sgnals and one for perpheral sgnals, s on a -dmensonal plane wth the x- and y-dmenson representng the estmated decson by the M and perpheral classfers respectvely. A tranng set (e.g. known sgnals for movng an arm) frst runs through the algorthm to calbrate varous classfers n the space. The acton assocated wth each combnaton of classfer outputs s recorded n a decson database called the knowledge space. Then an unknown real sgnal s plotted as a pont on the plane. The knowledge space takes the combnaton of classfer output and chooses the class that most often s assocated wth the combnaton of classfer output. The output of the BKS wll control the prosthess to execute the movement. Tranng Sgnals c,j s defned by: c, j = E( ε ε j ) = E( y d( x) )( y j d( x) ) (5), where ε (x) and ε j (x) s the error defned by y d( x) and y j d( x), respectvely. y (x) represents the mean of the classfer output, and d(x) represents the deal data. Classfer C Classfer C. Classfer Cm Confdence Level set Decson Database BKS core Meta Decson To Prosthess Classfer D,, C, D,, C, D,,,C, Weght Classfer C Classfer C Classfer Cm. Pre-defned Decson set {,,, } Meta Decson To Prosthess Unknown Sgnals Classfer D,, C, D,, C, D,,,C, Confdence Level set Weght Fgure 3: Weghted Average Vote Algorthm Fgure 4: Behavor Knowledge Space Algorthm 3..4 Jont Bayesan Decsons (JBD) Enlghtened by the prevous two algorthms and applyng Bayesan probablstc model, the thrd motle fuson algorthm beng proposed s called Jont Bayesan Decsons (JBD) shown n Fgure 5. JBD apples Bayes Theorem to acheve data fuson [6]. JBD combnes classfers outputs statstcally to decde the most accurate meta-decson. A tranng sequence s fed nto the JBD s Bayes probablstc model. The Bayes logc s traned, and decsons lkelhood 855

5 probabltes are decded. When a set of unknown classfer outputs arrves, Bayes Theorem s appled and a metadecson s made. For the prosthetc meta-decson, an extenson of Bayes Theorem s appled, whch ncludes all outputs of classfers as an nput data set of the fuson algorthm. Each element of the nput data set works jontly to decde what the rght decson s. Every possble classfer-decson combnaton s evaluated. A maxmum functon decdes whch decson has the hghest possblty to become a meta-decson. The formulae are shown n equatons (6) and (7): Dˆ C, C,.., C ) k M = M C, C,.., CM Dˆ k ) Dˆ k ) C, C,.., C Dˆ ) Dˆ ) = M (6), M D C,,.., ) arg max ( ( ˆ C CM = p D C, C,.., CM ) ) (7), = where M s the total number of decsons; C s the output of the th classfer; Dˆ s the th element of the metadecson. The beneft of JBD s that t ncludes the nformaton of WAV and BKS. The pror knowledge used by JBD durng ts tranng s smlar to the weght factor estmatons used by WAV both algorthms calculate the weght of dfferent classfers and the accuraces of each classfer for of evaluatng actons. Smlar to the BKS method, the JBD method chooses the hghest probablty decson as the best meta-decson. Classfer C Classfer C Classfer Decson Counter To Prosthess Classfer Bayes Probablstc Model D) C,..., Cn D) p ( D C,..., Cn ) = C,..., C ) Meta Decson Fgure 5: Jont Bayesan Decsons (JBD) Algorthm. n Cn 3..5 Performance Index Calculaton In order to compare the performance of dfferent motle fuson methods, we used the root mean square (RMS) method to evaluate the decson accuracy as shown n equaton (8), rms = = ( Dˆ S ) (8), where s the total numbers of decson ponts, Dˆ s the th element of the observed set, and S s the th element of the classfer output set. 4. Modelng and Smulaton 4. Classfers Output Sequences In the smulaton, there were two tracks of smulated sgnals, one representng sgnals from the perpheral nerve of the arm and one representng sgnals from the motor cortex. Due to the lack of real classfer output decsons, several smulated classfer decson sequences were generated. A sequence was seconds long wth a total of,00 decsons, each of whch was from a 0 mllsecond (ms) samplng perod for an sometrc bcep contracton. The output decsons from the two classfers vared due to ther error models. Ths reflects dfferent characterstcs of dfferent neurons that the smulaton attempted to capture. One such example s usng the output of one classfer to replace cortcal sgnals and another for perpheral motor sgnals. Cortcal neurons tend to have larger error values than perpheral motor neurons because there s not a drect mappng from decson to acton. Though both classfers analyze the sgnals for the same acton, the outputs of each classfer vary. To test the data fuson algorthms n a varety of classfer scenaros, the error functon was modulated. Two types of random varatons were used: Gaussan dstrbutons wth dfferent standard devatons and unform dstrbutons wth dfferent ranges. 4. Tranng Sequences In order to compare the performance, all three fuson algorthms (WAV, BKS, and JBD) used the same,00- decsons tranng sequence. Each pont ncluded a known decson and the outputs of two classfers related to the known decson. The fuson algorthms were then traned: WAV: the tranng sequence was fed nto the WAV algorthm to optmze weghts, BKS: the tranng sequence was fed nto the BKS algorthm to generate the knowledge space, JBS: the tranng sequence was fed nto the JBS algorthm to generate lkelhoods and pror probabltes. After tranng was fnshed, a sequence of unknown classfer outputs was fed nto the three data fuson 856

6 algorthms to produce meta-decson results. These results then were compared wth the orgnal acton sequence to assess each algorthm s performance. To cover a varety of mportant patterns, several sequences wth dfference error patterns were used. Each fuson algorthm performed dfferently based on the nput of these sequences. 4.3 Classfer Smulaton Two types of classfers were used; one whose E had a normal dstrbuton and one whose E had a unform dstrbuton. In the normally dstrbuted classfer, the error dstrbuton was modeled by roundng E ( t) = ( μ = 0, σ ), where σ {, 0.5, 0.5, 0., 0.05, 0.0} (9). In the unformly dstrbuted classfer, the error dstrbuton was modeled wth E ( t) = U[, ]. The frst set of experments tested the scenaro where two classfers had the same type of error dstrbuton but dfferent σ values. The second set of experments tested the scenaro where the two classfers had dfferent types of error dstrbutons. Fnally, a thrd set of experments wth the same unform error functon at dfferent ranges was used to control for the dstrbuton. 4.4 Performance Index Measurement The performance ndex measurement was obtaned by computng RMS onto the output meta-decson sequence versus the orgnal acton sequence. Fve sets of experments were carred out for evaluatng the algorthms characterstcs and comparng performance mprovement of MSDF (WAV, BKS, and JBD) over sngle classfers (classfer alone and classfer alone). The complete experment settngs are summarzed n Table. decson fuson algorthm. After each step, amount of error ncreases. These accumulatve errors were smulated through equaton (), where equaled. S was n the form shown n Fgure 6, where the x-axs represented tme n 0ms and y-axs represented force n kg. It was desgned to smulate an actual sometrc bcep sequence, where each force was appled from a restng start for one second and then relaxed for one second ms samples were taken for ths entre sequence, wth one sample from each 0-ms bn. Fgure 7 shows the correspondent classfer output sgnal sequence. Fg. 6 Input & Ideal Output Sgnal E Sngle Multple Table Summary of Experment Settngs Classfer Error Functon 5 Smulaton Results MSDF Tranng Sequence Both: ormal o ormal : ormal σ = {, 0.5, : Unform 0.5, 0., 0.05, o C: ormal 0.0}; WAV C: ormal Unform BKS C: ormal U [,] JBD C: Unform C: Unform C: Unform Range Range U[, ] Range={,.5,.,, 0.5} WAV BKS JBD Raw sgnals from the body undergo several transformatons before beng outputted by the classfers and taken n by the Fg. 7 Classfer Output Sequence C (σ = ) 5. Analyss of Standard Devaton Modulaton wth Balanced Classfers A comparson of decson fuson algorthms reveals several notable characterstcs. Fgure 8, Fgure 9, and Fgure 0 show the RMS error of each classfer alone and n the three decson fuson algorthms when two classfers have the same error functon. As the standard devaton or range decreased, the error from classfer and dropped off dramatcally n both sets of experments. Ths s due to the shrnkng error, whch reduces the probablty of dfferences from the orgnal acton sequence. (There are small dscrepances between C and C, whch s caused by 857

7 randomness of the nose.) Thus, t s not surprsng that the classfers and data fuson algorthms mproved as the standard devaton of the two classfers output data shrank. It mples that wth an deal (noseless) nput, both sngle and multple classfers wll perform well. However, a strkng fact s that mprovement through data fuson s very dramatc for classfers wth a large amount of errors. Oftentmes, poor nformaton gven nto an algorthm usually yelds poor results, but the outputs from the data fuson algorthms were substantally better than the sngle classfers alone. Thus, MSDF s effects are more potent when the classfers are noser. In partcular, WAV seems to outperform the other algorthms by a lttle, especally when standard devatons shrank. Ths s because weghts used to calbrate each classfer n WAV reduce the RMS and result n mprovement over BKS or JDB. 5. Analyss of Standard Devaton Modulaton wth Unbalanced Classfers In an unbalanced classfer (error-functon) scenaro as shown n Fgure 0, the mprovement from data fuson s agan apparent. The RMS of poorly performng sngle classfers (C and C) was over eght tmes the fused RMS. As seen n the prevous experments, a reducton n standard devaton s drectly correlated wth mproved accuracy of the data fuson algorthms. In partcular, BKS and JBD yeld better results wth unbalanced classfers than WAV. Ths s because WAV averages together both classfers, whle BKS and JBD may effectvely gnore the poor performer. Unbalanced classfers serve to characterze dfferent aspects of the data fuson algorthms. Although all three of the algorthms have mechansms to optmze the level of nformaton taken from each classfer, the algorthms may not completely protect from compromsng of the classfers. Surprsngly, the data fuson algorthms were much more robust than expected. As the standard devaton of the Classfer decreased whle the error range of the Classfer remaned unchanged, the error from the data fuson algorthms decreased as well. In fact, t decreased almost as much as n the scenaro wth two balanced classfers. Ths s an mportant mplcaton; t ndcates that balancng classfers wll not be essental. 5.3 Dscusson of Results One result that arose from the experments was that BKS and JBD were nearly dentcal n ther accuracy. Ths s due to the mechansms used by JBD, whch calculates pror probabltes only from a sngle value per 0-ms bn. Ths s opposed to WAV, whch consders an entre dstrbuton of confdence values for each class per 0-ms bn. Second, WAV outperforms BKS and JBD wth balanced classfers because WAV fnds the decson pont near the center of the ndvdual classfers outputs. In a balanced scenaro, the deal decson ponts are lkely to le between the two classfers outputs; t s hghly possble that a decson from one classfer s hgher and the other s lower RMS than the deal decson. BKS and JBD do not utlze ths characterstc of balanced classfers, whch results n relatvely poorer performance. Thus, correlaton between the classfers may undermne the robustness of BKS and JBD. Thrd, unbalanced classfers wth large error yeld better results from BKS and JBD than from WAV. Ths stems RMS RMS Comparson of RMS for Fuson Algorthms and Sngle Classfers (Classfer : ormal ose; Classfer : ormal ose) Standard Devatons Comparson of RMS for Fuson Algorthms and Sngle Classfers (Classfer : ormal ose; Classfer : Unform ose) Standard Devatons WAV BKS JBD C C Fgure 8: Balanced ormal-ormal Classfer RMS Comparson Comparson of RMS for Fuson Algorthms and Sngle Classfers (Classfer : Unform ose; Classfer : Unform ose) WAV BKS JBD C C Range Fgure 9: Balanced Unform-Unform Classfer RMS Comparson WAV BKS JBD C C Fgure 0: Unbalanced Classfer Performance RMS Comparson 858

8 from the fact that WAV averages the two classfers together, so the real decson pont s assumed to be between the outputs of the two classfers decson ponts. WAV assumes that the deal decson ponts are between the outputs of the classfers. However, as one class s hghly naccurate relatve to the other, the range between the outputs of the two classfers for any one 0-ms bn wll fluctuate wldly, undermnng the accuracy of WAV. BKS and JBD do not suffer from ths problem because ts tranng set s not based on the assumpton that the deal class s between the outputs of the classfers. Thus, BKS and JBD do not suffer from the fluctuatons that affect WAV. 6 Concluson MSDF yelds notceable mprovements n decson accuracy, and the combnaton of classfers wth dfferent strengths do not undermne the qualty of the data fuson. Thus, t appears that data fuson s a vable technology that can be appled to prosthetcs control. Furthermore, the Jont Bayesan Decson fuson technque holds promse as a data fuson algorthm. As the results show, the unbalanced nature of combnng cortcal and perpheral neurons n a data fuson system wll not be a hndrance to the performance of the system. The next step n mplementng data fuson onto a real prosthetc wll be the collecton of real neural sgnals and applyng data fuson technques onto cortcal and perpheral sgnals. Furthermore, feedback channels can be mplemented nto the archtecture to fne-tune drectonal movement. Fnally, dfferent data fuson technques can be combned nto herarchcal data fuson archtecture, or novel data fuson technques can be created that are talored to the unque demands that prosthetc control has on decson fuson and MSDF n general. The use of multple classfers n conjuncton wth data fuson s just the frst step n a long journey of decodng and elucdatng the mechansms of the bran [7]. Acknowledgement The author deeply apprecates Mr. Stuart Harshbarger from Johns Hopkns Unversty Appled Physcs Laboratory (JHU/APL) to provde the opportunty for the author to work n ths exctng project. The author also deeply apprecates the help of hs mentors Dr. Jeffery Lesho and Dr. James Beaty from JHU/APL where the author performed the research. They oversaw every step of author s research, from the proposal of the dea and the lterature search to the analyss of hs expermentaton, and offered nvaluable advce n both research and on ths paper. References [] Armed wth Ideas - APL Leads Prosthess Development Team arm.asp [] G. Rzzolatt, The Cortcal Motor System, euron, vol. 3, pp , September 7, 00 [3] D. Flament et al., Relatons of Motor Cortex eural Dscharge to Knematcs of Passve and Actve Elbow Movements n the Monkey, the Journal of europhysology, Vol. 60, o. 4, October 988 [4] J. R. Close et al., Motor-Unt Acton-Potental Counts: Ther Sgnfcance n Isometrc and Isotonc contractons, the Journal of Bone and Jont Surgery, [5] E. Todorov, Drect Cortcal Control of Muscle Actvaton n Voluntary Arm Movements: a Model, ature neuroscence, Vol. 3, o. 4, Aprl 000 [6] W. T. Thach, Correlaton of eural Dscharge wth Pattern and Force of Muscular Actvty, Jont Poston, and Drecton of Intended ext Movement n Motor cortex and Cerebellum, the Journal of europhysology, Vol. 4, o. 3, Mary 978 [7] A. P. Georgopoulos, On the Relatons between the Drecton of Two-Dmensonal Arm Movements and Cell Dscharge n Prmate Motor Cortex, the Journal of euroscence, Vol., o., pp , [8] S. Kake et al., Muscle and Movement Representatons n the Prmary Motor Cortex, Scence, Vol. 85, September 999, pp , ov. 98 [9] ayer Wanas, Feature Based Archtecture for Decson Fuson. Waterloo, Ontaro, 003. [0] R. Polkar et al, Multple Classfer Systems for Multsensor Data Fuson, Proceedngs of the IEEE Sensors Applcatons Symposum 006, (SAS06), Feb. 006 [] L. R. Hochberg et al., euronal Ensemble Control of Prosthetc Devces by a Human wth Tetraplega, ature, Vol. 44, o. 3, July 006 [] J. Sanes et al., Plastcty and Prmary Motor Cortex, Annual Revew euroscence Vol. 3, March 000, [3] Classfer (mathematcs) [4] D. Hall et al., Handbook of Multsensor Data Fuson, CRC Press, Washngton D. C. 00 [5] F. Salzensten and A. O. Boudraa, Unsupervsed Multsensor Data fuson approach, Proceedngs of Internatonal Symposum on Sgnal Processng and ts Applcatons (ISSPA), August, 00 [6] W. Wu et al., Bayesan Populaton Decodng of Motor Cortcal Actvty usng a Kalman Flter, Techncal Report, Dvson of Appled Mathematcs, Brown Unversty, C ms 308, May 005 [7] Jeff Hawkns, On Intellgence Tmes Books,