Advisor: Dr. Hsu

Reporter: Y.P.Huang

OutlineOutline

MotivationMotivation

ObjectiveObjective

IntroductionIntroduction

MethodMethod

ResultResult

Conclusions and Future WorkConclusions and Future Work

Personal OpinionPersonal Opinion

MotivationMotivation

Time series analysis is used for simulaneous analysis Time series analysis is used for simulaneous analysis of multiple channels of dataof multiple channels of data

Define complex inter- and intra- channel features of Define complex inter- and intra- channel features of electromyographic data for pattern classification.electromyographic data for pattern classification.

Quantify differences in complex patterns of electromQuantify differences in complex patterns of electromyographic data has patential value for clinical and resyographic data has patential value for clinical and research applicationsearch applications..

ObjectiveObjective

An unsupervised clustering neurocomputational apprAn unsupervised clustering neurocomputational approach self organizing maps was applied to the problem oach self organizing maps was applied to the problem of time series analysis of electromyographic data of time series analysis of electromyographic data

Provide muscle activity patterns related to differences Provide muscle activity patterns related to differences in the underlying movement task, ambulation at differin the underlying movement task, ambulation at different velocities and cadences on a treadmillent velocities and cadences on a treadmill

IntroductionIntroduction

Electromyography(EMG) data is frequently used in qElectromyography(EMG) data is frequently used in quantitative movement analysis as a measure of the mauantitative movement analysis as a measure of the magnitude and timing of muscle activity during a movegnitude and timing of muscle activity during a movementment

Linear envelope EMG(LEEMG) a processed version Linear envelope EMG(LEEMG) a processed version of the raw EMG from lower extremity muscles are knof the raw EMG from lower extremity muscles are known to vary across cadence and speeds of ambulation,own to vary across cadence and speeds of ambulation, and are related to muscle force output in static length and are related to muscle force output in static length conditionsconditions

Introduction(cont.)Introduction(cont.)

4 neurocomputational approaches(FF/BP,SOM,4 neurocomputational approaches(FF/BP,SOM,FIS and ANFIS) were compared with each othFIS and ANFIS) were compared with each other and to the standard visual analysis of electroer and to the standard visual analysis of electromyographic data from biomechanical analysis.myographic data from biomechanical analysis. FF (Feedforward network)FF (Feedforward network) BP(Back propagation network )BP(Back propagation network ) ANFIS(Adaptive network based fuzzy inference sANFIS(Adaptive network based fuzzy inference s

ystem)ystem)

MethodMethodSixteen healthy college-aged subjects(8 males and 8 femalSixteen healthy college-aged subjects(8 males and 8 females)es)5 lower extremity muscles5 lower extremity muscles Soleus(SOL)Soleus(SOL) Lateral gastrocnemius(LG)Lateral gastrocnemius(LG) Anterior tibialis(AT)Anterior tibialis(AT) Rectus femoris(RF)Rectus femoris(RF) Biceps femoris(BF)Biceps femoris(BF)

9 different stride lengths by combining9 different stride lengths by combining 3 cadences(low,self-selected and high) 3 cadences(low,self-selected and high) 3 velocities(low, self-selected and fast)3 velocities(low, self-selected and fast)

Method(cont.)Method(cont.)

The myoelectric signals detected by the surface electrodes were The myoelectric signals detected by the surface electrodes were preamplified then transmitted via fiber optic cablepreamplified then transmitted via fiber optic cable

The analog MES data were converted to digital form The analog MES data were converted to digital form

Using MatLab4.2.1 process EMG data input vectors.Using MatLab4.2.1 process EMG data input vectors.

The EMG data was subsequently processed into 505 point SOM iThe EMG data was subsequently processed into 505 point SOM input vectors by serial concatenation of the LEEMG data from enput vectors by serial concatenation of the LEEMG data from each of the 5 musclesach of the 5 muscles

The LEEMG dataset was clustered into 9(3*3) or 3(3*1) clusters.The LEEMG dataset was clustered into 9(3*3) or 3(3*1) clusters.

The training set consisted of 90% of the available input data.The training set consisted of 90% of the available input data.

Method(cont.)Method(cont.)

Method(cont.)Method(cont.)

ResultsResults

Total of 2148 input LEEMG vectors from Total of 2148 input LEEMG vectors from all 16 subjects across all 9 conditionsall 16 subjects across all 9 conditions

Results(cont.)Results(cont.)

Results(cont.)Results(cont.)

Results(cont.)Results(cont.)

ConclusionConclusion

Applied within the quantitative movement analysis doApplied within the quantitative movement analysis domain for time-series analysis of the resultant sets of mmain for time-series analysis of the resultant sets of measures.easures.

Usefulness of SOM for analysis within this domain.Usefulness of SOM for analysis within this domain.

Poor correct classification(18%-57%) may indicate thPoor correct classification(18%-57%) may indicate the clustering of the datasets may be more related to ane clustering of the datasets may be more related to another factorother factor

Velocity dependent differences in LEEMG are larger Velocity dependent differences in LEEMG are larger than cadence dependence in tradmill ambulationthan cadence dependence in tradmill ambulation

Future WorkFuture Work

Additional data measures such as mechanical power, Additional data measures such as mechanical power, kinematics and ground reaction forces.kinematics and ground reaction forces.

Use other neurocomputational approaches .Use other neurocomputational approaches .

Personal OpinionPersonal Opinion

Find full paper to see the method more detail.Find full paper to see the method more detail.

To find out other neurocomputational approaches appTo find out other neurocomputational approaches apply to LLEMG dataset.ly to LLEMG dataset.

SOM for time series analysis of SARS dataSOM for time series analysis of SARS data