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[{"key": "dc.contributor.advisor", "value": "Linnamo, Vesa", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Piitulainen, Harri", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Koskinen, Timo", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2019-06-27T11:30:20Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2019-06-27T11:30:20Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2019", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/64884", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Aktiopotentiaaliksi kutsuttu j\u00e4nnitteen muutos ajan funktiona on tunnettu bios\u00e4hk\u00f6inen impulssi.\nKirjallisuuskatsauksessa pyrittiin l\u00f6yt\u00e4m\u00e4\u00e4n ne motorisen yksik\u00f6n aktiopotentiaalin voimakkuuteen,\nmuotoon ja kestoon vaikuttavat tekij\u00e4t, jotka selitt\u00e4isiv\u00e4t riitt\u00e4v\u00e4n luotettavasti erot kahden tai\nuseamman motorisen yksik\u00f6n v\u00e4lill\u00e4. T\u00e4ll\u00f6in yksik\u00f6t olisivat identifioitavissa sen tuottaman\nyksil\u00f6llisen impulssin muodon eli j\u00e4nnitteen aikafunktion perusteella.\nKoska kaikkien aktiivisten motoristen yksik\u00f6iden tuottamat bios\u00e4hk\u00f6iset impulssit tai oikeammin\ns\u00e4hk\u00f6kent\u00e4t summautuvat mittaelektrodissa, motoristen yksik\u00f6iden erottaminen (so.\ndekompositiointi) t\u00e4ytyy suorittaa sulautetulla ohjelmoinnilla, digitaalisen signaalink\u00e4sittelyn ja\nmatemaattisten algoritmien avulla. T\u00e4m\u00e4n tutkielman yhteydess\u00e4 luotiin algoritmi, jolla\ndifferentiaalinen sEMG-signaali (surface ElectroMyoGram) purettiin erillisiin MUAP-jonoihin\n(Motor Unit Action Potential) ja jolla ne analysoitiin automaattisesti ja nopeasti, tavallisella\nkannettavalla tietokoneella ja MATlab-sovelluksella.\nTutkimuksessa p\u00e4\u00e4dyttiin kokeilemaan simuloitua HDsEMG-signaalia (High Density surface EMG)\nbipolaarisen pinta-EMG-signaalin sijaan. N\u00e4ytteet simuloivat monikanavaisen 10x9-matriisianturin\ntuloksia isometrisest\u00e4 10% MVC-suorituksesta (Maximum Voluntary Control). Tutkimusalgoritmi\nmuodosti differentiaalisignaalin, josta luokittelufunktio erotti motoriset yksik\u00f6t.\nYhten\u00e4 ongelmana oli m\u00e4\u00e4ritell\u00e4, kuinka moneen ryhm\u00e4\u00e4n aktiopotentiaalit tulee jakaa.\nValitettavasti t\u00e4m\u00e4n ongelman ratkaisuun ei ole olemassa suoraa menetelm\u00e4\u00e4. Ratkaisua t\u00e4ytyy\nhakea ep\u00e4suorasti, k\u00e4ytt\u00e4en apuna soveltuvia tunnuslukuja. T\u00e4m\u00e4n tutkimusaineiston kohdalla\np\u00e4\u00e4dyttiin 25 klusteriin.\nTutkielman tulosten perusteella lupaavin tutkituista menetelmist\u00e4 oli K-medoids-klusterointi\nneli\u00f6llisell\u00e4 eukleidisell\u00e4 (Squared Euclidean) samankaltaisuusmitalla. T\u00e4ss\u00e4 funktiossa jokainen\nmedoidi eli klusterikeskipiste edustaa ryhm\u00e4n keskiarvoa. Alun perin kehitetty algoritmi oli varsin\nhelppo mukauttaa noudattamaan t\u00e4t\u00e4 luokittelumenetelm\u00e4\u00e4. Valitettavasti yhdenk\u00e4\u00e4n\nklusterointimenetelm\u00e4n luotettavuusrajoja tai muitakaan laadullisia parametrej\u00e4 ei pystytty t\u00e4ss\u00e4\ntutkimuksessa m\u00e4\u00e4rittelem\u00e4\u00e4n, koska yksi\u00e4k\u00e4\u00e4n soveltuvia referenssituloksia ei saatu aineistoon\neiv\u00e4tk\u00e4 simuloitujen n\u00e4ytteiden syttymisajat korreloineet differentiaalisen bipolaarisignaalin kanssa.\nSelitys j\u00e4lkimm\u00e4iseen voi olla, ett\u00e4 matriisianturin keskelle syotetyt AP-aallot osuvat molempiin\nbipolaarisiin antureihin yht\u00e4aikaa eik\u00e4 differentiaalia synny - mutta t\u00e4m\u00e4n lopullinen todentaminen\nvaatisi hieman jatkotutkimusta.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "So-called Action Potential in neuron is a temporal change in voltage, and well known as a\nbioelectric impulse. Starting point of the literature review was to find all factors which induce\namplitude, shape and duration of a certain Motor Unit discharge, and to define differences between\ntwo or more Action Potentials. Then Motor Unit Action Potential train decomposition could be done\nby comparing the temporal voltage change, i.e. by comparing the shape of an action potential.\nBecause all the Motor Unit Action Potentials, that are active, are merged in a detection electrod, i.e.\nan electrode detects the sum of electric fields of electric charges. Due to the merged MUAP trains,\nthe decomposition has to be performed by embedded software by means of Digital Signal\nProcessing and mathematical algorithms. Novel decomposition algorithm was created during the\nstudy. It decomposes differential sEMG signal into separate MUAP trains and computes variables.\nAll this takes place fast and autonomously without human operator, just a common laptop and\nMATlab application is needed.\nInstead of physiological bipolar sEMG signal, the study was done to simulated HDsEMG signals,\nwhich were provided by Ales Holobar and Harri Piitulainen. The signals simulated high density\nmultichannel 10x9 matrix like results from isometric 10% MVC performance. Differential signals\nwere reconstructed and further decomposed MUAP trains by the study algorithm.\nOne problem was to determine, how many groups of action potentials should be clustered.\nUnfortunately, there is no direct method for solving this problem. The solution must be applied\nindirectly, using the appropriate parameters and the key figures. For these signals, 25 clusters were\nfound.\nBased on the results, the most promising method studied was k-medoids clustering function that\nuses Squared Euclidean similarity measure. In this method, each medoid represent averages of the\ncluster. The algorithm originally developed was quite easy to adapt to this classification method.\nUnfortunately, none of the reliability limits of the clustering method or other qualitative parameters\ncould be defined in this study, because none relevant reference results were found for the study and\nthe firing times of the simulated samples did not correlate with the differential bipolar signal. The\nexplanation for the latter may be that the AP waves fed to the center of the matrix sensor arrive at\nboth bipolar sensors simultaneously and no differential occurs - but the verification would require\nsome further research.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Miia Hakanen (mihakane@jyu.fi) on 2019-06-27T11:30:20Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2019-06-27T11:30:20Z (GMT). No. of bitstreams: 0\n Previous issue date: 2019", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "71", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "aktiopotentiaali", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "alfamotoneuroni", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "motorinen yksikk\u00f6", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "MUAP", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "sEMG", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "HDsEMG", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "dekompositiointi", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "tiedon luokittelu", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "k-means", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "k-medoids", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "wavelet-muunnos", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "MATlab", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Motoristen yksik\u00f6iden erottelu matemaattisilla luokittelumenetelmill\u00e4 differentiaalisesta elektromyografiasta", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201906273487", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", 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"dc.subject.discipline", "value": "Biomekaniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Biomechanics", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "5012", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": 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