IEEE J Biomed Health Inform - Supervised hierarchical Bayesian model-based electomyographic control and analysis.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ model(3404) distribut(989) bayesian(671) }
{ model(2341) predict(2261) use(1141) }
{ activ(1138) subject(705) human(624) }
{ motion(1329) object(1292) video(1091) }
{ process(1125) use(805) approach(778) }
{ high(1669) rate(1365) level(1280) }
{ group(2977) signific(1463) compar(1072) }
{ can(981) present(881) function(850) }
{ use(1733) differ(960) four(931) }
{ detect(2391) sensit(1101) algorithm(908) }
{ can(774) often(719) complex(702) }
{ inform(2794) health(2639) internet(1427) }
{ featur(3375) classif(2383) classifi(1994) }
{ search(2224) databas(1162) retriev(909) }
{ perform(1367) use(1326) method(1137) }
{ monitor(1329) mobil(1314) devic(1160) }
{ model(2656) set(1616) predict(1553) }
{ implement(1333) system(1263) develop(1122) }
{ network(2748) neural(1063) input(814) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(984) reconstruct(947) comput(926) }
{ case(1353) use(1143) diagnosi(1136) }
{ system(1050) medic(1026) inform(1018) }
{ studi(1119) effect(1106) posit(819) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
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{ blood(1257) pressur(1144) flow(957) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

This work suggests a supervised hierarchical Bayesian model for surface electromyography (sEMG)-based motion classification and its strategy analysis. The proposed model unifies the optimal feature extraction and classification through probabilistic inference and learning by identifying the latent neural states (LNSs) that govern a collection of sEMG signals. In addition, the inference step provides an approach to identify distinct muscle activation strategies according to sEMG patterns based on LNSs. To validate the model, nine-class classification using four sEMG sensors on the limb motions is tested. The model performance is evaluated with relatively high and low activation levels, generalized classification across subjects and online classification. The model, based on LNSs to capture various motions, is assessed with respect to activation levels, individual subjects and transition during online classification. Our approach cannot only classify sEMG patterns, but also provide the interpretation of sEMG strategic patterns. This work supports the potential of the proposed model for sEMG control-based applications.

Resumo Limpo

work suggest supervis hierarch bayesian model surfac electromyographi semgbas motion classif strategi analysi propos model unifi optim featur extract classif probabilist infer learn identifi latent neural state lnss govern collect semg signal addit infer step provid approach identifi distinct muscl activ strategi accord semg pattern base lnss valid model nineclass classif use four semg sensor limb motion test model perform evalu relat high low activ level general classif across subject onlin classif model base lnss captur various motion assess respect activ level individu subject transit onlin classif approach classifi semg pattern also provid interpret semg strateg pattern work support potenti propos model semg controlbas applic

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