EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach
The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification pr...
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| Published in | Medical & biological engineering & computing Vol. 60; no. 6; pp. 1659 - 1673 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2022
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0140-0118 1741-0444 1741-0444 |
| DOI | 10.1007/s11517-022-02559-3 |
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| Abstract | The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments.
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| AbstractList | Abstract The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments. The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments. Graphical abstract The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments. The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments.The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments. |
| Author | Guarneri, Gabriella Guiotto, Annamaria Avogaro, Angelo Spolaor, Fabiola Sawacha, Zimi Piatkowska, Weronika |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35428958$$D View this record in MEDLINE/PubMed |
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| Keywords | Diabetic neuropathies Stair climbing Electromyography Clustering Diabetes mellitus Gait analysis |
| Language | English |
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IEEE Trans Biomed Eng 45:287–299. https://doi.org/10.1109/10.661154 BoultonAJMThe diabetic foot Medicine201543333710.1016/j.mpmed.2014.10.006 BasmajianJVBlumensteinRElectrode placement in EMG biofeedback1980Williams & Wilkins AkashiPMHSaccoICNWatariRHennigEThe effect of diabetic neuropathy and previous foot ulceration in EMG and ground reaction forces during gaitClin Biomech20082358459210.1016/j.clinbiomech.2007.11.015 NeneAMayagoitiaRVeltinkPAssessment of rectus femoris function during initial swing phaseGait Posture19999191:STN:280:DC%2BD3c%2FksFWmsw%3D%3D10.1016/s0966-6362(98)00042-310575064 SaccoICNAmadioACInfluence of the diabetic neuropathy on the behavior of electromyographic and sensorial responses in treadmill gaitClin Biomech (Bristol, Avon)2003184264341:STN:280:DC%2BD3s3ktFSltQ%3D%3D10.1016/s0268-0033(03)00043-3 McFadyenBJWinterDAAn integrated biomechanical analysis of normal stair ascent and descentJ Biomech1988217337441:STN:280:DyaL1M%2FjsVegsA%3D%3D10.1016/0021-9290(88)90282-53182877 HandsakerJCBrownSJBowlingFLCooperGMaganarisCNBoultonAJMReevesNDContributory factors to unsteadiness during walking up and down stairs in patients with diabetic peripheral neuropathyDiabetes Care2014373047305310.2337/dc14-095525315208 Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson: Addison Wesley, Boston WhiteSGMcNairPJAbdominal and erector spinae muscle activity during gait: the use of cluster analysis to identify patterns of activityClin Biomech (Bristol, Avon)20021717718410.1016/s0268-0033(02)00007-4 MulroySGronleyJWeissWNewsamCPerryJUse of cluster analysis for gait pattern classification of patients in the early and late recovery phases following strokeGait Posture20031811412510.1016/s0966-6362(02)00165-012855307 SpolaorFSawachaZGuarneriGDel DinSAvogaroACobelliCAltered EMG patterns in diabetic neuropathic and not neuropathic patients during step ascending and descendingJ Electromyogr Kinesiol201631323910.1016/j.jelekin.2016.08.00727632533 LeardiniASawachaZPaoliniGIngrossoSNativoRBenedettiMGA new anatomically based protocol for gait analysis in childrenGait Posture20072656057110.1016/j.gaitpost.2006.12.01817291764 SawachaZGuarneriGAvogaroACobelliCA new classification of diabetic gait pattern based on cluster analysis of biomechanical dataJ Diabetes Sci Technol201041127113810.1177/193229681000400511 MuellerMJSinacoreDRHoogstrateSDalyLHip and ankle walking strategies: effect on peak plantar pressures and implications for neuropathic ulcerationArch Phys Med Rehabil199475119612001:STN:280:DyaK2M%2FntVOhsw%3D%3D10.1016/0003-9993(94)90004-37979928 Blanc Y, Dimanico U (2010) Electrode placement in surface electromyography (sEMG) “minimal crosstalk area” (MCA). The Open Rehabilitation Journal 3 JosephJWatsonRTelemetering electromyography of muscles used in walking up and down stairsJ Bone Joint Surg Br1967497747801:STN:280:DyaF1c%2FptFCjsQ%3D%3D10.1302/0301-620X.49B4.774 McLachlanGCluster analysis and related techniques in medical researchStat Methods Med Res1992127481:STN:280:DyaK2c%2FosVCmtQ%3D%3D10.1177/0962280292001001031341650 Clustering (2009) Making sense of data II. John Wiley & Sons, Ltd, pp 67–110. https://doi.org/10.1002/9780470417409.ch3 FeldmanELStevensMJThomasPKBrownMBCanalNGreeneDAA practical two-step quantitative clinical and electrophysiological assessment for the diagnosis and staging of diabetic neuropathyDiabetes Care199417128112891:STN:280:DyaK2M7itFWmtw%3D%3D10.2337/diacare.17.11.12817821168 MalikOASenanayakeSMNAZaheerDAn intelligent recovery progress evaluation system for ACL reconstructed subjects using integrated 3-D kinematics and EMG featuresIEEE J Biomed Health Inform20151945346310.1109/JBHI.2014.232040824801517 Benedetti MG, Agostini V, Knaflitz M, Bonato P (2012) Muscle activation patterns during level walking and stair ambulation, applications of EMG in clinical and sports medicine. IntechOpen. https://doi.org/10.5772/25792 RoglicGOrganizationWHGlobal report on diabetes2016Geneva, SwitzerlandWorld Health Organization ChenJJShiaviRTemporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studiesIEEE Trans Biomed Eng1990372953021:STN:280:DyaK3c3ivV2rtg%3D%3D10.1109/10.523302184121 SawachaZGabriellaGCristoferiGGuiottoAAvogaroACobelliCDiabetic gait and posture abnormalities: a biomechanical investigation through three dimensional gait analysisClin Biomech (Bristol, Avon)20092472272810.1016/j.clinbiomech.2009.07.007 VinikAIErbasTDiabetic autonomic neuropathyHandb Clin Neurol201311727929410.1016/B978-0-444-53491-0.00022-524095132 OnoderaANGomesAAPripasDMezzaraneRASaccoICNLower limb electromygraphy and kinematics of neuropathic diabetic patients during real-life activities: Stair negotiationMuscle Nerve20114426927710.1002/mus.2207221698651 LeeSJHidlerJBiomechanics of overground vs. treadmill walking in healthy individualsJ Appl Physiol200810474775510.1152/japplphysiol.01380.200618048582 MatosMMendesRSilvaABSousaNPhysical activity and exercise on diabetic foot related outcomes: a systematic reviewDiabetes Res Clin Pract2018139819010.1016/j.diabres.2018.02.02029477503 MayfieldJAReiberGESandersLJJanisseDPogachLMAssociationADPreventive foot care in diabetesDiabetes Care200427Suppl 1S636410.2337/diacare.27.2007.s6314693928 AntonelliDBaralisEBrunoGCerquitelliTChiusanoSMahotoNAnalysis of diabetic patients through their examination historyExpert Syst Appl2013404672467810.1016/j.eswa.2013.02.006 SawachaZSartorCDYiLCGuiottoASpolaorFSaccoICNClustering classification of diabetic walking abnormalities: a new approach taking into account intralimb coordination patternsGait Posture202079334010.1016/j.gaitpost.2020.03.01632334348 RissanenSMKankaanpääMMeigalATarvainenMPNuutinenJTarkkaIMAiraksinenOKarjalainenPASurface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysisMed Biol Eng Comput20084684985810.1007/s11517-008-0369-018633662 KwonO-YMinorSDMalufKSMuellerMJComparison of muscle activity during walking in subjects with and without diabetic neuropathyGait Posture20031810511310.1016/s0966-6362(02)00166-212855306 SawachaZSpolaorFGuarneriGContessaPCarraroEVenturinAAvogaroACobelliCAbnormal muscle activation during gait in diabetes patients with and without neuropathyGait Posture20123510110510.1016/j.gaitpost.2011.08.01622098824 WatelainEBarbierFAllardPThevenonAAnguéJCGait pattern classification of healthy elderly men based on biomechanical dataArch Phys Med Rehabil2000815795861:STN:280:DC%2BD3c3msFektw%3D%3D10.1016/s0003-9993(00)90038-810807095 Hair JF, Black WC, Babin BJ, Anderson RE (2014) Multivariate data analysis, 7th edn. Pearson Education, Upper Saddle River. Warren LiaoTClustering of time series data—a surveyPattern Recogn2005381857187410.1016/j.patcog.2005.01.025 WhitleyEBallJStatistics review 4: sample size calculationsCrit Care20026433534110.1186/cc152112225610137461 SaccoICNPiconAPMacedoDOButuganMKWatariRSartorCDAlterations in the lower limb joint moments precede the peripheral neuropathy diagnosis in diabetes patientsDiabetes Technol Ther20151740541210.1089/dia.2014.028425664904 SJ Lee (2559_CR30) 2008; 104 G McLachlan (2559_CR21) 1992; 1 ICN Sacco (2559_CR36) 2015; 17 AP Picon (2559_CR35) 2012; 16 2559_CR40 M Matos (2559_CR44) 2018; 139 2559_CR28 Z Sawacha (2559_CR6) 2012; 35 EB Simonsen (2559_CR34) 2012; 34 JC Handsaker (2559_CR8) 2014; 37 Z Sawacha (2559_CR11) 2020; 79 J Joseph (2559_CR23) 1967; 49 2559_CR26 D Antonelli (2559_CR14) 2013; 40 JJ Chen (2559_CR18) 1990; 37 AI Vinik (2559_CR2) 2013; 117 SG White (2559_CR43) 2002; 17 SM Rissanen (2559_CR20) 2008; 46 Z Sawacha (2559_CR15) 2010; 4 A Leardini (2559_CR29) 2007; 26 2559_CR32 2559_CR33 Z Sawacha (2559_CR37) 2009; 24 A Nene (2559_CR39) 1999; 9 ICN Sacco (2559_CR10) 2003; 18 CD Katsis (2559_CR41) 2006; 37 2559_CR12 JV Basmajian (2559_CR27) 1980 2559_CR13 OA Malik (2559_CR19) 2015; 19 E Whitley (2559_CR22) 2002; 6 E Watelain (2559_CR17) 2000; 81 MJ Mueller (2559_CR38) 1994; 75 AN Onodera (2559_CR7) 2011; 44 S Mulroy (2559_CR42) 2003; 18 (2559_CR1) 2016 O-Y Kwon (2559_CR4) 2003; 18 AJM Boulton (2559_CR3) 2015; 43 T Warren Liao (2559_CR16) 2005; 38 BJ McFadyen (2559_CR31) 1988; 21 PMH Akashi (2559_CR5) 2008; 23 EL Feldman (2559_CR24) 1994; 17 F Spolaor (2559_CR9) 2016; 31 JA Mayfield (2559_CR25) 2004; 27 |
| References_xml | – reference: BoultonAJMThe diabetic foot Medicine201543333710.1016/j.mpmed.2014.10.006 – reference: PiconAPSartorCDRoveriMIPássaroACOrtegaNRSaccoICNDiabetic patients with and without peripheral neuropathy reveal different hip and ankle biomechanical strategies during stair descentBraz J Phys Ther20121652853410.1590/S1413-35552012005000048 – reference: SawachaZGabriellaGCristoferiGGuiottoAAvogaroACobelliCDiabetic gait and posture abnormalities: a biomechanical investigation through three dimensional gait analysisClin Biomech (Bristol, Avon)20092472272810.1016/j.clinbiomech.2009.07.007 – reference: SaccoICNPiconAPMacedoDOButuganMKWatariRSartorCDAlterations in the lower limb joint moments precede the peripheral neuropathy diagnosis in diabetes patientsDiabetes Technol Ther20151740541210.1089/dia.2014.028425664904 – reference: MatosMMendesRSilvaABSousaNPhysical activity and exercise on diabetic foot related outcomes: a systematic reviewDiabetes Res Clin Pract2018139819010.1016/j.diabres.2018.02.02029477503 – reference: KwonO-YMinorSDMalufKSMuellerMJComparison of muscle activity during walking in subjects with and without diabetic neuropathyGait Posture20031810511310.1016/s0966-6362(02)00166-212855306 – reference: AkashiPMHSaccoICNWatariRHennigEThe effect of diabetic neuropathy and previous foot ulceration in EMG and ground reaction forces during gaitClin Biomech20082358459210.1016/j.clinbiomech.2007.11.015 – reference: MuellerMJSinacoreDRHoogstrateSDalyLHip and ankle walking strategies: effect on peak plantar pressures and implications for neuropathic ulcerationArch Phys Med Rehabil199475119612001:STN:280:DyaK2M%2FntVOhsw%3D%3D10.1016/0003-9993(94)90004-37979928 – reference: OnoderaANGomesAAPripasDMezzaraneRASaccoICNLower limb electromygraphy and kinematics of neuropathic diabetic patients during real-life activities: Stair negotiationMuscle Nerve20114426927710.1002/mus.2207221698651 – reference: SawachaZGuarneriGAvogaroACobelliCA new classification of diabetic gait pattern based on cluster analysis of biomechanical dataJ Diabetes Sci Technol201041127113810.1177/193229681000400511 – reference: Hair JF, Black WC, Babin BJ, Anderson RE (2014) Multivariate data analysis, 7th edn. Pearson Education, Upper Saddle River. – reference: MalikOASenanayakeSMNAZaheerDAn intelligent recovery progress evaluation system for ACL reconstructed subjects using integrated 3-D kinematics and EMG featuresIEEE J Biomed Health Inform20151945346310.1109/JBHI.2014.232040824801517 – reference: JosephJWatsonRTelemetering electromyography of muscles used in walking up and down stairsJ Bone Joint Surg Br1967497747801:STN:280:DyaF1c%2FptFCjsQ%3D%3D10.1302/0301-620X.49B4.774 – reference: LeardiniASawachaZPaoliniGIngrossoSNativoRBenedettiMGA new anatomically based protocol for gait analysis in childrenGait Posture20072656057110.1016/j.gaitpost.2006.12.01817291764 – reference: KatsisCDGoletsisYLikasAFotiadisDISarmasIA novel method for automated EMG decomposition and MUAP classificationArtif Intell Med20063755641:STN:280:DC%2BD28zis1Gmtg%3D%3D10.1016/j.artmed.2005.09.00216377160 – reference: WhitleyEBallJStatistics review 4: sample size calculationsCrit Care20026433534110.1186/cc152112225610137461 – reference: MulroySGronleyJWeissWNewsamCPerryJUse of cluster analysis for gait pattern classification of patients in the early and late recovery phases following strokeGait Posture20031811412510.1016/s0966-6362(02)00165-012855307 – reference: SawachaZSpolaorFGuarneriGContessaPCarraroEVenturinAAvogaroACobelliCAbnormal muscle activation during gait in diabetes patients with and without neuropathyGait Posture20123510110510.1016/j.gaitpost.2011.08.01622098824 – reference: Benedetti MG, Agostini V, Knaflitz M, Bonato P (2012) Muscle activation patterns during level walking and stair ambulation, applications of EMG in clinical and sports medicine. IntechOpen. https://doi.org/10.5772/25792 – reference: NeneAMayagoitiaRVeltinkPAssessment of rectus femoris function during initial swing phaseGait Posture19999191:STN:280:DC%2BD3c%2FksFWmsw%3D%3D10.1016/s0966-6362(98)00042-310575064 – reference: AntonelliDBaralisEBrunoGCerquitelliTChiusanoSMahotoNAnalysis of diabetic patients through their examination historyExpert Syst Appl2013404672467810.1016/j.eswa.2013.02.006 – reference: RoglicGOrganizationWHGlobal report on diabetes2016Geneva, SwitzerlandWorld Health Organization – reference: VinikAIErbasTDiabetic autonomic neuropathyHandb Clin Neurol201311727929410.1016/B978-0-444-53491-0.00022-524095132 – reference: Clustering (2009) Making sense of data II. John Wiley & Sons, Ltd, pp 67–110. https://doi.org/10.1002/9780470417409.ch3 – reference: Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson: Addison Wesley, Boston – reference: WatelainEBarbierFAllardPThevenonAAnguéJCGait pattern classification of healthy elderly men based on biomechanical dataArch Phys Med Rehabil2000815795861:STN:280:DC%2BD3c3msFektw%3D%3D10.1016/s0003-9993(00)90038-810807095 – reference: RissanenSMKankaanpääMMeigalATarvainenMPNuutinenJTarkkaIMAiraksinenOKarjalainenPASurface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysisMed Biol Eng Comput20084684985810.1007/s11517-008-0369-018633662 – reference: SaccoICNAmadioACInfluence of the diabetic neuropathy on the behavior of electromyographic and sensorial responses in treadmill gaitClin Biomech (Bristol, Avon)2003184264341:STN:280:DC%2BD3s3ktFSltQ%3D%3D10.1016/s0268-0033(03)00043-3 – reference: FeldmanELStevensMJThomasPKBrownMBCanalNGreeneDAA practical two-step quantitative clinical and electrophysiological assessment for the diagnosis and staging of diabetic neuropathyDiabetes Care199417128112891:STN:280:DyaK2M7itFWmtw%3D%3D10.2337/diacare.17.11.12817821168 – reference: BasmajianJVBlumensteinRElectrode placement in EMG biofeedback1980Williams & Wilkins – reference: Farjo J, Assi RA, Masri W, Zaraket F (2013) Does principal component analysis improve cluster-based analysis?, in: 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops. Presented at the 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops. 400–403. https://doi.org/10.1109/ICSTW.2013.52 – reference: MayfieldJAReiberGESandersLJJanisseDPogachLMAssociationADPreventive foot care in diabetesDiabetes Care200427Suppl 1S636410.2337/diacare.27.2007.s6314693928 – reference: Blanc Y, Dimanico U (2010) Electrode placement in surface electromyography (sEMG) “minimal crosstalk area” (MCA). The Open Rehabilitation Journal 3 – reference: HandsakerJCBrownSJBowlingFLCooperGMaganarisCNBoultonAJMReevesNDContributory factors to unsteadiness during walking up and down stairs in patients with diabetic peripheral neuropathyDiabetes Care2014373047305310.2337/dc14-095525315208 – reference: SpolaorFSawachaZGuarneriGDel DinSAvogaroACobelliCAltered EMG patterns in diabetic neuropathic and not neuropathic patients during step ascending and descendingJ Electromyogr Kinesiol201631323910.1016/j.jelekin.2016.08.00727632533 – reference: ChenJJShiaviRTemporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studiesIEEE Trans Biomed Eng1990372953021:STN:280:DyaK3c3ivV2rtg%3D%3D10.1109/10.523302184121 – reference: LeeSJHidlerJBiomechanics of overground vs. treadmill walking in healthy individualsJ Appl Physiol200810474775510.1152/japplphysiol.01380.200618048582 – reference: McFadyenBJWinterDAAn integrated biomechanical analysis of normal stair ascent and descentJ Biomech1988217337441:STN:280:DyaL1M%2FjsVegsA%3D%3D10.1016/0021-9290(88)90282-53182877 – reference: WhiteSGMcNairPJAbdominal and erector spinae muscle activity during gait: the use of cluster analysis to identify patterns of activityClin Biomech (Bristol, Avon)20021717718410.1016/s0268-0033(02)00007-4 – reference: Warren LiaoTClustering of time series data—a surveyPattern Recogn2005381857187410.1016/j.patcog.2005.01.025 – reference: SimonsenEBAlkjærTThe variability problem of normal human walkingMed Eng Phys20123421922410.1016/j.medengphy.2011.07.01321852174 – reference: McLachlanGCluster analysis and related techniques in medical researchStat Methods Med Res1992127481:STN:280:DyaK2c%2FosVCmtQ%3D%3D10.1177/0962280292001001031341650 – reference: Bonato P, D’Alessio T, Knaflitz M (1998) A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. 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| Snippet | The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with... Abstract The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic... |
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| SubjectTerms | Algorithms Ankle Bioengineering Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Cardiovascular disease Climbing Cluster analysis Clustering Computer Applications Deactivation Diabetes Diabetes mellitus Diabetic neuropathy Electromyography Gait Human Physiology Hypotheses Imaging Metabolic disorders Muscle function Muscles Original Original Article Parameters Radiology Rehabilitation Ulcers |
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| Title | EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach |
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