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Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Biomedical Engineering)ORCID-id: 0000-0001-8294-861X
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Losing a limb causes difficulties in our daily life. To regain the ability to live an independent life, artificial limbs have been developed. Hand prostheses belong to a group of artificial limbs that can be controlled by the user through the activity of the remnant muscles above the amputation. Electromyogram (EMG) is one of the sources that can be used for control methods for hand prostheses. Surface EMGs are powerful, non-invasive tools that provide information about neuromuscular activity of the subjected muscle, which has been essential to its use as a source of control for prosthetic limbs. However, the complexity of this signal introduces a big challenge to its applications. EMG pattern recognition to decode different limb movements is an important advancement regarding the control of powered prostheses. It has the potential to enable the control of powered prostheses using the generated EMG by muscular contractions as an input. However, its use has yet to be transitioned into wide clinical use. Different algorithms have been developed in state of the art to decode different movements; however, the challenge still lies in different stages of a successful hand gesture recognition and improvements in these areas could potentially increase the functionality of powered prostheses. This thesis firstly focuses on improving the EMG signal’s quality by proposing novel and advanced filtering techniques. Four efficient approaches (adaptive neuro-fuzzy inference system-wavelet, artificial neural network-wavelet, adaptive subtraction and automated independent component analysis-wavelet) are proposed to improve the filtering process of surface EMG signals and effectively eliminate ECG interferences. Then, the offline performance of different EMG-based recognition algorithms for classifying different hand movements are evaluated with the aim of obtaining new myoelectric control configurations that improves the recognition stage. Afterwards, to gain proper insight on the implementation of myoelectric pattern recognition, a wide range of myoelectric pattern recognition algorithms are investigated in real time. The experimental result on 15 healthy volunteers suggests that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) outperform other classifiers. The real-time investigation illustrates that in addition to the LDA and MLE, multilayer perceptron also outperforms the other algorithms when compared using classification accuracy and completion rate.

Abstract [sv]

Att förlora en extremitet orsakar svårigheter i vår vardag. För att återfå förmågan till ett självständigt liv har artificiella händer och ben utvecklats. Handproteser kan kontrolleras av användaren genom aktiviteten hos återstående muskler ovanför amputationen. Elektromyogram (EMG) är en av de källor som kan användas till kontrollmetoder för handproteser. Yt-EMG är kraftfulla icke-invasiva verktyg som ger information om neuromuskulär aktivitet hos en specifik muskel, vilket är avgörande för dess användning att styra proteser. Komplexiteten hos signalen utgör dock en stor utmaning. EMG-mönsterigenkänning för att avkoda olika handrörelser är ett viktigt framsteg när det gäller kontroll av motoriserade proteser. Denna metod har potential att möjliggöra styrning av proteser genom att använda EMG-signalerna från muskelkontraktioner som insignal. Denna metod har dock ännu inte fått någon stor klinisk spridning. Olika algoritmer har utvecklats inom området för att avkoda olika rörelser; men utmaningen att identifiera olika handrörelser i olika faser kvarstår, och förbättringar inom dessa områden kan komma att öka funktionaliteten hos motoriserade proteser. Denna avhandling undersöker flera aspekter kring detta, först hur kvaliteten hos EMG-signaler kan förbättras genom att nya och avancerade filtreringstekniker. Fyra effektiva tillvägagångssätt (adaptivt neuro-fuzzy inference system-wavelet, artificiellt neuralt nätverk-wavelet, adaptiv subtraktion och automatiserad oberoende komponentanalys-wavelet) presenteras för att förbättra filtreringsprocessen för yt-EMG-signaler och effektivt eliminera EKG-störningar. Även offline-prestanda för olika EMG-baserade igenkänningsalgoritmer undersöks, däribland förmågan att klassificera olika handrörelser med sikte på att erhålla nya myoelektriska kontrollkonfigurationer som förbättrar igenkänningen. För att undersöka hur väl de myoelektriska mönsterigenkänningssalgoritmerna fungerar i verkliga situationer, har ett brett spektrum av myoelektriska algoritmer undersökts i realtid. 15 friska frivilliga försökspersoner har använt systemet och resultaten tyder på att linjär diskriminantanalys (LDA) och maximal sannolikhetsbedömning (MLE) är bättre än de andra klassificeringsmetoderna. Realtidsundersökningen visar också att förutom LDA och MLE, så är algoritmerna med flerlagersperception bättre än de övriga algoritmerna då de jämförs med avseende på klassificeringsnoggrannhet och beräkningshastighet.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University , 2019.
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 283
HSV kategori
Forskningsprogram
elektronik
Identifikatorer
URN: urn:nbn:se:mdh:diva-41669ISBN: 978-91-7485-418-3 (tryckt)OAI: oai:DiVA.org:mdh-41669DiVA, id: diva2:1271318
Disputas
2019-02-22, Delta, Mälardalens högskola, Västerås, 09:30 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-12-17 Laget: 2018-12-17 Sist oppdatert: 2019-01-02bibliografisk kontrollert
Delarbeid
1. A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet
Åpne denne publikasjonen i ny fane eller vindu >>A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet
2016 (engelsk)Inngår i: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, Vol. 26, s. 52-59Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-32779 (URN)10.1016/j.jelekin.2015.11.003 (DOI)000370187700008 ()26643795 (PubMedID)2-s2.0-84960226613 (Scopus ID)
Prosjekter
ESS-H - Embedded Sensor Systems for Health Research Profile
Tilgjengelig fra: 2016-08-25 Laget: 2016-08-24 Sist oppdatert: 2019-06-25bibliografisk kontrollert
2. A Combination Method for Electrocardiogram Rejection from Surface Electromyogram
Åpne denne publikasjonen i ny fane eller vindu >>A Combination Method for Electrocardiogram Rejection from Surface Electromyogram
2014 (engelsk)Inngår i: Open Biomedical Engineering Journal, ISSN 1874-1207, E-ISSN 1874-1207, Vol. 8, nr 1, s. 13-19Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.

sted, utgiver, år, opplag, sider
Netherlands: Bentham Science Publishers, 2014
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-27167 (URN)10.2174/1874120701408010013 (DOI)2-s2.0-84899673853 (Scopus ID)
Prosjekter
ESS-H - Embedded Sensor Systems for Health Research Profile
Tilgjengelig fra: 2014-12-22 Laget: 2014-12-19 Sist oppdatert: 2018-12-17bibliografisk kontrollert
3. Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique
Åpne denne publikasjonen i ny fane eller vindu >>Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique
2014 (engelsk)Inngår i: Biomedical Physics and Engineering, ISSN 2251-7200, Vol. 4, nr 1, s. 33-38Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: The electrocardiogram artifact is a major contamination in the electromyogram signals when electromyogram signal is recorded from upper trunk muscles and because of that the contaminated electromyogram is not useful. Objective: Removing electrocardiogram contamination from electromyogram signals. Methods: In this paper, the clean electromyogram signal, electrocardiogram artifact and electrocardiogram signal were recorded from leg muscles, the pectoralis major muscle of the left side and V4, respectively. After the pre-processing, contaminated electromyogram signal is simulated with a combination of clean electromyogram and electrocardiogram artifact. Then, contaminated electromyogram is cleaned using adaptive subtraction method. This method contains some steps; (1) QRS detection, (2) formation of electrocardiogram template by averaging the electrocardiogram complexes, (3) using low pass filter to remove undesirable artifacts, (4) subtraction. Results: Performance of our method is evaluated using qualitative criteria, power spectrum density and coherence and quantitative criteria signal to noise ratio, relative error and cross correlation. The result of signal to noise ratio, relative error and cross correlation is equal to 10.493, 0.04 and %97 respectively. Finally, there is a comparison between proposed method and some existing methods. Conclusion: The result indicates that adaptive subtraction method is somewhat effective to remove electrocardiogram artifact from contaminated electromyogram signal and has an acceptable result.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-27871 (URN)25505766 (PubMedID)
Tilgjengelig fra: 2015-04-22 Laget: 2015-04-22 Sist oppdatert: 2018-12-17bibliografisk kontrollert
4. ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA
Åpne denne publikasjonen i ny fane eller vindu >>ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA
2015 (engelsk)Inngår i: Studies in Health Technology and Informatics, Volume 211, 2015, s. 91-97Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.

Serie
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 211
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-27873 (URN)10.3233/978-1-61499-516-6-91 (DOI)000455821300006 ()2-s2.0-84939229104 (Scopus ID)978-1-61499-515-9 (ISBN)
Konferanse
12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, Västerås, Sweden, June 2-4, 2015
Tilgjengelig fra: 2015-04-22 Laget: 2015-04-22 Sist oppdatert: 2019-06-18bibliografisk kontrollert
5. Evaluation of surface EMG-based recognition algorithms for decoding hand movements
Åpne denne publikasjonen i ny fane eller vindu >>Evaluation of surface EMG-based recognition algorithms for decoding hand movements
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Emneord
Electromyography; Feature extraction; Myoelectric pattern recognition; Dimensionality reduction; Classification
HSV kategori
Forskningsprogram
elektronik
Identifikatorer
urn:nbn:se:mdh:diva-41470 (URN)
Tilgjengelig fra: 2018-11-30 Laget: 2018-11-30 Sist oppdatert: 2018-12-17bibliografisk kontrollert
6. Real-time and offline evaluation of myoelectric pattern recognition for upper limb prosthesis control
Åpne denne publikasjonen i ny fane eller vindu >>Real-time and offline evaluation of myoelectric pattern recognition for upper limb prosthesis control
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-41471 (URN)
Tilgjengelig fra: 2018-11-30 Laget: 2018-11-30 Sist oppdatert: 2018-12-17bibliografisk kontrollert

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