publication . Article . Other literature type . 2017

Comparison of six electromyography acquisition setups on hand movement classification tasks.

Stefano Pizzolato; Luca Tagliapietra; Matteo Cognolato; Monica Reggiani; Henning Müller; Manfredo Atzori;
Open Access English
  • Published: 12 Oct 2017 Journal: PLoS ONE, volume 12, issue 10 (issn: 1932-6203, Copyright policy)
  • Publisher: Public Library of Science (PLoS)
  • Country: Switzerland
Abstract
Hand prostheses controlled by surface electromyography are promising due to the non-invasive approach and the control capabilities offered by machine learning. Nevertheless, dexterous prostheses are still scarcely spread due to control difficulties, low robustness and often prohibitive costs. Several sEMG acquisition setups are now available, ranging in terms of costs between a few hundred and several thousand dollars. The objective of this paper is the relative comparison of six acquisition setups on an identical hand movement classification task, in order to help the researchers to choose the proper acquisition setup for their requirements. The acquisition set...
Subjects
free text keywords: Informatique, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine, Research Article, Computer and Information Sciences, Biology and Life Sciences, Anatomy, Musculoskeletal System, Limbs (Anatomy), Arms, Hands, Medicine and Health Sciences, Biotechnology, Medical Devices and Equipment, Assistive Technologies, Prosthetics, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Membrane Electrophysiology, Electrode Recording, Muscle Electrophysiology, Machine Learning, Physical Sciences, Mathematics, Applied Mathematics, Algorithms, Machine Learning Algorithms, Simulation and Modeling, lcsh:Medicine, lcsh:R, lcsh:Science, lcsh:Q, Data acquisition, Support vector machine, Ranging, Accelerometer, Data set, Robustness (computer science), Feature extraction, Computer science, Computer vision, Artificial intelligence, business.industry, business, Electromyography, medicine.diagnostic_test, medicine
44 references, page 1 of 3

1 Finley FR, Wirta RW. Myocoder studies of multiple myopotential resp onse. Archives of Physical Medicine and Rehabilitation. 1967;48(11):598–601. 6060789 [PubMed]

2 Farina D, Member S, Jiang N, Rehbaum H, Member S. The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges. 2014;22(4):797–809.

3 Micera S, Carpaneto J, Raspopovic S. Control of hand prostheses using peripheral information. IEEE Reviews in Biomedical Engineering. 2010;3:48–68. 10.1109/RBME.2010.2085429 22275201 [OpenAIRE] [PubMed] [DOI]

4 Fougner A, Stavdahl Oy, Kyberd PJ, Losier YG, Parker PA. Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control–A Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2012;20(5):663–677. 10.1109/TNSRE.2012.2196711 22665514 [OpenAIRE] [PubMed] [DOI]

5 Atzori M, Müller H. Control Capabilities of Myoelectric Robotic Prostheses by Hand Amputees: A Scientific Research and Market Overview. Frontiers in Systems Neuroscience. 2015;9(162). 10.3389/fnsys.2015.00162 26648850 [OpenAIRE] [PubMed] [DOI]

6 Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. The Journal of Rehabilitation Research and Development. 2011;48(6):643 10.1682/JRRD.2010.09.0177 21938652 [OpenAIRE] [PubMed] [DOI]

7 Hargrove L, Losier Y, Lock B, Englehart K, Hudgins B. A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE. IEEE; 2007. p. 4842–4845.

8 Atzori M, Cognolato M, Müller H. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands. Frontiers in Neurorobotics. 2016;10 10.3389/fnbot.2016.00009 27656140 [OpenAIRE] [PubMed] [DOI]

9 Müller H, Kalpathy-Cramer J, Eggel I, Bedrick S, Said R, Bakke B, et al. Overview of the CLEF 2009 medical image retrieval track. In: Working Notes of CLEF 2009, Corfu, Greece; 2009.

10 Everingham M, van Gool L, Williams CKI, Winn J, Zisserman A. The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results; 2010. http://www.pascal-network.org/challenges/VOC/voc2010/workshop.

11 Atzori M, Gijsberts A, Castellini C, Caputo B, Hager AGM, Elsig S, et al Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Scientific Data. 2014;1 10.1038/sdata.2014.53 25977804 [OpenAIRE] [PubMed] [DOI]

12 Atzori M, Gijsberts A, Kuzborskij I, Elsig S, Mittaz Hager AG, Deriaz O, et al Characterization of a benchmark database for myoelectric movement classification. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2015;23(1):73–83. 10.1109/TNSRE.2014.2328495 [DOI]

13 Gijsberts A, Atzori M, Castellini C, Muller H, Caputo B. The movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification. IEEE transactions on neural systems and rehabilitation engineering. 2014;22(4):735–744. 10.1109/TNSRE.2014.2303394 24760932 [PubMed] [DOI]

14 Atzori M, Gijsberts A, Müller H, Caputo B. Classification of hand movements in amputated subjects by sEMG and accelerometers. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2014. p. 63.

15 Englehart K, Hudgin B, Parker PA. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering. 2001;48(3):302–311. 10.1109/10.914793 11327498 [OpenAIRE] [PubMed] [DOI]

44 references, page 1 of 3
Abstract
Hand prostheses controlled by surface electromyography are promising due to the non-invasive approach and the control capabilities offered by machine learning. Nevertheless, dexterous prostheses are still scarcely spread due to control difficulties, low robustness and often prohibitive costs. Several sEMG acquisition setups are now available, ranging in terms of costs between a few hundred and several thousand dollars. The objective of this paper is the relative comparison of six acquisition setups on an identical hand movement classification task, in order to help the researchers to choose the proper acquisition setup for their requirements. The acquisition set...
Subjects
free text keywords: Informatique, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine, Research Article, Computer and Information Sciences, Biology and Life Sciences, Anatomy, Musculoskeletal System, Limbs (Anatomy), Arms, Hands, Medicine and Health Sciences, Biotechnology, Medical Devices and Equipment, Assistive Technologies, Prosthetics, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Membrane Electrophysiology, Electrode Recording, Muscle Electrophysiology, Machine Learning, Physical Sciences, Mathematics, Applied Mathematics, Algorithms, Machine Learning Algorithms, Simulation and Modeling, lcsh:Medicine, lcsh:R, lcsh:Science, lcsh:Q, Data acquisition, Support vector machine, Ranging, Accelerometer, Data set, Robustness (computer science), Feature extraction, Computer science, Computer vision, Artificial intelligence, business.industry, business, Electromyography, medicine.diagnostic_test, medicine
44 references, page 1 of 3

1 Finley FR, Wirta RW. Myocoder studies of multiple myopotential resp onse. Archives of Physical Medicine and Rehabilitation. 1967;48(11):598–601. 6060789 [PubMed]

2 Farina D, Member S, Jiang N, Rehbaum H, Member S. The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges. 2014;22(4):797–809.

3 Micera S, Carpaneto J, Raspopovic S. Control of hand prostheses using peripheral information. IEEE Reviews in Biomedical Engineering. 2010;3:48–68. 10.1109/RBME.2010.2085429 22275201 [OpenAIRE] [PubMed] [DOI]

4 Fougner A, Stavdahl Oy, Kyberd PJ, Losier YG, Parker PA. Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control–A Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2012;20(5):663–677. 10.1109/TNSRE.2012.2196711 22665514 [OpenAIRE] [PubMed] [DOI]

5 Atzori M, Müller H. Control Capabilities of Myoelectric Robotic Prostheses by Hand Amputees: A Scientific Research and Market Overview. Frontiers in Systems Neuroscience. 2015;9(162). 10.3389/fnsys.2015.00162 26648850 [OpenAIRE] [PubMed] [DOI]

6 Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. The Journal of Rehabilitation Research and Development. 2011;48(6):643 10.1682/JRRD.2010.09.0177 21938652 [OpenAIRE] [PubMed] [DOI]

7 Hargrove L, Losier Y, Lock B, Englehart K, Hudgins B. A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE. IEEE; 2007. p. 4842–4845.

8 Atzori M, Cognolato M, Müller H. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands. Frontiers in Neurorobotics. 2016;10 10.3389/fnbot.2016.00009 27656140 [OpenAIRE] [PubMed] [DOI]

9 Müller H, Kalpathy-Cramer J, Eggel I, Bedrick S, Said R, Bakke B, et al. Overview of the CLEF 2009 medical image retrieval track. In: Working Notes of CLEF 2009, Corfu, Greece; 2009.

10 Everingham M, van Gool L, Williams CKI, Winn J, Zisserman A. The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results; 2010. http://www.pascal-network.org/challenges/VOC/voc2010/workshop.

11 Atzori M, Gijsberts A, Castellini C, Caputo B, Hager AGM, Elsig S, et al Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Scientific Data. 2014;1 10.1038/sdata.2014.53 25977804 [OpenAIRE] [PubMed] [DOI]

12 Atzori M, Gijsberts A, Kuzborskij I, Elsig S, Mittaz Hager AG, Deriaz O, et al Characterization of a benchmark database for myoelectric movement classification. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2015;23(1):73–83. 10.1109/TNSRE.2014.2328495 [DOI]

13 Gijsberts A, Atzori M, Castellini C, Muller H, Caputo B. The movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification. IEEE transactions on neural systems and rehabilitation engineering. 2014;22(4):735–744. 10.1109/TNSRE.2014.2303394 24760932 [PubMed] [DOI]

14 Atzori M, Gijsberts A, Müller H, Caputo B. Classification of hand movements in amputated subjects by sEMG and accelerometers. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2014. p. 63.

15 Englehart K, Hudgin B, Parker PA. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering. 2001;48(3):302–311. 10.1109/10.914793 11327498 [OpenAIRE] [PubMed] [DOI]

44 references, page 1 of 3
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