publication . Article . Other literature type . 2019

Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor

Nadia Nasri; Sergio Orts-Escolano; Francisco Gomez-Donoso; Miguel Cazorla;
Open Access English
  • Published: 17 Jan 2019
  • Publisher: MDPI
  • Country: Spain
Abstract
Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures....
Persistent Identifiers
Subjects
free text keywords: Surface electromyography sensor, Dataset, Gated recurrent units, Gesture recognition, Ciencia de la Computación e Inteligencia Artificial, Article, surface electromyography sensor, dataset, gated recurrent units, gesture recognition, Electrical and Electronic Engineering, Analytical Chemistry, Atomic and Molecular Physics, and Optics, Biochemistry, Electromyography, medicine.diagnostic_test, medicine, Computer vision, Computer science, Test set, Use of technology, Gesture, Artificial intelligence, business.industry, business, Gesture recognition, lcsh:Chemical technology, lcsh:TP1-1185
Related Organizations
50 references, page 1 of 4

Cook, A.M., Polgar, J.M.. Essentials of Assistive Technologies. 2012

Costa, A., Martinez-Martin, E., Cazorla, M., Julian, V.. PHAROS—PHysical Assistant RObot System. Sensors. 2018; 18 [OpenAIRE] [DOI]

LeBlanc, M.. The LN-4 Prosthetic Hand. Give Hope—Give a Hand. 2008

Momen, K., Krishnan, S., Chau, T.. Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Trans. Neural Syst. Rehabil. Eng.. 2007; 15: 535-542 [PubMed] [DOI]

Amsuss, S., Goebel, P.M., Jiang, N., Graimann, B., Paredes, L., Farina, D.. Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Trans. Biomed. Eng.. 2014; 61: 1167-1176 [PubMed] [DOI]

Boostani, R., Moradi, M.H.. Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol. Meas.. 2003; 24: 309 [OpenAIRE] [PubMed] [DOI]

Gijsberts, A., Atzori, M., Castellini, C., Müller, H., Caputo, B.. Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 22: 735-744 [PubMed] [DOI]

Atzori, M., Gijsberts, A., Castellini, C., Caputo, B., Hager, A.G.M., Elsig, S., Giatsidis, G., Bassetto, F., Müller, H.. Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci. Data. 2014; 1: 140053 [OpenAIRE] [PubMed] [DOI]

Asimov, I.. The Three Laws of Robotics. 1941

HRI ’11: Proceedings of the 6th International Conference on Human-robot Interaction, Lausanne, Switzerland, 6–9 March 2011. 2011: 609114

Scholtz, J.. Human Robot Interactions: Creating Synergistic Cyber Forces. 2002

Yang, C., Chang, S., Liang, P., Li, Z., Su, C.Y.. Teleoperated robot writing using EMG signals. Proceedings of the 2015 IEEE International Conference on Information and Automation. : 2264-2269

Reddivari, H., Yang, C., Ju, Z., Liang, P., Li, Z., Xu, B.. Teleoperation control of Baxter robot using body motion tracking. Proceedings of the 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). : 1-6 [DOI]

Xu, Y., Yang, C., Liang, P., Zhao, L., Li, Z.. Development of a hybrid motion capture method using MYO armband with application to teleoperation. Proceedings of the 2016 IEEE International Conference on Mechatronics and Automation. : 1179-1184 [DOI]

Bisi, S., De Luca, L., Shrestha, B., Yang, Z., Gandhi, V.. Development of an EMG-Controlled Mobile Robot. Robotics. 2018; 7 [OpenAIRE] [DOI]

50 references, page 1 of 4
Abstract
Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures....
Persistent Identifiers
Subjects
free text keywords: Surface electromyography sensor, Dataset, Gated recurrent units, Gesture recognition, Ciencia de la Computación e Inteligencia Artificial, Article, surface electromyography sensor, dataset, gated recurrent units, gesture recognition, Electrical and Electronic Engineering, Analytical Chemistry, Atomic and Molecular Physics, and Optics, Biochemistry, Electromyography, medicine.diagnostic_test, medicine, Computer vision, Computer science, Test set, Use of technology, Gesture, Artificial intelligence, business.industry, business, Gesture recognition, lcsh:Chemical technology, lcsh:TP1-1185
Related Organizations
50 references, page 1 of 4

Cook, A.M., Polgar, J.M.. Essentials of Assistive Technologies. 2012

Costa, A., Martinez-Martin, E., Cazorla, M., Julian, V.. PHAROS—PHysical Assistant RObot System. Sensors. 2018; 18 [OpenAIRE] [DOI]

LeBlanc, M.. The LN-4 Prosthetic Hand. Give Hope—Give a Hand. 2008

Momen, K., Krishnan, S., Chau, T.. Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Trans. Neural Syst. Rehabil. Eng.. 2007; 15: 535-542 [PubMed] [DOI]

Amsuss, S., Goebel, P.M., Jiang, N., Graimann, B., Paredes, L., Farina, D.. Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Trans. Biomed. Eng.. 2014; 61: 1167-1176 [PubMed] [DOI]

Boostani, R., Moradi, M.H.. Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol. Meas.. 2003; 24: 309 [OpenAIRE] [PubMed] [DOI]

Gijsberts, A., Atzori, M., Castellini, C., Müller, H., Caputo, B.. Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 22: 735-744 [PubMed] [DOI]

Atzori, M., Gijsberts, A., Castellini, C., Caputo, B., Hager, A.G.M., Elsig, S., Giatsidis, G., Bassetto, F., Müller, H.. Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci. Data. 2014; 1: 140053 [OpenAIRE] [PubMed] [DOI]

Asimov, I.. The Three Laws of Robotics. 1941

HRI ’11: Proceedings of the 6th International Conference on Human-robot Interaction, Lausanne, Switzerland, 6–9 March 2011. 2011: 609114

Scholtz, J.. Human Robot Interactions: Creating Synergistic Cyber Forces. 2002

Yang, C., Chang, S., Liang, P., Li, Z., Su, C.Y.. Teleoperated robot writing using EMG signals. Proceedings of the 2015 IEEE International Conference on Information and Automation. : 2264-2269

Reddivari, H., Yang, C., Ju, Z., Liang, P., Li, Z., Xu, B.. Teleoperation control of Baxter robot using body motion tracking. Proceedings of the 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). : 1-6 [DOI]

Xu, Y., Yang, C., Liang, P., Zhao, L., Li, Z.. Development of a hybrid motion capture method using MYO armband with application to teleoperation. Proceedings of the 2016 IEEE International Conference on Mechatronics and Automation. : 1179-1184 [DOI]

Bisi, S., De Luca, L., Shrestha, B., Yang, Z., Gandhi, V.. Development of an EMG-Controlled Mobile Robot. Robotics. 2018; 7 [OpenAIRE] [DOI]

50 references, page 1 of 4
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