publication . Article . Preprint . Other literature type . 2019

putEMG—A Surface Electromyography Hand Gesture Recognition Dataset

Piotr Kaczmarek; Tomasz Mańkowski; Jakub Tomczyński;
Open Access English
  • Published: 14 Aug 2019 Journal: Sensors, volume 19, issue 16 (issn: 1424-8220, Copyright policy)
  • Publisher: MDPI AG
Abstract
s forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin&rsquo
Persistent Identifiers
Subjects
free text keywords: sEMG, dataset, gesture recognition, hand, human-machine interface, wearable, Article, Electrical and Electronic Engineering, Analytical Chemistry, Atomic and Molecular Physics, and Optics, Biochemistry, Computer Science - Human-Computer Interaction, lcsh:Chemical technology, lcsh:TP1-1185, Gesture, Artificial intelligence, business.industry, business, Support vector machine, Computer science, Classifier (linguistics), Scripting language, computer.software_genre, computer, Electromyography, medicine.diagnostic_test, medicine, Gesture recognition, RGB color model, Pattern recognition, Invariant (mathematics)
37 references, page 1 of 3

Roland, T., Wimberger, K., Amsuess, S., Russold, M.F., Baumgartner, W.. An insulated flexible sensor for stable electromyography detection: Application to prosthesis control. Sensors. 2019; 19 [OpenAIRE] [PubMed] [DOI]

Yamagami, M., Peters, K., Milovanovic, I., Kuang, I., Yang, Z., Lu, N., Steele, K.. Assessment of dry epidermal electrodes for long-term electromyography measurements. Sensors. 2018; 18 [OpenAIRE] [PubMed] [DOI]

Posada-Quintero, H.F., Rood, R.T., Burnham, K., Pennace, J., Chon, K.H.. Assessment of carbon/salt/adhesive electrodes for surface electromyography measurements. IEEE J. Transl. Eng. Health Med.. 2016; 4: 1-9 [OpenAIRE] [PubMed] [DOI]

Tabard-Fougère, A., Rose-Dulcina, K., Pittet, V., Dayer, R., Vuillerme, N., Armand, S.. EMG normalization method based on grade 3 of manual muscle testing: Within-and between-day reliability of normalization tasks and application to gait analysis. Gait Posture. 2018; 60: 6-12 [OpenAIRE] [PubMed] [DOI]

Geng, W., Du, Y., Jin, W., Wei, W., Hu, Y., Li, J.. Gesture recognition by instantaneous surface EMG images. Sci. Rep.. 2016; 6: 36571 [OpenAIRE] [PubMed] [DOI]

Khushaba, R.N., Takruri, M., Miro, J.V., Kodagoda, S.. Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features. Neural Netw.. 2014; 55: 42-58 [OpenAIRE] [PubMed] [DOI]

Khushaba, R.N., Al-Timemy, A., Kodagoda, S., Nazarpour, K.. Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst. Appl.. 2016; 61: 154-161 [OpenAIRE] [DOI]

Hakonen, M., Piitulainen, H., Visala, A.. Current state of digital signal processing in myoelectric interfaces and related applications. Biomed. Signal Process. Control. 2015; 18: 334-359 [OpenAIRE] [DOI]

Tomczyński, J., Mańkowski, T., Kaczmarek, P.. Localisation method for sEMG electrode array, towards hand gesture recognition HMI development. Proceedings of the 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). : 48-52

Palermo, F., Cognolato, M., Gijsberts, A., Müller, H., Caputo, B., Atzori, M.. Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR). 2017: 1154-1159

Zhai, X., Jelfs, B., Chan, R.H., Tin, C.. Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network. Front. Neurosci.. 2017; 11: 379 [OpenAIRE] [PubMed] [DOI]

Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., Laurillau, Y.. EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst. Appl.. 2013; 40: 4832-4840 [OpenAIRE] [DOI]

Phinyomark, A., Scheme, E.. EMG pattern recognition in the era of big data and deep learning. Big Data Cogn. Comput.. 2018; 2 [OpenAIRE] [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., Kuzborskij, I., Elsig, S., Hager, A.G.M., Deriaz, O., Castellini, C., Müller, H., Caputo, B.. Characterization of a benchmark database for myoelectric movement classification. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 23: 73-83 [PubMed] [DOI]

37 references, page 1 of 3
Abstract
s forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin&rsquo
Persistent Identifiers
Subjects
free text keywords: sEMG, dataset, gesture recognition, hand, human-machine interface, wearable, Article, Electrical and Electronic Engineering, Analytical Chemistry, Atomic and Molecular Physics, and Optics, Biochemistry, Computer Science - Human-Computer Interaction, lcsh:Chemical technology, lcsh:TP1-1185, Gesture, Artificial intelligence, business.industry, business, Support vector machine, Computer science, Classifier (linguistics), Scripting language, computer.software_genre, computer, Electromyography, medicine.diagnostic_test, medicine, Gesture recognition, RGB color model, Pattern recognition, Invariant (mathematics)
37 references, page 1 of 3

Roland, T., Wimberger, K., Amsuess, S., Russold, M.F., Baumgartner, W.. An insulated flexible sensor for stable electromyography detection: Application to prosthesis control. Sensors. 2019; 19 [OpenAIRE] [PubMed] [DOI]

Yamagami, M., Peters, K., Milovanovic, I., Kuang, I., Yang, Z., Lu, N., Steele, K.. Assessment of dry epidermal electrodes for long-term electromyography measurements. Sensors. 2018; 18 [OpenAIRE] [PubMed] [DOI]

Posada-Quintero, H.F., Rood, R.T., Burnham, K., Pennace, J., Chon, K.H.. Assessment of carbon/salt/adhesive electrodes for surface electromyography measurements. IEEE J. Transl. Eng. Health Med.. 2016; 4: 1-9 [OpenAIRE] [PubMed] [DOI]

Tabard-Fougère, A., Rose-Dulcina, K., Pittet, V., Dayer, R., Vuillerme, N., Armand, S.. EMG normalization method based on grade 3 of manual muscle testing: Within-and between-day reliability of normalization tasks and application to gait analysis. Gait Posture. 2018; 60: 6-12 [OpenAIRE] [PubMed] [DOI]

Geng, W., Du, Y., Jin, W., Wei, W., Hu, Y., Li, J.. Gesture recognition by instantaneous surface EMG images. Sci. Rep.. 2016; 6: 36571 [OpenAIRE] [PubMed] [DOI]

Khushaba, R.N., Takruri, M., Miro, J.V., Kodagoda, S.. Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features. Neural Netw.. 2014; 55: 42-58 [OpenAIRE] [PubMed] [DOI]

Khushaba, R.N., Al-Timemy, A., Kodagoda, S., Nazarpour, K.. Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst. Appl.. 2016; 61: 154-161 [OpenAIRE] [DOI]

Hakonen, M., Piitulainen, H., Visala, A.. Current state of digital signal processing in myoelectric interfaces and related applications. Biomed. Signal Process. Control. 2015; 18: 334-359 [OpenAIRE] [DOI]

Tomczyński, J., Mańkowski, T., Kaczmarek, P.. Localisation method for sEMG electrode array, towards hand gesture recognition HMI development. Proceedings of the 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). : 48-52

Palermo, F., Cognolato, M., Gijsberts, A., Müller, H., Caputo, B., Atzori, M.. Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR). 2017: 1154-1159

Zhai, X., Jelfs, B., Chan, R.H., Tin, C.. Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network. Front. Neurosci.. 2017; 11: 379 [OpenAIRE] [PubMed] [DOI]

Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., Laurillau, Y.. EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst. Appl.. 2013; 40: 4832-4840 [OpenAIRE] [DOI]

Phinyomark, A., Scheme, E.. EMG pattern recognition in the era of big data and deep learning. Big Data Cogn. Comput.. 2018; 2 [OpenAIRE] [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., Kuzborskij, I., Elsig, S., Hager, A.G.M., Deriaz, O., Castellini, C., Müller, H., Caputo, B.. Characterization of a benchmark database for myoelectric movement classification. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 23: 73-83 [PubMed] [DOI]

37 references, page 1 of 3
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