Abstract
Classification is a crucial task in processing of surface electromyography (sEMG) signals. The Multi-Label Classification (ML) has gained prominence for biomedical applications, especially in gesture recognition for robotic prosthetic hand control. Differently of traditional method for classification involving three or more classes (multi-class), where each sample has only one output label, in the ML method, each sample may be referred to more than one output label. This work presents a pilot study of classification strategy using multi-label for hand gesture recognition. The methodology used to design the proposed system was the problem-transformation approach. The labels that compound the classifiers were developed based on the relationships of prosthesis operation and the anatomical nature of recognized gestures. In this way, it is easier to perform the motors of a prosthetic hand. The sEMG signals were obtained through the commercial Myo Armband (Thalmic Labs), in which data from 7 subjects were acquired performing 5 hand gestures. For the ML approach, the feature set was compound by L-Scale (LS), Maximum Fractal Length (MFL), Willison Amplitude (WAMP), and Mean Square Root (MSR). Nine classifiers were used, and the best classifiers with a accuracy of 97 and 98% with k-Nearest Neighbor (KNN) and Support Vector Machine with Radial Basis Function Kernel (SVMRBF). Regarding the single-label, no significant differences were observed between the multi-label tests.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang G, Yin J, Su X et al (2014) Augmenting multi-instance multilabel learning with sparse Bayesian models for skin biopsy image analysis. BioMed Res Int 13:e305629
León CL (2017) Multilabel classification of EEG-based combined motor imageries implemented for the 3D control of a robotic arm. Thesis, Unversity of Lorraine, Lorraine, France
Maxwell A, Li R, Yang B et al (2017) Deep learning architectures for multi-label classification of intelligent health risk prediction. BMC Bioinform 18:523
Olsson Alexander E, Paulina S, Elin A, Anders B, Nebojša M, Christian A (2019) Extraction of multi-labelled movement information from the raw HD-sEMG image with time-domain depth. Sci Rep 9:1–10
Tsoumakas G, Katakis I (2007) Multi-label classification: an overview. Int J Data Warehousing Mining 3:1–13
Björklund P (2018) Investigating the use of multi-label classification methods for the purpose of classifying electromyographic signals. Master’s thesis, Biomedical Engineering Lund University, Lund, Sweden
Alexander O (2018) sEMG classication with convolutional neural networks: a multi-label approach for prosthetic hand control. Ph.D. thesis, Lund University, Lund, Sweden
Na D, Liu L-Z, Yu X-J, Li Q, Yeh S-C (2019) Classification of multichannel surface-electromyography signals based on convolutional neural networks. J Ind Inform Integr 15:201–206
Pontim CE, Campos DP, Utida MA, Setti JAP (2019) Prototyping and control of a 3D printed bionic hand operated by myoelectric signals: toward accessibility for amputees. In: Anais do XII Simpósio de Engenharia Biomédica e IX Simpósio de Instrumentação e Imagens Médicas (Uberlândia, Brasil)Zenodo
Gary K, David G (2010) Essentials of electromyography. Human Kinetics 2010. Google-Books-ID: 9izzXnnKzJoC
Su R, Chen X, Cao S, Xu Z (2016) Random forest-based recognition of isolated sign language subwords using data from accelerometers and surface electromyographic sensors. Sensors 16:100
Angkoon P, Khushaba Rami N, Erik S (2018) Feature extraction and selection for myoelectric control based on wearable EMG sensors. Sensors (Basel, Switzerland) 18
Mendes Junior José Jair A, Freitas Melissa LB, Siqueira Hugo V, Lazzaretti André E, Pichorim Sergio F, Stevan Sergio L (2020) Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomed Signal Process Control 59:101920
Freitas Melissa LB, Jair Alves MJ, Prado CD, Luiz SS (2019) Hand gestures classification using multichannel sEMG armband. In: Rodrigo C-F, Carlos MJ, Victor AA (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings (Singapore), pp 239–246
Olsson Alexander E, Paulina S, Elin A, Anders B, Nebojša M, Christian A (2019) Extraction of multi-labelled movement information from the raw HD-sEMG image with time-domain depth. Sci Rep 9:7244
Acknowledgements
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), from Fundação Araucária (FA), and from Financiadora de Estudos e Projetos (FINEP).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no conflict of interest.
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Mendes Junior, J.J.A., Pontim, C.E., Campos, D.P. (2022). Multi-label EMG Classification of Isotonic Hand Movements: A Suitable Method for Robotic Prosthesis Control. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_243
Download citation
DOI: https://doi.org/10.1007/978-3-030-70601-2_243
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70600-5
Online ISBN: 978-3-030-70601-2
eBook Packages: EngineeringEngineering (R0)