Abstract
This work presents an evaluation of the number of channel using an armband device for classification of hand gestures, aiming to reduce the feature vector dimensionality. Four classifiers and their optimized feature sets were used to recognize 6 gestures. Two search methods were applied to find the best channels: wrapper by Sequential Forward Selection and Binary Particle Swarm Optimization. Moreover, the data were organized in two approaches, analyzing the contribution of each channel and the contribution of each individual feature-channel. Each method resulted in different occurrences for the repeated channels, being the channels placed on flexor carpi ulnaris, palmaris longus, braquioradialis, and extensor digitorum muscles the most repeated. This analysis obtained an average of gain of 2 and a 60% of dimensionality reduction in classification, specially for the Support Vector Machine classifier, reaching 92.3% of hit rate with 9 inputs in Feature-Channel and wrapper approach. The applied methods indicate that there are some dependencies of features and channels in classification process. These dependencies could determine an ideal quantity of channels for a set of gestures and features not only depending on the classifier, but depending of the acquisition channels.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hassan HF, Abou-Loukh SJ, Ibraheem IK (2019) Teleoperated robotic arm movement using electromyography signal with wearable Myo armband. J King Saud Univ Eng Sci 32(6):378–387
Mendes JJA Jr, Freitas MLB, Stevan SL Jr, Pichorim SF (2019) Recognition of libras static alphabet with MyoTM and multi-layer perceptron XXVI Brazilian congress on biomedical engineering, pp 413–419
Freitas MLB, Mendes JJA Jr, Campos DP, Stevan SL (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. Springer, Singapore, pp 239–246
Côté-Allard U, Gagnon-Turcotte G, Laviolette F, Gosselin B (2019) A low-cost, wireless, 3-D-printed custom Armband for sEMG hand gesture recognition. In: Sensors (Basel, Switzerland). Multidisciplinary Digital Publishing Institute (MDPI), p 19
Kim S, Kim J, Koo B et al (2019) Development of an Armband EMG module and a pattern recognition algorithm for the 5-finger myoelectric hand prosthesis. Int J Precis Eng Manufact 20:1997–2006
Phinyomark A, Khushaba R, Scheme E (2018) Feature extraction and selection for myoelectric control based on wearable EMG sensors. Sensors (Basel, Switzerland) 18
Farrell TR, Weir RF (2008) A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control. IEEE Trans Biomed Eng 55:2198–2211
Zhang A, Gao N, Wang L, Li Q (2018) Combined influence of classifiers, window lengths and number of channels on EMG pattern recognition for upper limb movement classification. In: 2018 11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–5
Hargrove LJ, Englehart K, Hudgins B (2007) A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans Biomed Eng 54:847–853
Hakonen M, Piitulainen H, Visala A (2015) Current state of digital signal processing in myoelectric interfaces and related applications. Biomed Sig Process Control 18:334–359
Toledo-Pérez DC, Martínez-Prado MA, Gómez-Loenzo RA, Paredes-García WJ, Rodríguez-Reséndiz J (2019) A study of movement classification of the lower limb based on up to 4-EMG channels. Electronics 8(3):259. Multidisciplinary Digital Publishing Institute
Mendes JJA Jr, Freitas MLB, Siqueira HV, Lazzaretti AE, Pichorim SF, Stevan SL (2020) Feature selection and dimensionality reduction: an extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomed Sig Process Contr 59:101920
Young AJ, Hargrove LJ, Kuiken TA (2012) Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Trans Biomed Eng 59:645–652
Anam K, Al-Jumaily A (2017) Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Netw Off J Int Neural Netw Soc 85:51–68
Geng Y, Zhang X, Zhang YT, Li G (2014) A novel channel selection method for multiple motion classification using high-density electromyography. BioMed Eng Online 13:102
Celadon N, Dosen S, Paleari M, Farina D, Ariano P (2015) Individual finger classification from surface EMG: influence of electrode set. In: Conference proceedings: ...annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. Annual conference, vol 15, pp 7284–7287
Hwang HJ, Hahne JM, Müller KR (2014) Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom. J Neural Eng 11:056008
Al-Angari HM, Kanitz G, Tarantino S, Cipriani C (2016) Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements. Biomed Sig Process Control 27:24–31
Purushothaman G, Vikas R (2018) Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals. Australas Phys Eng Sci Med 41:549–559
El Aboudi N, Benhlima Laila. Review on wrapper feature selection approaches. In: 2016 international conference on engineering MIS (ICEMIS), pp 1–5
Too J, Abdullah AR, Mohd Saad N (2019) A new co-evolution binary particle swarm optimization with multiple inertia weight strategy for feature selection. Informatics 6(2):21. Multidisciplinary Digital Publishing Institute
Acknowledgements
This study was financed in part by the Coordenaço de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
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, J.J.A., Freitas, M.L.B., Campos, D.P., Pontim, C.E., Stevan, S.L., Pichorim, S.F. (2022). Channel Influence in Armband Approach for Gesture Recognition by sEMG Signals. 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_234
Download citation
DOI: https://doi.org/10.1007/978-3-030-70601-2_234
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70600-5
Online ISBN: 978-3-030-70601-2
eBook Packages: EngineeringEngineering (R0)