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Evaluation of multi-class support-vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device

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Abstract

One-vs-One (OVO) and One-vs-All (OVA) are decomposition methods for multi-class strategies used to allow binary Support-Vector Machines (SVM) to transform a given k-class problem into pairwise small problems. In this context, the present work proposes the analysis of these two decomposition methods applied to the hand posture recognition problem in which the sEMG data of eight participants were collected by means of an 8-channel armband bracelet located on the forearm. Linear, Polynomial and Radial Basis Function kernels functions and its adjustments level were implemented combined to the strategies OVO and OVA to compare the performance of the SVM when mapping posture data into the classification spaces spanned by the studied kernels. Acquired sEMG signals were segmented considering 0.16 s e 0.32 s time windows. Root Mean Square (RMS) feature was extracted from each time window of each posture and used for SVM training. The present work focused in investigating the relationship between the multi-class strategies combined to kernels adjustments levels and SVM classification performance. Promising results were observed using OVA strategy which presents a reduced number of binary SVM implementation achieved a mean accuracy of 97.63%.

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Acknowledgements

The authors acknowledge the support of the Instituto Tecnológico de Aeronáutica (ITA) / Departamento de Engenharia Eletrônica e de Computação, São José dos Campos - SP, Brazil and the Laboratório de Controle Aplicado (LCA) of the Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), São Paulo - SP, Brazil. The authors thank the CAPES for the support as well.

Funding

This work was supported by the Brazilian Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

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Authors and Affiliations

  1. Instituto Tecnológico de Aeronáutica (ITA), Praça Marechal Eduardo Gomes, 50 - Vila das Acacias, São José dos Campos, SP, 12228-900, Brazil

    Thays Falcari & Osamu Saotome

  2. Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), R. Pedro Vicente, 625 - Canindé, São Paulo, SP, 01109-010, Brazil

    Ricardo Pires & Alexandre Brincalepe Campo

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  1. Thays Falcari
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  2. Osamu Saotome
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Correspondence to Thays Falcari.

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The authors confirm that there are no known conflict of interest associated with this publication and there has been no financial support for this work that could have influenced its outcome.

Ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Experiments were performed after approval from research ethics committee of Instituto Federal de Educação, Ciência e Tecnologia de São Paulo under the CAAE Registration Number: 80304417.4.0000.5473.

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Falcari, T., Saotome, O., Pires, R. et al. Evaluation of multi-class support-vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device. Biomed. Eng. Lett. 10, 275–284 (2020). https://doi.org/10.1007/s13534-019-00141-9

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  • DOI: https://doi.org/10.1007/s13534-019-00141-9

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