A Novel Technique for Improving the Robustness to Sensor Rotation in Hand Gesture Recognition Using sEMG

  • Victor H. VimosEmail author
  • Marco Benalcázar
  • Alex F. Oña
  • Patricio J. Cruz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1078)


Hand gesture recognition consists of identifying the class among a set of classes of a hand movement given. Surface electromyography (sEMG) measures the electrical activity generated by voluntary contractions of skeletal muscles. The performance of a recognition system is affected significantly by the orientation of the armband. This orientation could change every time that the user wears the armband. In this paper, a novel technique to improve the robustness in a recognition system with variation in the orientation of the armband is proposed. To test the performance of the proposed model, 4 experiments at recognizing 6 hand gestures are executed. In these experiments the proposed method shows a recognition accuracy of 92.4% versus 59.5%, which corresponds to the accuracy of a traditional recognition model without the correction of orientation.


Hand gesture recognition sEMG SVM 



The authors gratefully acknowledge the financial support provided by the Escuela Politécnica Nacional and the Corporación Ecuatoriana para el Desarrollo de la Investigación y la Academia (CEDIA) for the development of the research project CEPRA-2019-13-Reconocimiento de Gestos.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Escuela Politécnica NacionalQuitoEcuador
  2. 2.Departamento de Informática y Ciencias de la ComputaciónEscuela Politécnica NacionalQuitoEcuador
  3. 3.Escuela de Formación de TecnólogosEscuela Politécnica NacionalQuitoEcuador
  4. 4.Departamento de Automatización y Control IndustrialEscuela Politécnica NacionalQuitoEcuador

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