Classification of Daily-Life Grasping Activities sEMG Fractal Dimension

Elmer Escandón, Christian Flores

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Applications in pattern recognition and feature extraction for hand tasks are widely applied in prosthesis design through superficial electromyographic signals (sEMG) characterization. Novel applications still require higher classification accuracies and inter-subject invariability. Moreover, as machine learning techniques are implemented in a prosthesis, higher interest is focused on the training data, considering real-life variables as muscle fatigue and continuous data collection. This paper presents the detection of three different grasping action groups using two electrodes positioned in the extensor and flexor digitorum from a benchmark database with acquired real-life signals. Higuchi’s Fractal Dimension feature extraction technique is applied to determine a feature vector as training input data. Consequently, the training algorithm with a Support Vector Machine (SVM) technique for two kernel functions: linear and radial. Results indicate accuracies of 97.2%, 92.2%, 89.7% for two, three, and four task grasping actions with a Radial Basis Function kernel, respectively.

Original languageEnglish
Title of host publicationProceedings of the 6th Brazilian Technology Symposium, BTSym 2020 - Emerging Trends and Challenges in Technology
EditorsYuzo Iano, Osamu Saotome, Guillermo Kemper, Ana Claudia Mendes de Seixas, Gabriel Gomes de Oliveira
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030756796
StatePublished - 2021
Event6th Brazilian Technology Symposium, BTSym 2020 - Virtual, Online
Duration: 26 Oct 202028 Oct 2020

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


Conference6th Brazilian Technology Symposium, BTSym 2020
CityVirtual, Online


  • Daily-life grasping activities
  • Higuchi’s fractal dimension
  • Support Vector Machines
  • Task classification
  • sEMG


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