Locomotion Mode Classification Based on Support Vector Machines and Hip Joint Angles: A Feasibility Study for Applications in Wearable Robotics

Vito Papapicco, Andrea Parri, Elena Martini, Vitoantonio Bevilacqua, Simona Crea, Nicola Vitiello

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Intention decoding of locomotion-related activities covers an essential role in the control architecture of active orthotic devices for gait assistance. This work presents a subject-independent classification method, based on support vector machines, for the identification of locomotion-related activities, i.e. overground walking, ascending and descending stairs. The algorithm uses features extracted only from hip angles measured by joint encoders integrated on a lower-limb active orthosis for gait assistance. Different sets of features are tested in order to identify the configuration with better performance. The highest success rate (i.e. 99% of correct classification) is achieved using the maximum number of features, namely seven features. In future works the algorithm based on the identified set of features will be implemented on the real-time controller of the active pelvis orthosis and tested in activities of daily life.

Original languageEnglish
Title of host publicationSpringer Proceedings in Advanced Robotics
PublisherSpringer Science and Business Media B.V.
Pages197-205
Number of pages9
DOIs
Publication statusPublished - 2019

Publication series

NameSpringer Proceedings in Advanced Robotics
Volume7
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

Keywords

  • Gait Assistance
  • Locomotion Mode
  • Locomotor-related Activity
  • Support Vector Machine (SVMs)
  • Wearable Robotics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Engineering (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Applied Mathematics

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