Lift movement detection with a QDA classifier for an active hip exoskeleton

Baojun Chen, Lorenzo Grazi, Francesco Lanotte, Nicola Vitiello, Simona Crea

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

To provide assistance with an active exoskeleton, the control system of the device has to automatically detect the onset of the user’s movement and provide timely assistance, according to the recognized movement. In this paper, we present an algorithm designed to detect the lift movement with an active pelvis exoskeleton, based on a quadratic-discriminant-analysis classifier combined with a rule-based algorithm. The algorithm relies on sensory information acquired from the sensory apparatus of the exoskeleton, without needing additional sensors to be placed on the user’s body. The algorithm was validated in experiments with seven healthy subjects. Participants were requested to execute different actions, i.e. lift and lower a load, stand up, sit down and walk, while wearing the exoskeleton. On average, the algorithm showed an accuracy of 98.7 ± 0.6% in recognizing the lift task; such performance make it suitable for use in real application scenarios.

Original languageEnglish
Title of host publicationBiosystems and Biorobotics
PublisherSpringer International Publishing AG
Pages224-228
Number of pages5
DOIs
Publication statusPublished - Jan 1 2019

Publication series

NameBiosystems and Biorobotics
Volume22
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Artificial Intelligence

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  • Cite this

    Chen, B., Grazi, L., Lanotte, F., Vitiello, N., & Crea, S. (2019). Lift movement detection with a QDA classifier for an active hip exoskeleton. In Biosystems and Biorobotics (pp. 224-228). (Biosystems and Biorobotics; Vol. 22). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-01887-0_43