Design of assistive torque for a lower limb exoskeleton based on motion prediction

Susanto, Riska Analia, Kai-Tai Song

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

4 Scopus citations

Abstract

This paper presents a method to generate assistive torque to a lower limb exoskeleton using a predictive approach. A control scheme is proposed to predict the suitable force of next movement of legs using gait information. By using human walking gait cycle and Center of Pressure (CoP), it is expected that the exoskeleton can provide an assistive force to people and help them to walk normally. In order to predict the next movement for assistive torque given by exoskeleton to the human, a predictive Artificial Neural Network (pANN) is developed. A prototype lower limb exoskeleton has been designed and constructed for experimental study. The controller is implemented by using LabVIEW programming. The experimental results verified that the proposed controller can provide proper assistive torques to hip and knee joints for both legs while wearing the exoskeleton.

Original languageEnglish
Title of host publication2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-177
Number of pages6
ISBN (Electronic)9784907764500
DOIs
StatePublished - 18 Nov 2016
Event55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016 - Tsukuba, Japan
Duration: 20 Sep 201623 Sep 2016

Publication series

Name2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016

Conference

Conference55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
CountryJapan
CityTsukuba
Period20/09/1623/09/16

Keywords

  • Artificial Neural Network
  • Exoskeleton
  • Gait motion prediction
  • Walking gait

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