Development of an abnormal gait analysis system in gait exercise assist robot 'Welwalk' for hemiplegic stroke patients

Issei Nakashima, Daisuke Imoto, Satoshi Hirano, Masahiko Mukaino, Masayuki Imaida, Eiichi Saitoh, Yohei Otaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Welwalk WW-1000 is a gait exercise robotic assist system that allows subjects to walk on treadmill by attaching a knee-ankle-foot robot to a paralyzed limb. Abnormal gait patterns during exercise using Welwalk WW-1000 are evaluated by gait observation or marker-based motion analysis systems. However, gait observation is a subjective and ordinal measure, and marker-based motion analysis systems are challenging to implement due to the complexity of preparing equipment and attaching markers to subjects. In this study, we propose the Welwalk WW-2000 system, which incorporated a marker-less motion analysis system that detects abnormal gait patterns during exercise using the robotic system. Using this system, it is expected that a gait exercise program can be planned from easily obtainable, objective information. This system detects the features of abnormal gait patterns using the body position coordinates of the subject obtained from three-dimensional, inertial, knee angle, and load sensors. The purpose of this study was to validate the marker-less motion analysis system against marker-based motion analysis systems. One healthy male simulated the seven abnormal gait patterns which occur frequently in stroke patients, with four grades of severity. Spearman's rank correlation coefficients were calculated for the relationship between the abnormal gait pattern parameters calculated by each motion analysis system. The correlations between the two systems ranged from 0.81 to 0.95. Therefore, it was confirmed that the marker-less motion analysis system of the Welwalk WW-2000 was valid.

Original languageEnglish
Title of host publication2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
PublisherIEEE Computer Society
Pages1030-1035
Number of pages6
ISBN (Electronic)9781728159072
DOIs
Publication statusPublished - 11-2020
Externally publishedYes
Event8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 - New York City, United States
Duration: 29-11-202001-12-2020

Publication series

NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Volume2020-November
ISSN (Print)2155-1774

Conference

Conference8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Country/TerritoryUnited States
CityNew York City
Period29-11-2001-12-20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

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