Dynamic Motion Tracking Based on Point Cloud Matching with Personalized Body Segmentation

Tomoko Ono, Ryo Eguchi, Masaki Takahashi

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

1 Citation (Scopus)

Abstract

Falling is a serious problem with the growing elderly population. In this sense, clinical institutions have implemented motor function assessment programs. In particular, the timed up and go test (TUG) is the most frequently applied clinical trial to assess the elderly walking ability in many clinical institutions and communities. In this study, we proposed a gait measurement system that can evaluate motor function in dynamic gait tests, such as the TUG test, using the point clouds of depth sensors (Kinect). The TUG test is a dynamic task that includes 3m of walking and turning motion. However, estimating joint positions using conventional methods that use Kinect skeleton function or point clouds is difficult. To solve these problems, before applying the iterative closest point algorithm, we proposed a method to move the segment model to a pre-estimated position and perform matching. In the accuracy verification experiments of several young people, the average error of each joint position was less than approximately 0.03 m, and the average error of the knee angle was approximately 4.54 to 5.13 degrees. These results indicate that the values estimated by the proposed method are useful as values for evaluating clinical tasks.

Original languageEnglish
Title of host publication2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
PublisherIEEE Computer Society
Pages61-67
Number of pages7
ISBN (Electronic)9781728159072
DOIs
Publication statusPublished - 2020 Nov
Event8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 - New York City, United States
Duration: 2020 Nov 292020 Dec 1

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
Period20/11/2920/12/1

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

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