Human Pose Estimation from Imperfect Sensor Data via the Extended Kalman Filter

Vlad Joukov, Rollen D’Souza, Dana Kulić

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

2 Citations (Scopus)

Abstract

Accurate human pose estimation is of vital importance for a variety of human-robot interaction applications, including cooperative task execution, imitation learning and robot-assisted rehabilitation. As robots move from controlled indoor environments to unstructured and outdoor environments, the ability to accurately measure human pose without fixed sensors becomes increasingly important. In this paper, we present a general framework for accurately estimating human pose from a variety of sensors, including body-worn inertial measurement units and cameras, that can be used in indoor and outdoor environments to accurately estimate human pose during arbitrary 3D movements. Using a kinematic model of the human body, the sensor data is fused to estimate the body joint angles and velocities using a constrained Extended Kalman Filter which automatically incorporates feasible joint limits. For periodic movement such as gait, performance can be further improved via online learning of the gait model, individualized to the user. The proposed approach can deal with intermittent data availability and measurement errors during highly dynamic movements.

Original languageEnglish
Title of host publication2016 International Symposium on Experimental Robotics
EditorsDana Kulić, Yoshihiko Nakamura, Oussama Khatib, Gentiane Venture
Place of PublicationCham Switzerland
PublisherSpringer
Pages789-798
Number of pages10
ISBN (Electronic)9783319501154
ISBN (Print)9783319501147
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventInternational Symposium on Experimental Robotics 2016 - Tokyo, Japan
Duration: 3 Oct 20166 Oct 2016
http://iser2016.org/
https://link.springer.com/book/10.1007/978-3-319-50115-4 (Proceedings)

Publication series

NameSpringer Proceedings in Advanced Robotics
PublisherSpringer
Volume1
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

Conference

ConferenceInternational Symposium on Experimental Robotics 2016
Abbreviated titleISER 2016
Country/TerritoryJapan
CityTokyo
Period3/10/166/10/16
Internet address

Keywords

  • Extended Kalman Filter
  • Human pose estimation
  • Motion capture

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