Tracking whole hand kinematics using extended Kalman filter.

Qiushi Fu, Marco Santello

Research output: Contribution to journalArticle

Abstract

This paper describes the general procedure, model construction, and experimental results of tracking whole hand kinematics using extended Kalman filter (EKF) based on data recorded from active surface markers. We used a hand model with 29 degrees of freedom that consists of hand global posture, wrist, and digits. The marker protocol had 4 markers on the distal forearm and 20 markers on the dorsal surface of the joints of the digits. To reduce computational load, we divided the state space into four sub-spaces, each of which were estimated with an EKF in a specific order. We tested our framework and found reasonably accurate results (2-4 mm tip position error) when sampling tip to tip pinch at 120 Hz.

Original languageEnglish (US)
Pages (from-to)4606-4609
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2010
Externally publishedYes

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Extended Kalman filters
Biomechanical Phenomena
Kinematics
Hand
Selection Bias
Sampling
Wrist
Posture
Forearm
Theoretical Models
Joints

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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