DMCA
A Novel Sensing and Data Fusion System for 3-D Arm Motion Tracking in Telerehabilitation
| Citations: | 5 - 1 self |
BibTeX
@MISC{Tao_anovel,
author = {Yaqin Tao and Huosheng Hu and Senior Member},
title = {A Novel Sensing and Data Fusion System for 3-D Arm Motion Tracking in Telerehabilitation},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract—In this paper, we present a novel sensing and data fusion system to track 3-D arm motion in a telerehabilitation program. A particle filter (PF) algorithm is adopted in the system to fuse data from inertial and visual sensors in a probabilistic manner. It is able to propagate multimodal distributions of system states based on an “importance sampling ” technique by using sets of weighted particles. To avoid the problem of conventional PF algorithms that suffer from particle degeneracy and perform poorly in a narrow distribution situation, we adopt two strategies in our system, namely state space pruning and an arm physical geometry constraint. Experimental results show that the proposed PF framework outperforms other fusion methods and provides accurate results in comparison to the ground truth. Index Terms—Biomedical measurements, particle filter (PF), sensor fusion, telerehabilitation, upper limb pose estimation, 3-D arm motion tracking. I.
Keyphrases
data fusion system novel sensing 3-d arm motion tracking particle filter 3-d arm motion importance sampling technique ground truth probabilistic manner system state arm physical geometry constraint upper limb pose estimation index term biomedical measurement fusion method visual sensor multimodal distribution telerehabilitation program accurate result particle degeneracy pf framework weighted particle sensor fusion experimental result narrow distribution situation conventional pf algorithm state space pruning
