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Constrained Particle Filter for Improving Kinect Based Measurements

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

Details

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages306-310
Number of pages5
ISBN (Electronic)978-9-0827-9701-5
ISBN (Print)978-1-5386-3736-4
DOIs
Publication statusPublished - Sep 2018
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Period1/01/00 → …

Abstract

Microsoft Kinect has been gaining popularity in home-based rehabilitation solution due to its affordability and ease of use. It is used as a marker less human skeleton tracking device. However, apart from the fact that the skeleton data are contaminated with high frequency noise, the major drawback lies in the inability to retain the antropometric properties, like the body segments' length, which varies with time during the tracking. In this paper, a particle filter based approach has been proposed to track the human skeleton data in the presence of high frequency noise and multi-objective genetic algorithm is employed to reduce the bone length variations. In our approach multiple segments in skeleton are filtered simultaneously and segments' lengths are preserved by considering their interconnection unlike other methods in available literature. The proposed algorithm has achieved MAPE of 3.44% in maintaining the body segment length close to the ground truth and outperforms state-of-the-art methods.

Keywords

  • biomechanics, bone, genetic algorithms, image motion analysis, image sensors, medical image processing, object tracking, particle filtering (numerical methods), patient rehabilitation, constrained particle filter, kinect based measurements, home-based rehabilitation solution, human skeleton tracking device, high frequency noise, antropometric properties, particle filter based approach, human skeleton data, bone length variations, body segment length, Microsoft kinect, multiobjective genetic algorithm, Bones, Joints, Radar tracking, Noise measurement, Signal processing algorithms, Tracking, Kinect, Particle filter, NSGA, Multi objective optimization

Publication forum classification

Field of science, Statistics Finland