Denoising of joint tracking data by kinect sensors using clustered Gaussian process regression

An Ti Chiang, Qi Chen, Shijie Li, Yao Wang, Mei Fu

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

Abstract

Using Kinect sensors to monitor and provide feedback to patients performing intervention or rehabilitation exercises is an upcoming trend in healthcare. However, the joint positions measured by the Kinect sensor are often unreliable, especially for joints that are occluded by other parts of the body. Motion capture (MOCAP) systems using multiple cameras from different view angles can accurately track marker positions on the patient. But such systems are costly and inconvenient to patients. In this work, we simultaneously capture the joint positions using both a Kinect sensor and a MOCAP system during a training stage and train a Gaussian Process regression model to map the noisy Kinect measurements to the more accurate MOCAP measurements. To deal with the inherent variations in limb lengths and body postures among different people, we further propose a joint standardization method, which translates the raw joint positions of different people into a standard coordinate, where the distance between each pair of adjacent joints is kept at a reference distance. Our experiments show that the denoised Kinect measurements by the proposed method are more accurate than several benchmark methods.

Original languageEnglish (US)
Title of host publicationMMHealth 2017 - Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, co-located with MM 2017
PublisherAssociation for Computing Machinery, Inc
Pages19-25
Number of pages7
ISBN (Electronic)9781450355049
DOIs
StatePublished - Oct 23 2017
Event2nd International Workshop on Multimedia for Personal Health and Health Care, MMHealth 2017 - Mountain View, United States
Duration: Oct 23 2017 → …

Publication series

NameMMHealth 2017 - Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, co-located with MM 2017

Other

Other2nd International Workshop on Multimedia for Personal Health and Health Care, MMHealth 2017
CountryUnited States
CityMountain View
Period10/23/17 → …

Keywords

  • Denoising of Kinect measurements
  • Gaussian process regression

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology
  • Health Informatics

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  • Cite this

    Chiang, A. T., Chen, Q., Li, S., Wang, Y., & Fu, M. (2017). Denoising of joint tracking data by kinect sensors using clustered Gaussian process regression. In MMHealth 2017 - Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, co-located with MM 2017 (pp. 19-25). (MMHealth 2017 - Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, co-located with MM 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3132635.3132642