CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications

Luis Zavala Mondragon, B. Lamichhane (Corresponding author), Lu Zhang, Gerard de Haan

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


Computer vision based patient activity monitoring systems can be attractive for various unobtrusive clinical applications. Such a monitoring system can be developed using movement information derived from the skeleton model of the current body pose, e.g. obtained using a depth camera. Earlier research using estimated skeleton models have been focused mostly on gaming applications. In this paper, we propose CNN-SkelPose as a skeleton model estimation method for clinical applications. CNN-SkelPose uses a trained Convolutional Neural Network to extract both the local and global information from the depth image. CNN-SkelPose outperforms the baseline model of Skeltrack for reliable skeleton model estimation in patient monitoring scenarios. Our results show the inadequacy of existing methods for skeleton model estimation when applied to a clinical scenario and suggests CNN-SkelPose as an improvement towards this application.
Original languageEnglish
Pages (from-to)2369-2380
Number of pages12
JournalJournal of Ambient Intelligence and Humanized Computing
Issue number6
Early online date28 Feb 2019
Publication statusPublished - 1 Jun 2020


  • Actigraphy
  • CNN
  • Clinical application
  • Skeleton model


Dive into the research topics of 'CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications'. Together they form a unique fingerprint.

Cite this