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

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 resear...

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Bibliographic Details
Published inJournal of ambient intelligence and humanized computing Vol. 11; no. 6; pp. 2369 - 2380
Main Authors Zavala-Mondragon, Luis A., Lamichhane, Bishal, Zhang, Lu, Haan, Gerard de
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2020
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Artificial neural networks
Cameras
Computational Intelligence
Computer vision
Datasets
Engineering
Hands
Methods
Monitoring
Original Research
Robotics and Automation
User Interfaces and Human Computer Interaction
Actigraphy
CNN
Clinical application
Skeleton model
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Summary: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.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-019-01259-5