Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications

Felix Achilles; Alexandru Eugen Ichim; Huseyin Coskun; Federico Tombari; Soheyl Noachtar; Nassir Navab

doi:10.1007/978-3-319-46720-7_57

Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications

Felix Achilles, Alexandru Eugen Ichim, Huseyin Coskun, Federico Tombari, Soheyl Noachtar, Nassir Navab

Informatics 16 - Chair of Computer Aided Medical Procedures

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

58 Scopus citations

Abstract

Motion analysis is typically used for a range of diagnostic procedures in the hospital. While automatic pose estimation from RGBD input has entered the hospital in the domain of rehabilitation medicine and gait analysis,no such method is available for bed-ridden patients. However,patient pose estimation in the bed is required in several fields such as sleep laboratories,epilepsy monitoring and intensive care units. In this work,we propose a learning-based method that allows to automatically infer 3D patient pose from depth images. To this end we rely on a combination of convolutional neural network and recurrent neural network,which we train on a large database that covers a range of motions in the hospital bed. We compare to a state of the art pose estimation method which is trained on the same data and show the superior result of our method. Furthermore,we show that our method can estimate the joint positions under a simulated occluding blanket with an average joint error of 7.56 cm.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
Publisher	Springer Verlag
Pages	491-499
Number of pages	9
ISBN (Print)	9783319467191
DOIs	https://doi.org/10.1007/978-3-319-46720-7_57
State	Published - 2016
Event	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 21 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9900 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	21/10/16 → 21/10/16

Keywords

CNN
Motion capture
Occlusion
Pose estimation
RNN
Random forest

Access to Document

10.1007/978-3-319-46720-7_57

Cite this

Achilles, F., Ichim, A. E., Coskun, H., Tombari, F., Noachtar, S., & Navab, N. (2016). Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, W. Wells, & G. Unal (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 491-499). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_57

Achilles, Felix ; Ichim, Alexandru Eugen ; Coskun, Huseyin et al. / Patient MoCap : Human pose estimation under blanket occlusion for hospital monitoring applications. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Sebastian Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal. Springer Verlag, 2016. pp. 491-499 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications",

abstract = "Motion analysis is typically used for a range of diagnostic procedures in the hospital. While automatic pose estimation from RGBD input has entered the hospital in the domain of rehabilitation medicine and gait analysis,no such method is available for bed-ridden patients. However,patient pose estimation in the bed is required in several fields such as sleep laboratories,epilepsy monitoring and intensive care units. In this work,we propose a learning-based method that allows to automatically infer 3D patient pose from depth images. To this end we rely on a combination of convolutional neural network and recurrent neural network,which we train on a large database that covers a range of motions in the hospital bed. We compare to a state of the art pose estimation method which is trained on the same data and show the superior result of our method. Furthermore,we show that our method can estimate the joint positions under a simulated occluding blanket with an average joint error of 7.56 cm.",

keywords = "CNN, Motion capture, Occlusion, Pose estimation, RNN, Random forest",

author = "Felix Achilles and Ichim, {Alexandru Eugen} and Huseyin Coskun and Federico Tombari and Soheyl Noachtar and Nassir Navab",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",

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Achilles, F, Ichim, AE, Coskun, H, Tombari, F, Noachtar, S & Navab, N 2016, Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications. in S Ourselin, L Joskowicz, MR Sabuncu, W Wells & G Unal (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 491-499, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 21/10/16. https://doi.org/10.1007/978-3-319-46720-7_57

Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications. / Achilles, Felix; Ichim, Alexandru Eugen; Coskun, Huseyin et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Sebastian Ourselin; Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal. Springer Verlag, 2016. p. 491-499 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Achilles, Felix

AU - Ichim, Alexandru Eugen

AU - Coskun, Huseyin

AU - Tombari, Federico

AU - Noachtar, Soheyl

AU - Navab, Nassir

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Motion analysis is typically used for a range of diagnostic procedures in the hospital. While automatic pose estimation from RGBD input has entered the hospital in the domain of rehabilitation medicine and gait analysis,no such method is available for bed-ridden patients. However,patient pose estimation in the bed is required in several fields such as sleep laboratories,epilepsy monitoring and intensive care units. In this work,we propose a learning-based method that allows to automatically infer 3D patient pose from depth images. To this end we rely on a combination of convolutional neural network and recurrent neural network,which we train on a large database that covers a range of motions in the hospital bed. We compare to a state of the art pose estimation method which is trained on the same data and show the superior result of our method. Furthermore,we show that our method can estimate the joint positions under a simulated occluding blanket with an average joint error of 7.56 cm.

AB - Motion analysis is typically used for a range of diagnostic procedures in the hospital. While automatic pose estimation from RGBD input has entered the hospital in the domain of rehabilitation medicine and gait analysis,no such method is available for bed-ridden patients. However,patient pose estimation in the bed is required in several fields such as sleep laboratories,epilepsy monitoring and intensive care units. In this work,we propose a learning-based method that allows to automatically infer 3D patient pose from depth images. To this end we rely on a combination of convolutional neural network and recurrent neural network,which we train on a large database that covers a range of motions in the hospital bed. We compare to a state of the art pose estimation method which is trained on the same data and show the superior result of our method. Furthermore,we show that our method can estimate the joint positions under a simulated occluding blanket with an average joint error of 7.56 cm.

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KW - Occlusion

KW - Pose estimation

KW - RNN

KW - Random forest

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BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

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Achilles F, Ichim AE, Coskun H, Tombari F, Noachtar S, Navab N. Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications. In Ourselin S, Joskowicz L, Sabuncu MR, Wells W, Unal G, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 491-499. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46720-7_57

Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications

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Keywords

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