Ev
Esther van der Heide
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3D Human Pose Estimation
Using a Top-View Depth Camera
Master thesis
(2020)
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Prerak Mody, Klaus Hildebrandt, Fei Zuo, Esther van der Heide, Hayley Hung, Elmar Eisemann
The onset of delirium, a disturbance in the mental activities of a patient, can be potentially detected by understanding activities within an Intensive Care Unit (ICU) room. Such activities can be extracted by estimating human pose via a visual capture of the scene. This work uses a top-view depth camera in an ICU room to estimate pose of the non-patient stakeholders. The top-view leads to self-occlusions of body joints and thus poses a challenge for estimation of complete human pose. In addition, the presence of multiple persons in the room poses a secondary challenge, as detected body-joints need to be parsed into individual poses. To address these challenges, a 3D point cloud is extracted from the top-view depth image and passed through a 3D Convolutional Neural Network (CNN). This baseline method is capable of estimating both body-joints and body-parts to eventually output human pose for multiple persons. To improve the quality of output poses, the baseline method can benefit from additional spatial context since the problem of human pose estimation has a highly structured output. The proposed techniques either increase the receptive field, perform feature extraction at multiple scales or change the order of data processing. An increase in F1-score for the proposed methods highlights the importance of additional spatial context as a crucial tool to improve the performance of pose estimation models.
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The onset of delirium, a disturbance in the mental activities of a patient, can be potentially detected by understanding activities within an Intensive Care Unit (ICU) room. Such activities can be extracted by estimating human pose via a visual capture of the scene. This work uses a top-view depth camera in an ICU room to estimate pose of the non-patient stakeholders. The top-view leads to self-occlusions of body joints and thus poses a challenge for estimation of complete human pose. In addition, the presence of multiple persons in the room poses a secondary challenge, as detected body-joints need to be parsed into individual poses. To address these challenges, a 3D point cloud is extracted from the top-view depth image and passed through a 3D Convolutional Neural Network (CNN). This baseline method is capable of estimating both body-joints and body-parts to eventually output human pose for multiple persons. To improve the quality of output poses, the baseline method can benefit from additional spatial context since the problem of human pose estimation has a highly structured output. The proposed techniques either increase the receptive field, perform feature extraction at multiple scales or change the order of data processing. An increase in F1-score for the proposed methods highlights the importance of additional spatial context as a crucial tool to improve the performance of pose estimation models.
Master thesis
(2019)
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Himanshu Shah, Esther van der Heide, Ranga Rao Venkatesha Prasad, Gerd Kortuem, Christoph Lofi
Delirium occurs in upto 80% of the ICU patients and is one of the most common source of morbidity and mortality in the ICU. Inattention is one of the core features of delirium and affects 97% - 100% of the delirium patients. This indicates that attention can be a good parameter for detecting delirium. In literature, it has been found that the use of visual attention tests have been successful in detecting delirium among the ICU patients. The VitalSky is a luminous ceiling used for providing light therapy to the ICU patients. Since, the VitalSky is already available in the ICU and allows to render visual contents, it can be used for detecting delirium in the ICU patients by performing visual attention tests. In this thesis, visual attention tests are developed for the VitalSky. Moreover, different interaction devices are explored that can be used by the ICU patients to respond to the tasks of the attention test and help in automation of the test. The technical feasibility and usability of the automated setup is also evaluated in this thesis. The validation of the performance of the attention tests and user preference of the response devices is assessed by conducting a pilot study on 10 healthy volunteers and collecting their subjective feedback.
...
Delirium occurs in upto 80% of the ICU patients and is one of the most common source of morbidity and mortality in the ICU. Inattention is one of the core features of delirium and affects 97% - 100% of the delirium patients. This indicates that attention can be a good parameter for detecting delirium. In literature, it has been found that the use of visual attention tests have been successful in detecting delirium among the ICU patients. The VitalSky is a luminous ceiling used for providing light therapy to the ICU patients. Since, the VitalSky is already available in the ICU and allows to render visual contents, it can be used for detecting delirium in the ICU patients by performing visual attention tests. In this thesis, visual attention tests are developed for the VitalSky. Moreover, different interaction devices are explored that can be used by the ICU patients to respond to the tasks of the attention test and help in automation of the test. The technical feasibility and usability of the automated setup is also evaluated in this thesis. The validation of the performance of the attention tests and user preference of the response devices is assessed by conducting a pilot study on 10 healthy volunteers and collecting their subjective feedback.