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Pain assessment can benefit from observation of pain behaviors, such as guarding or facial expression, and observational pain scales are widely used in clinical practice with nonverbal patients. However, little is known about head... more
Pain assessment can benefit from observation of pain behaviors, such as guarding or facial expression, and observational pain scales are widely used in clinical practice with nonverbal patients. However, little is known about head movements and postures in the context of pain. In this regard, we analyze videos of three publically available datasets. The BioVid dataset was recorded with healthy participants subjected to painful heat stimuli. In the BP4D dataset, healthy participants performed a cold-pressor test and several other tasks (meant to elicit emotion). The UNBC dataset videos show shoulder pain patients during range-of-motion tests to their affected and unaffected limbs. In all videos, participants were sitting in an upright position. We studied head movements and postures that occurred during the painful and control trials by measuring head orientation from video over time, followed by analyzing posture and movement summary statistics and occurrence frequencies of typical ...
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Research Interests:
Research Interests:
ABSTRACT Together with classification of facial expressions, the rating of their intensities is of major interest. Classical supervised learning techniques require labeling of the intensities, which is labor intensive and requires expert... more
ABSTRACT Together with classification of facial expressions, the rating of their intensities is of major interest. Classical supervised learning techniques require labeling of the intensities, which is labor intensive and requires expert knowledge, but nevertheless is not guaranteed to be objective. We propose a new approach to learn an intensity rating function which does not require expert knowledge, because it simplifies the labeling task by avoiding the difficulty of selecting an absolute intensity value and to keep the labeling consistent for the whole dataset. It is based on a novel kind of ground truth which we call Comparative Labeling. It specifies sample pairs for which the first element is desired to have a lower intensity than the second. We introduce a learning scheme to find an optimal intensity function in respect of the Comparative Labeling and propose performance measures to assess the quality of the learned function. The technique is applied to rate the intensity of facial expressions of posed pain. The evaluation results show that the learned function is well suited for determining dynamic intensity variation over time. We also assess the suitability of the rating as an inter-individual intensity measure by comparing it to the intensity ratings given by human observers.
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ABSTRACT Automatic pain recognition can improve medical treatment, especially when the patient is not able to utter on his pain experience. Facial expressions with their intensities and dynamics contain valuable information for... more
ABSTRACT Automatic pain recognition can improve medical treatment, especially when the patient is not able to utter on his pain experience. Facial expressions with their intensities and dynamics contain valuable information for recognising pain. We propose a concept for distinguishing facial expressions of pain from others and assessing the pain expression intensity. It is based on a Support Vector Machine (SVM) classifier and a function model for intensity rating. The intensity model is trained using Comparative Learning, a new technique that simplifies labelling of the data. Using a database of 3D posed pain sequences we show the suitability of the concept to recognise pain expressions, distinguish different intensities and spot even slight intensity changes in its temporal context.
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ABSTRACT Real-time 3D reconstruction is a hot topic in current research. Several popular approaches are based on the truncated signed distance function (TSDF), a volumetric scene representation that allows for integration of multiple... more
ABSTRACT Real-time 3D reconstruction is a hot topic in current research. Several popular approaches are based on the truncated signed distance function (TSDF), a volumetric scene representation that allows for integration of multiple depth images taken from different viewpoints. Aiming at a deeper understanding of TSDF we discuss its parameters, conduct experiments on the influence of voxel size on reconstruction accuracy and derive practical recommendations.
ABSTRACT Since its release in late 2010 the Microsoft Kinect depth sensor has boosted real time gesture recognition and new man-machine interaction endeavors in the computer vision community. Based on depth image data, in this paper we... more
ABSTRACT Since its release in late 2010 the Microsoft Kinect depth sensor has boosted real time gesture recognition and new man-machine interaction endeavors in the computer vision community. Based on depth image data, in this paper we propose an accurate, fast and robust face pose estimation approach, which for example can be of interest for user behavior analysis, or be of use as a means of man machine interaction modality. In our method we apply the depth sensor to create a user specific model which is fitted with an Iterative Closest Point algorithm. This model consists of point vertices and surface normals. In the fitting procedure we employ the normal vectors for the minimization of distances between the model and the measured point cloud. As the experimental results show, our method is precise, fast and robust in case of strong head rotation, even during facial expression and partial face occlusion.