The Experts below are selected from a list of 10059 Experts worldwide ranked by ideXlab platform
Jorge Dias - One of the best experts on this subject based on the ideXlab platform.
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Bayesian reasoning for Laban Movement Analysis used in human-machine interaction
International Journal of Reasoning-based Intelligent Systems, 2020Co-Authors: Jörg Rett, Jorge Dias, Juan-manuel AhuactzinAbstract:We present the implementation of computational Laban Movement Analysis (LMA) for human-machine interaction using Bayesian reasoning. The research field of computational human Movement Analysis is lacking a general underlying modelling language, i.e., how to map the features into symbols. With such a semantic descriptor, the recognition problem can be posed as a problem to recognise a sequence of symbols taken from an alphabet consisting of motion-entities. LMA has been proven successful in areas where humans are observing other humans' Movements. LMA provides a model for observation and description and a notational system (Labanotation). To implement LMA in a computer, we have chosen a Bayesian approach. The framework allows us to model the process, learn the dependencies between features and symbols and to perform online classification using LMA-labels. We have chosen the application 'social robots' to demonstrate the feasibility of our solution.
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Computational Laban Movement Analysis using probability calculus
2020Co-Authors: Joerg Rett, Jorge DiasAbstract:This work presents a system which implements the concept of Laban Movement Analysis (LMA) using probability calculus and Bayesian theory. Our Human-InteractionDatabase (HID) provides sequences of position data from several persons performing several Movements in 3-D (magnetic tracker) and 2-D (vision). From the position data a set of low-level features is calculated. Probability calculus is used to relate these low-level features and the frame of reference associated to the variables of LMA. The Bayesian theory provides the concept for calculation, learning and classication.
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HUMAN-ROBOT INTERACTION: INVARIANT 3-D FEATURES FOR LABAN Movement Analysis SHAPE COMPONENT
2020Co-Authors: Jorge DiasAbstract:In the field of human-machine interaction, there are still lacking efficient tools within visual perception of human non-verbal comunication. In this context, some investigation has been conducted within the paradigm of Laban Movement Analysis (LMA) [1‐5]. This work, will explore how visual signals can be processed in order to retrieve useful features which will allow the characterization of Movements in a semantic/intuitive way (e.g. reaching). The LMA shape component will be the main focus of this work, and the implementation will follow the guidelines of previous work. The head and both hands will be tracked within the image, and stereo vision model will be used to retrieve 3-D information of the performer’s pose. From this 3-D data, invariant features will be generated, and used as evidences in a Bayesian framework, which is the selected tool for Laban Movement Analysis implementation. The current work intents to further extend the LMA Bayesian models, towards a full robust descriptor of non-verbal cues for machine interpretation of human behaviour. Results show that the more complete the global Laban Movement Analysis model becomes, better results are achieved, leading to the thought that Laban can provide a good computational Movement classifier.
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Parameterizing interpersonal behaviour with Laban Movement Analysis — A Bayesian approach
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012Co-Authors: Kamrad Khoshhal Roudposhti, Luís Santos, Hadi Aliakbarpour, Jorge DiasAbstract:In this paper we propose a probabilistic model to parameterize human interactive behaviour from human motion. To Support the model taxonomy, we use Laban Movement Analysis (LMA), proposed by Rudolph Laban [11], to characterize human non-verbal communication. In interpersonal communication, body motion carries a lot of meaningful information, useful to analyse group dynamic behaviors in a wide range of social scenarios (e.g. behaviour Analysis of human interpersonal activities and surveillance system). Taking the advantage of interpretation of social signals defined by Alex Pentland [19], and the descriptive body Movement Analysis proposed by Laban, we identified characteristics allowing both works to complement each other. To explore in group dynamics, we attempt to show the existent connections between Pentland's descriptions for Interpersonal Behaviours (IBs), and LMA parameters for human body part motions. Those relations are the keys to characterize the interpersonal communication. Given the uncertainty of the phenomenon, Bayesian's methodology is applied. The results present LMA parameters as reliable indicators for IBs, allowing us to generalize the model.
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CVPR Workshops - Parameterizing interpersonal behaviour with Laban Movement Analysis — A Bayesian approach
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012Co-Authors: Kamrad Khoshhal Roudposhti, Luís Picado Santos, Hadi Aliakbarpour, Jorge DiasAbstract:In this paper we propose a probabilistic model to parameterize human interactive behaviour from human motion. To Support the model taxonomy, we use Laban Movement Analysis (LMA), proposed by Rudolph Laban [11], to characterize human non-verbal communication. In interpersonal communication, body motion carries a lot of meaningful information, useful to analyse group dynamic behaviors in a wide range of social scenarios (e.g. behaviour Analysis of human interpersonal activities and surveillance system). Taking the advantage of interpretation of social signals defined by Alex Pentland [19], and the descriptive body Movement Analysis proposed by Laban, we identified characteristics allowing both works to complement each other. To explore in group dynamics, we attempt to show the existent connections between Pentland's descriptions for Interpersonal Behaviours (IBs), and LMA parameters for human body part motions. Those relations are the keys to characterize the interpersonal communication. Given the uncertainty of the phenomenon, Bayesian's methodology is applied. The results present LMA parameters as reliable indicators for IBs, allowing us to generalize the model.
M.m. Trivedi - One of the best experts on this subject based on the ideXlab platform.
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AVSS - A track-based human Movement Analysis and privacy protection system adaptive to environmental contexts
Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance 2005., 2005Co-Authors: Sangho Park, M.m. TrivediAbstract:This paper presents a track-based system for human Movement Analysis and privacy protection. Our system is adaptive to environmental contexts such as illumination variations, complex moving cast shadows, different camera perspectives, and diverse sue scenarios. Most of outdoor surveillance systems have been targeting at specific environmental situation: i.e., specific time, place, and activity scenarios. We address that more general human Movement Analysis systems should be able to handle multiple heterogeneous situations in an adaptive manner. We introduce the concept of "spatio-temporal personal boundary" to represent different grouping patterns of human tracks, and we incorporate the concept with various site models. Experimental evaluations with extensive outdoor data show our system's robustness to environmental changes and effectiveness to properly handle various environmental contexts.
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A track-based human Movement Analysis and privacy protection system adaptive to environmental contexts
IEEE Conference on Advanced Video and Signal Based Surveillance 2005., 2005Co-Authors: Sangho Park, M.m. TrivediAbstract:This paper presents a track-based system for human Movement Analysis and privacy protection. Our system is adaptive to environmental contexts such as illumination variations, complex moving cast shadows, different camera perspectives, and diverse sue scenarios. Most of outdoor surveillance systems have been targeting at specific environmental situation: i.e., specific time, place, and activity scenarios. We address that more general human Movement Analysis systems should be able to handle multiple heterogeneous situations in an adaptive manner. We introduce the concept of "spatio-temporal personal boundary" to represent different grouping patterns of human tracks, and we incorporate the concept with various site models. Experimental evaluations with extensive outdoor data show our system's robustness to environmental changes and effectiveness to properly handle various environmental contexts.
Santosh Nair - One of the best experts on this subject based on the ideXlab platform.
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3-D eye Movement Analysis.
Behavior Research Methods Instruments & Computers, 2002Co-Authors: Andrew T. Duchowski, Eric Medlin, Nathan Cournia, Anand Gramopadhye, Santosh Nair, Hunter Murphy, Jeenal Vorah, Brian MelloyAbstract:This paper presents a novel three-dimensional (3-D) eye Movement Analysis algorithm for binocular eye tracking within virtualreality (VR). The user’s gaze direction, head position, and orientation are tracked in order to allow recording of the user’s fixations within the environment. Although the linear signal Analysis approach is itself not new, its application to eye Movement Analysis in three dimensions advances traditional two-dimensional approaches, since it takes into account the six degrees of freedom of head Movements and is resolution independent. Results indicate that the 3-D eye Movement Analysis algorithm can successfully be used for Analysis of visual process measures in VR. Process measures not only can corroborate performance measures, but also can lead to discoveries of the reasons for performance improvements. In particular, Analysis of users’ eye Movements in VR can potentially lead to further insights into the underlying cognitive processes of VR subjects.
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ETRA - 3D eye Movement Analysis for VR visual inspection training
Proceedings of the symposium on Eye tracking research & applications - ETRA '02, 2002Co-Authors: Andrew T. Duchowski, Eric Medlin, Brian Melloy, Nathan Cournia, Anand Gramopadhye, Santosh NairAbstract:This paper presents an improved 3D eye Movement Analysis algorithm for binocular eye tracking within Virtual Reality for visual inspection training. The user's gaze direction, head position and orientation are tracked to allow recording of the user's fixations within the environment. The paper summarizes methods for (1) integrating the eye tracker into a Virtual Reality framework, (2) calculating the user's 3D gaze vector, and (3) calibrating the software to estimate the user's inter-pupillary distance post-facto. New techniques are presented for eye Movement Analysis in 3D for improved signal noise suppression. The paper describes (1) the use of Finite Impulse Response (FIR) filters for eye Movement Analysis, (2) the utility of adaptive thresholding and fixation grouping, and (3) a heuristic method to recover lost eye Movement data due to miscalibration. While the linear signal Analysis approach is itself not new, its application to eye Movement Analysis in three dimensions advances traditional 2D approaches since it takes into account the 6 degrees of freedom of head Movements and is resolution independent. Results indicate improved noise suppression over our previous signal Analysis approach.
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3D eye Movement Analysis for VR visual inspection training
Proceedings of the symposium on Eye tracking research & applications - ETRA '02, 2002Co-Authors: Andrew T. Duchowski, Eric Medlin, Brian Melloy, Nathan Cournia, Anand Gramopadhye, Santosh NairAbstract:This paper presents an improved 3D eye Movement Analysis algorithm for binocular eye tracking within Virtual Reality for visual inspection training. The user's gaze direction, head position and orientation are tracked to allow recording of the user's fixations within the environment. The paper summarizes methods for (1) integrating the eye tracker into a Virtual Reality framework, (2) calculating the user's 3D gaze vector, and (3) calibrating the software to estimate the user's inter-pupillary distance post-facto. New techniques are presented for eye Movement Analysis in 3D for improved signal noise suppression. The paper describes (1) the use of Finite Impulse Response (FIR) filters for eye Movement Analysis, (2) the utility of adaptive thresholding and fixation grouping, and (3) a heuristic method to recover lost eye Movement data due to miscalibration. While the linear signal Analysis approach is itself not new, its application to eye Movement Analysis in three dimensions advances traditional 2D approaches since it takes into account the 6 degrees of freedom of head Movements and is resolution independent. Results indicate improved noise suppression over our previous signal Analysis approach.
Luís Santos - One of the best experts on this subject based on the ideXlab platform.
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Parameterizing interpersonal behaviour with Laban Movement Analysis — A Bayesian approach
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012Co-Authors: Kamrad Khoshhal Roudposhti, Luís Santos, Hadi Aliakbarpour, Jorge DiasAbstract:In this paper we propose a probabilistic model to parameterize human interactive behaviour from human motion. To Support the model taxonomy, we use Laban Movement Analysis (LMA), proposed by Rudolph Laban [11], to characterize human non-verbal communication. In interpersonal communication, body motion carries a lot of meaningful information, useful to analyse group dynamic behaviors in a wide range of social scenarios (e.g. behaviour Analysis of human interpersonal activities and surveillance system). Taking the advantage of interpretation of social signals defined by Alex Pentland [19], and the descriptive body Movement Analysis proposed by Laban, we identified characteristics allowing both works to complement each other. To explore in group dynamics, we attempt to show the existent connections between Pentland's descriptions for Interpersonal Behaviours (IBs), and LMA parameters for human body part motions. Those relations are the keys to characterize the interpersonal communication. Given the uncertainty of the phenomenon, Bayesian's methodology is applied. The results present LMA parameters as reliable indicators for IBs, allowing us to generalize the model.
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Motion patterns: Signal interpretation towards the Laban Movement Analysis semantics
IFIP Advances in Information and Communication Technology, 2011Co-Authors: Luís Santos, Jorge DiasAbstract:This work studies the performance of different signal features regarding the qualitative meaning of Laban Movement Analysis semantics. Motion modeling is becoming a prominent scientific area, with research towards multiple applications. The theoretical representation of Movements is a valuable tool when developing such models. One representation growing particular relevance in the community is Laban Movement Analysis (LMA). LMA is a Movement descriptive language which was developed with underlying semantics. Divided in components, its qualities are mostly divided in binomial extreme states. One relevant issue to this problem is the interpretation of signal features into Laban semantics. There are multiple signal processing algorithms for feature generation, each providing different characteristics. We implemented some, covering a range of those measure categories. The results for method comparison are provided in terms of class separability of the LMA space state.
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Human Robot interaction studies on laban human Movement Analysis and dynamic background segmentation
2009 IEEE RSJ International Conference on Intelligent Robots and Systems, 2009Co-Authors: Luís Santos, José Augusto Prado, Jorge DiasAbstract:Human Movement Analysis through vision sensing systems is an important subject regarding Human-Robot interaction. This is a growing area of research, with wide range of applications fields. The ability to recognize human actions using passive sensing modalities, is a decisive factor for machine interaction. In mobile platforms, image processing is regarded as a problem, due to constant changes. We propose an approach, based on Horopter technique, to extract Regions Of Interest (ROI) delimiting human contours. This fact will allow tracking algorithms to provide faster and accurate responses to human feature extraction. The key features are head and both hand positions, that will be tracked within image context. Posterior to feature acquisition, they will be contextualized within a technique, Laban Movement Analysis (LMA) and will be used to provide sets of classifiers. The implementation of the LMA technique will be based on Bayesian Networks. We will use these Bayesian classifiers to label/classify human emotion within the context of expressive Movements. Compared to full image tracking, results improved with the implemented approach, the horopter and consequently so did classification results.
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Laban Movement Analysis for multi-ocular systems
2008 IEEE RSJ International Conference on Intelligent Robots and Systems, 2008Co-Authors: Joerg Rett, Luís Santos, Jorge DiasAbstract:We present as a contribution to the field of human-machine interaction a system that analyzes human Movements online through multiple observers, based on the concept of Laban Movement Analysis (LMA). The implementation uses a Bayesian model for learning and classification, while the results are presented for the application to analyze expressive Movements. In sports like Karate four judges are placed in the corners to observe the fight to ensure that the overall judgment is correct. In this paper we propose a multi-ocular system where each sub-system observes a Movement from a different monocular perspective. The sub-systems send continuously guesses in form of probability distributions to the central system. The central system fuses the evidences and presents the final result. We present the Laban Movement Analysis as a concept to identify useful features of human Movements to classify human actions. The Movements are extracted using both, vision and magnetic tracker. The descriptor opens possibilities towards expressiveness and emotional content. To solve the problem of classification we use the Bayesian framework as it offers an intuitive approach to learning and classification. The presented work targets applications like social robots, smart houses and surveillance.
Andrew T. Duchowski - One of the best experts on this subject based on the ideXlab platform.
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3-D eye Movement Analysis.
Behavior Research Methods Instruments & Computers, 2002Co-Authors: Andrew T. Duchowski, Eric Medlin, Nathan Cournia, Anand Gramopadhye, Santosh Nair, Hunter Murphy, Jeenal Vorah, Brian MelloyAbstract:This paper presents a novel three-dimensional (3-D) eye Movement Analysis algorithm for binocular eye tracking within virtualreality (VR). The user’s gaze direction, head position, and orientation are tracked in order to allow recording of the user’s fixations within the environment. Although the linear signal Analysis approach is itself not new, its application to eye Movement Analysis in three dimensions advances traditional two-dimensional approaches, since it takes into account the six degrees of freedom of head Movements and is resolution independent. Results indicate that the 3-D eye Movement Analysis algorithm can successfully be used for Analysis of visual process measures in VR. Process measures not only can corroborate performance measures, but also can lead to discoveries of the reasons for performance improvements. In particular, Analysis of users’ eye Movements in VR can potentially lead to further insights into the underlying cognitive processes of VR subjects.
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ETRA - 3D eye Movement Analysis for VR visual inspection training
Proceedings of the symposium on Eye tracking research & applications - ETRA '02, 2002Co-Authors: Andrew T. Duchowski, Eric Medlin, Brian Melloy, Nathan Cournia, Anand Gramopadhye, Santosh NairAbstract:This paper presents an improved 3D eye Movement Analysis algorithm for binocular eye tracking within Virtual Reality for visual inspection training. The user's gaze direction, head position and orientation are tracked to allow recording of the user's fixations within the environment. The paper summarizes methods for (1) integrating the eye tracker into a Virtual Reality framework, (2) calculating the user's 3D gaze vector, and (3) calibrating the software to estimate the user's inter-pupillary distance post-facto. New techniques are presented for eye Movement Analysis in 3D for improved signal noise suppression. The paper describes (1) the use of Finite Impulse Response (FIR) filters for eye Movement Analysis, (2) the utility of adaptive thresholding and fixation grouping, and (3) a heuristic method to recover lost eye Movement data due to miscalibration. While the linear signal Analysis approach is itself not new, its application to eye Movement Analysis in three dimensions advances traditional 2D approaches since it takes into account the 6 degrees of freedom of head Movements and is resolution independent. Results indicate improved noise suppression over our previous signal Analysis approach.
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3D eye Movement Analysis for VR visual inspection training
Proceedings of the symposium on Eye tracking research & applications - ETRA '02, 2002Co-Authors: Andrew T. Duchowski, Eric Medlin, Brian Melloy, Nathan Cournia, Anand Gramopadhye, Santosh NairAbstract:This paper presents an improved 3D eye Movement Analysis algorithm for binocular eye tracking within Virtual Reality for visual inspection training. The user's gaze direction, head position and orientation are tracked to allow recording of the user's fixations within the environment. The paper summarizes methods for (1) integrating the eye tracker into a Virtual Reality framework, (2) calculating the user's 3D gaze vector, and (3) calibrating the software to estimate the user's inter-pupillary distance post-facto. New techniques are presented for eye Movement Analysis in 3D for improved signal noise suppression. The paper describes (1) the use of Finite Impulse Response (FIR) filters for eye Movement Analysis, (2) the utility of adaptive thresholding and fixation grouping, and (3) a heuristic method to recover lost eye Movement data due to miscalibration. While the linear signal Analysis approach is itself not new, its application to eye Movement Analysis in three dimensions advances traditional 2D approaches since it takes into account the 6 degrees of freedom of head Movements and is resolution independent. Results indicate improved noise suppression over our previous signal Analysis approach.