Virtual reality (VR) applications support design processes across multiple domains by providing s... more Virtual reality (VR) applications support design processes across multiple domains by providing shared environments in which the designers refine solutions. Given the different needs specific to these domains, the number of VR applications is increasing. Therefore, we propose to support their development by providing a new VR framework based on scenarios. Our VR framework uses scenarios to structure design activities dedicated to collaborative design in VR. The scenarios incorporate a new generic and theoretical collaborative design model that describes the designers' activities based on external representations. The concept of a common object of design is introduced to enable collaborations in VR and the synchronization of the scenarios between the designers. Consequently, the VR Framework enables the configuration of scenarios to create customized and versatile VR collaborative applications that meet the requirements of each stakeholder and domain.
In a co-located collaborative virtual environment, multiple users share the same physical tracked... more In a co-located collaborative virtual environment, multiple users share the same physical tracked space and the same virtual workspace. When the virtual workspace is larger than the real workspace, navigation interaction techniques must be deployed to let the users explore the entire virtual environment. When a user navigates in the virtual space while remaining static in the real space, his/her position in the physical workspace and in the virtual workspace are no longer the same. Thus, in the context where each user is immersed in the virtual environment with a Head-Mounted-Display, a user can still perceive where his/her collaborators are in the virtual environment but not where they are in real world. In this paper, we propose and compare three methods to warn users about the position of collaborators in the shared physical workspace to ensure a proper cohabitation and safety of the collaborators. The first one is based on a virtual grid shaped as a cylinder, the second one is based on a ghost representation of the user and the last one displays the physical safe-navigation space on the floor of the virtual environment. We conducted a user-study with two users wearing a Head-Mounted-Display in the context of a collaborative First-Person-Shooter game. Our three methods were compared with a condition where the physical tracked space was separated into two zones, one per user, to evaluate the impact of each condition on safety, displacement freedom and global satisfaction of users. Results suggest that the ghost avatar and the cylinder grid can be good alternatives to the separation of the tracked space. CCS CONCEPTS • Human-centered computing → Virtual reality; Collaborative interaction; User studies;
How to Recognize Emotions Without Signal Processing
Abstract To recognize emotions from physiological signals, several steps are required: collection... more Abstract To recognize emotions from physiological signals, several steps are required: collection of data during emotion elicitation, signal processing of physiological data, and machine learning. Although this processing chain has shown satisfactory results in prior studies, transferring this solution outside the laboratory context is complex. With the development of wearable devices including physiological sensors, it becomes possible to consider consumer applications. But, to be effective, it is necessary to develop a recognition system able to work potentially in real time on mobile devices. This chapter presents recognition systems based on convolutional neural networks which can be implemented in mobile devices. This solution offers the major advantage of eliminating the signal-processing step. Moreover, a model which is able to predict continuous values of emotion (i.e., valence, arousal, and dominance) was built. The experiments showed an accuracy of 0.76 on these three dimensions.
... Daniela, Ludovic, Cedric, Colin, Guillaume, Mehdi, Lucila et Julie. I really thank you for yo... more ... Daniela, Ludovic, Cedric, Colin, Guillaume, Mehdi, Lucila et Julie. I really thank you for your support, your help and the time spent together. Another special thanks to Sandrine (colleague, friend and office-mate) to be always ready to help me and for the tons of « fraise tagada ...
HAL (Le Centre pour la Communication Scientifique Directe), Oct 6, 2016
Recognition of emotional state is a major challenge for affective computing 1. With the developme... more Recognition of emotional state is a major challenge for affective computing 1. With the development of wearable with physiological sensors, recognition outside the laboratory becomes possible. To build a recognition system of emotion from physiological signals, the most common approach e.g. ,2 is the use of a pipeline as follow: inducing emotion on individuals and collecting data, extracting statistical features with signal processing and detecting specific pattern related to emotion with machine learning algorithms. In this paper, we propose to avoid the signal processing step with deep learning models, especially with convolutional neural network (CNN). Thus, CNN offers the possibility to deal directly with raw data 3. Moreover, in contrary to previous research e.g. ,4 , we chose to predict continuous value of emotional state. This type of output (value on a dimension of emotion-e.g. arousal) seems to be more adapted to human functioning 5 and offers more precision. Method To test the performance of CNN to classify emotional states from physiological data, we used the DEAP database 6. This database contains physiological data from 32 participants who had been exposed to 40 minutes of music video as emotion inducer. We used only four sensors (sampling rate of 128Hz) from DEAP database: Galvanic Skin Response, Respiration Belt, Plethysmograph and Temperature. Indeed, these information can be extracted from actual consumer wearable (e.g. Microsoft Band). Technically, to recognize emotional state, we trained deep models composed of three layers of convolution (with respectively a depth of 32, 64 and 128) and two fully-connected layers. Convolution layers are composed of a convolution, a max pooling and a Relu layer. Fully-connected layers are composed of neural layers, activation functions and dropout. Results To test the accuracy of our models, we split the DEAP database into two datasets: a training dataset with 75% of data and a testing dataset with 25% of data. We tested three models: one by components of emotion: Arousal, Valence and Dominance. Tensorflow was used to construct, train and test our model. Results on testing dataset show accuracy around 76 % for the 3 models. Conclusion Deep learning with CNN seems to be an interesting approach to recognize emotional state without signal processing on data. Moreover, our models offer possibility to predict continuous values on emotion dimensions. The futures works should test this solution on physiological signals extracted from consumer wearable sensors.
HAL (Le Centre pour la Communication Scientifique Directe), Oct 6, 2016
Recognition of emotional state is a major challenge for affective computing 1. With the developme... more Recognition of emotional state is a major challenge for affective computing 1. With the development of wearable with physiological sensors, recognition outside the laboratory becomes possible. To build a recognition system of emotion from physiological signals, the most common approach e.g. ,2 is the use of a pipeline as follow: inducing emotion on individuals and collecting data, extracting statistical features with signal processing and detecting specific pattern related to emotion with machine learning algorithms. In this paper, we propose to avoid the signal processing step with deep learning models, especially with convolutional neural network (CNN). Thus, CNN offers the possibility to deal directly with raw data 3. Moreover, in contrary to previous research e.g. ,4 , we chose to predict continuous value of emotional state. This type of output (value on a dimension of emotion-e.g. arousal) seems to be more adapted to human functioning 5 and offers more precision. Method To test the performance of CNN to classify emotional states from physiological data, we used the DEAP database 6. This database contains physiological data from 32 participants who had been exposed to 40 minutes of music video as emotion inducer. We used only four sensors (sampling rate of 128Hz) from DEAP database: Galvanic Skin Response, Respiration Belt, Plethysmograph and Temperature. Indeed, these information can be extracted from actual consumer wearable (e.g. Microsoft Band). Technically, to recognize emotional state, we trained deep models composed of three layers of convolution (with respectively a depth of 32, 64 and 128) and two fully-connected layers. Convolution layers are composed of a convolution, a max pooling and a Relu layer. Fully-connected layers are composed of neural layers, activation functions and dropout. Results To test the accuracy of our models, we split the DEAP database into two datasets: a training dataset with 75% of data and a testing dataset with 25% of data. We tested three models: one by components of emotion: Arousal, Valence and Dominance. Tensorflow was used to construct, train and test our model. Results on testing dataset show accuracy around 76 % for the 3 models. Conclusion Deep learning with CNN seems to be an interesting approach to recognize emotional state without signal processing on data. Moreover, our models offer possibility to predict continuous values on emotion dimensions. The futures works should test this solution on physiological signals extracted from consumer wearable sensors.
How to Recognize Emotions Without Signal Processing
Neuroergonomics, 2019
Abstract To recognize emotions from physiological signals, several steps are required: collection... more Abstract To recognize emotions from physiological signals, several steps are required: collection of data during emotion elicitation, signal processing of physiological data, and machine learning. Although this processing chain has shown satisfactory results in prior studies, transferring this solution outside the laboratory context is complex. With the development of wearable devices including physiological sensors, it becomes possible to consider consumer applications. But, to be effective, it is necessary to develop a recognition system able to work potentially in real time on mobile devices. This chapter presents recognition systems based on convolutional neural networks which can be implemented in mobile devices. This solution offers the major advantage of eliminating the signal-processing step. Moreover, a model which is able to predict continuous values of emotion (i.e., valence, arousal, and dominance) was built. The experiments showed an accuracy of 0.76 on these three dimensions.
Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020
Virtual Reality (VR) applications are promising solutions in supporting design processes across m... more Virtual Reality (VR) applications are promising solutions in supporting design processes across multiple domains. In complex systems (e.g., machines, cities, interior layouts), VR applications are used alongside Computer Assisted Design (CAD) systems which are (1) rigid (i.e., they lack customization), and (2) limit the design iterations. VR systems need to address these shortcomings so that they can become widespread and adaptable across design domains. We thus propose a new VR Framework based on scenarios and a new generic theoretical design model to assist developers in creating versatile and personalized applications for designers. The new generic theoretical model describes the common design activities shared by many design domains, and the scenario depicts the design model to allow design iterations in VR. Through scenarios, the VR Framework enables creating customized copies of the generic design process to fulfill the needs of each design domain. The customization capability of our solution is illustrated on a use case.
Virtual reality (VR) applications support design processes across multiple domains by providing s... more Virtual reality (VR) applications support design processes across multiple domains by providing shared environments in which the designers refine solutions. Given the different needs specific to these domains, the number of VR applications is increasing. Therefore, we propose to support their development by providing a new VR framework based on scenarios. Our VR framework uses scenarios to structure design activities dedicated to collaborative design in VR. The scenarios incorporate a new generic and theoretical collaborative design model that describes the designers’ activities based on external representations. The concept of a common object of design is introduced to enable collaborations in VR and the synchronization of the scenarios between the designers. Consequently, the VR Framework enables the configuration of scenarios to create customized and versatile VR collaborative applications that meet the requirements of each stakeholder and domain.
Live simulation exercises and computer-based simulations have an important role to play in emerge... more Live simulation exercises and computer-based simulations have an important role to play in emergency management. The needs and requirements of the stakeholders vary according to the different phases of emergency management. In this chapter we discuss how simulation may be used to support the stakeholders in each phase of the emergency. Several technologies exist for developing computer-based simulations. We focus on two technologies that have recently become popular in emergency management: agent-based and virtual reality. Developing a simulator requires understanding how emergency personnel make decisions, how they diagnose a situation, how they communicate and cooperate with other emergency personnel, and how these things are affected by the constantly changing crisis environment. In short, we need to know the cognitive activities of emergency personnel. Based on a cognitive engineering approach, we describe a methodology for developing a computer-based simulator. We show how this...
Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology, 2017
In a co-located collaborative virtual environment, multiple users share the same physical tracked... more In a co-located collaborative virtual environment, multiple users share the same physical tracked space and the same virtual workspace. When the virtual workspace is larger than the real workspace, navigation interaction techniques must be deployed to let the users explore the entire virtual environment. When a user navigates in the virtual space while remaining static in the real space, his/her position in the physical workspace and in the virtual workspace are no longer the same. Thus, in the context where each user is immersed in the virtual environment with a Head-Mounted-Display, a user can still perceive where his/her collaborators are in the virtual environment but not where they are in real world. In this paper, we propose and compare three methods to warn users about the position of collaborators in the shared physical workspace to ensure a proper cohabitation and safety of the collaborators. The first one is based on a virtual grid shaped as a cylinder, the second one is based on a ghost representation of the user and the last one displays the physical safe-navigation space on the floor of the virtual environment. We conducted a user-study with two users wearing a Head-Mounted-Display in the context of a collaborative First-Person-Shooter game. Our three methods were compared with a condition where the physical tracked space was separated into two zones, one per user, to evaluate the impact of each condition on safety, displacement freedom and global satisfaction of users. Results suggest that the ghost avatar and the cylinder grid can be good alternatives to the separation of the tracked space. CCS CONCEPTS • Human-centered computing → Virtual reality; Collaborative interaction; User studies;
Impact of virtual reality headset use on eye blinking and lipid layer thickness
Journal Français d'Ophtalmologie, 2021
PURPOSE Blinking plays an important role in protecting the eyes, and the use of computers has bee... more PURPOSE Blinking plays an important role in protecting the eyes, and the use of computers has been associated with a reduction in the blink rate. The goal of this study was to evaluate the effect of a virtual reality headset on blinking and lipid layer thickness and to compare these data to those associated with a conventional desktop monitor. METHODS Two experiments were performed to compare the effect of 20minutes of use of a virtual reality headset (FOVE) and 20minutes of use of a desktop monitor on the frequency and length of blinks (experiment 1, 15 participants) and on the thickness of the lipid layer as measured by Lipiview (experiment 2, 12 participants). RESULTS In the first experiment, the blink rate [F(1.83)=4.3, P=0.04, β=0.36] and duration [F(1.83)=13, P=0.001, β=0.35] increased with time under both conditions, but no statistical difference was found between the two conditions (headset vs. desktop monitor) either for blink rate [rmANOVA F(1.11)=0.01, P=0.92; headset: 15.1 blinks, 95% CI: 12.6 to 17.6 blinks; desktop: 14.6 blinks, 95% CI: 13.6 to 15.7 blinks] or for blink duration [rmANOVA F(1.11)=4.534, P=0.06; headset: 205.75ms, 95% CI: 200.9 to 210.6ms; desktop: 202.82ms, 95% CI: 198.2 to 207.5ms]. However, strong individual variations were observed. Evaluation of simulator sickness and visual fatigue by questionnaire showed no significant differences between the two conditions (SSQ simulator sickness questionnaire: V=46, P=0.62; VFQ visual fatigue questionnaire: V=15.5, P=0.13). In the second experiment, the lipid layer thickness increased significantly after use of the VR headset [F(1.18)=11.03, P=0.004, headset: 76.2nm, desktop: 58.8nm]. CONCLUSION In terms of recommendations, the effect of virtual reality headsets on blink duration and frequency during a moderate exposure (20minutes) is comparable to that of a conventional desktop monitor. However, the strong individual variations observed, the lack of reliable tests to evaluate this individual sensitivity, and the significant increase in lipid layer thickness in experiment 2 suggest the value of a more detailed investigation, in particular with consideration of a longer exposure time and other tear film parameters.
Driving simulators are increasingly integrated into the training process by driving schools, but ... more Driving simulators are increasingly integrated into the training process by driving schools, but doubts still exist concerning the face validity of such tools, in particular concerning accident prevention. To get some enlighten on the usefulness of this training method we investigate which factors are correlated with driving accidents in virtual training. The factors we decide to consider are: 1) the excessive speed, 2) the violation of safety distance 3) the incapability to predict other drivers' behaviour and 4) the perceived "feeling of presence" that often influences the performances in virtual tasks. The results of our study involving 36 participants pointed out that no statistically significant correlation can be established between any of these factors and the number of cars' accidents during the virtual driving task.
An assessment of a mixed reality environment: Toward an ethnomethodological approach
Simulation & Gaming, 2006
Training firefighters is a difficult process in which emotions and nonverbal behaviors play an im... more Training firefighters is a difficult process in which emotions and nonverbal behaviors play an important role. The authors have developed a mixed reality environment for training a small group of firefighters, which takes into account these aspects. The assessment of the environment was made up of three phases: assessing the virtual agents to gauge their expressiveness, assessing the contextual virtual environment to see if it provides the same decision-making support as that found in the real world, and verifying indexicality to support agent interaction.
Interactions sociales en univers virtuels vers une recherche d’immersion émotionnelle
Pour générer un sentiment d’immersion dans une simulation avec des personnages virtuels, il est f... more Pour générer un sentiment d’immersion dans une simulation avec des personnages virtuels, il est fondamental que ceux ci puissent exprimer un comportement interactionnel et émotionnel réaliste. Notre objectif est de provoquer un sentiment d’immersion sociale et culturelle chez des sujets humains en interaction dans l’univers virtuel en reproduisant des comportements corporels et gestuels dépendant de leurs états émotionnels propres. Une importance particulière a également été accordée à la dimension indexicale de l’interaction notamment à l’usage de gestes de désignation d’objets extérieurs et à la gestion du dialogue entre individus.
Virtual reality (VR) applications support design processes across multiple domains by providing s... more Virtual reality (VR) applications support design processes across multiple domains by providing shared environments in which the designers refine solutions. Given the different needs specific to these domains, the number of VR applications is increasing. Therefore, we propose to support their development by providing a new VR framework based on scenarios. Our VR framework uses scenarios to structure design activities dedicated to collaborative design in VR. The scenarios incorporate a new generic and theoretical collaborative design model that describes the designers' activities based on external representations. The concept of a common object of design is introduced to enable collaborations in VR and the synchronization of the scenarios between the designers. Consequently, the VR Framework enables the configuration of scenarios to create customized and versatile VR collaborative applications that meet the requirements of each stakeholder and domain.
In a co-located collaborative virtual environment, multiple users share the same physical tracked... more In a co-located collaborative virtual environment, multiple users share the same physical tracked space and the same virtual workspace. When the virtual workspace is larger than the real workspace, navigation interaction techniques must be deployed to let the users explore the entire virtual environment. When a user navigates in the virtual space while remaining static in the real space, his/her position in the physical workspace and in the virtual workspace are no longer the same. Thus, in the context where each user is immersed in the virtual environment with a Head-Mounted-Display, a user can still perceive where his/her collaborators are in the virtual environment but not where they are in real world. In this paper, we propose and compare three methods to warn users about the position of collaborators in the shared physical workspace to ensure a proper cohabitation and safety of the collaborators. The first one is based on a virtual grid shaped as a cylinder, the second one is based on a ghost representation of the user and the last one displays the physical safe-navigation space on the floor of the virtual environment. We conducted a user-study with two users wearing a Head-Mounted-Display in the context of a collaborative First-Person-Shooter game. Our three methods were compared with a condition where the physical tracked space was separated into two zones, one per user, to evaluate the impact of each condition on safety, displacement freedom and global satisfaction of users. Results suggest that the ghost avatar and the cylinder grid can be good alternatives to the separation of the tracked space. CCS CONCEPTS • Human-centered computing → Virtual reality; Collaborative interaction; User studies;
How to Recognize Emotions Without Signal Processing
Abstract To recognize emotions from physiological signals, several steps are required: collection... more Abstract To recognize emotions from physiological signals, several steps are required: collection of data during emotion elicitation, signal processing of physiological data, and machine learning. Although this processing chain has shown satisfactory results in prior studies, transferring this solution outside the laboratory context is complex. With the development of wearable devices including physiological sensors, it becomes possible to consider consumer applications. But, to be effective, it is necessary to develop a recognition system able to work potentially in real time on mobile devices. This chapter presents recognition systems based on convolutional neural networks which can be implemented in mobile devices. This solution offers the major advantage of eliminating the signal-processing step. Moreover, a model which is able to predict continuous values of emotion (i.e., valence, arousal, and dominance) was built. The experiments showed an accuracy of 0.76 on these three dimensions.
... Daniela, Ludovic, Cedric, Colin, Guillaume, Mehdi, Lucila et Julie. I really thank you for yo... more ... Daniela, Ludovic, Cedric, Colin, Guillaume, Mehdi, Lucila et Julie. I really thank you for your support, your help and the time spent together. Another special thanks to Sandrine (colleague, friend and office-mate) to be always ready to help me and for the tons of « fraise tagada ...
HAL (Le Centre pour la Communication Scientifique Directe), Oct 6, 2016
Recognition of emotional state is a major challenge for affective computing 1. With the developme... more Recognition of emotional state is a major challenge for affective computing 1. With the development of wearable with physiological sensors, recognition outside the laboratory becomes possible. To build a recognition system of emotion from physiological signals, the most common approach e.g. ,2 is the use of a pipeline as follow: inducing emotion on individuals and collecting data, extracting statistical features with signal processing and detecting specific pattern related to emotion with machine learning algorithms. In this paper, we propose to avoid the signal processing step with deep learning models, especially with convolutional neural network (CNN). Thus, CNN offers the possibility to deal directly with raw data 3. Moreover, in contrary to previous research e.g. ,4 , we chose to predict continuous value of emotional state. This type of output (value on a dimension of emotion-e.g. arousal) seems to be more adapted to human functioning 5 and offers more precision. Method To test the performance of CNN to classify emotional states from physiological data, we used the DEAP database 6. This database contains physiological data from 32 participants who had been exposed to 40 minutes of music video as emotion inducer. We used only four sensors (sampling rate of 128Hz) from DEAP database: Galvanic Skin Response, Respiration Belt, Plethysmograph and Temperature. Indeed, these information can be extracted from actual consumer wearable (e.g. Microsoft Band). Technically, to recognize emotional state, we trained deep models composed of three layers of convolution (with respectively a depth of 32, 64 and 128) and two fully-connected layers. Convolution layers are composed of a convolution, a max pooling and a Relu layer. Fully-connected layers are composed of neural layers, activation functions and dropout. Results To test the accuracy of our models, we split the DEAP database into two datasets: a training dataset with 75% of data and a testing dataset with 25% of data. We tested three models: one by components of emotion: Arousal, Valence and Dominance. Tensorflow was used to construct, train and test our model. Results on testing dataset show accuracy around 76 % for the 3 models. Conclusion Deep learning with CNN seems to be an interesting approach to recognize emotional state without signal processing on data. Moreover, our models offer possibility to predict continuous values on emotion dimensions. The futures works should test this solution on physiological signals extracted from consumer wearable sensors.
HAL (Le Centre pour la Communication Scientifique Directe), Oct 6, 2016
Recognition of emotional state is a major challenge for affective computing 1. With the developme... more Recognition of emotional state is a major challenge for affective computing 1. With the development of wearable with physiological sensors, recognition outside the laboratory becomes possible. To build a recognition system of emotion from physiological signals, the most common approach e.g. ,2 is the use of a pipeline as follow: inducing emotion on individuals and collecting data, extracting statistical features with signal processing and detecting specific pattern related to emotion with machine learning algorithms. In this paper, we propose to avoid the signal processing step with deep learning models, especially with convolutional neural network (CNN). Thus, CNN offers the possibility to deal directly with raw data 3. Moreover, in contrary to previous research e.g. ,4 , we chose to predict continuous value of emotional state. This type of output (value on a dimension of emotion-e.g. arousal) seems to be more adapted to human functioning 5 and offers more precision. Method To test the performance of CNN to classify emotional states from physiological data, we used the DEAP database 6. This database contains physiological data from 32 participants who had been exposed to 40 minutes of music video as emotion inducer. We used only four sensors (sampling rate of 128Hz) from DEAP database: Galvanic Skin Response, Respiration Belt, Plethysmograph and Temperature. Indeed, these information can be extracted from actual consumer wearable (e.g. Microsoft Band). Technically, to recognize emotional state, we trained deep models composed of three layers of convolution (with respectively a depth of 32, 64 and 128) and two fully-connected layers. Convolution layers are composed of a convolution, a max pooling and a Relu layer. Fully-connected layers are composed of neural layers, activation functions and dropout. Results To test the accuracy of our models, we split the DEAP database into two datasets: a training dataset with 75% of data and a testing dataset with 25% of data. We tested three models: one by components of emotion: Arousal, Valence and Dominance. Tensorflow was used to construct, train and test our model. Results on testing dataset show accuracy around 76 % for the 3 models. Conclusion Deep learning with CNN seems to be an interesting approach to recognize emotional state without signal processing on data. Moreover, our models offer possibility to predict continuous values on emotion dimensions. The futures works should test this solution on physiological signals extracted from consumer wearable sensors.
How to Recognize Emotions Without Signal Processing
Neuroergonomics, 2019
Abstract To recognize emotions from physiological signals, several steps are required: collection... more Abstract To recognize emotions from physiological signals, several steps are required: collection of data during emotion elicitation, signal processing of physiological data, and machine learning. Although this processing chain has shown satisfactory results in prior studies, transferring this solution outside the laboratory context is complex. With the development of wearable devices including physiological sensors, it becomes possible to consider consumer applications. But, to be effective, it is necessary to develop a recognition system able to work potentially in real time on mobile devices. This chapter presents recognition systems based on convolutional neural networks which can be implemented in mobile devices. This solution offers the major advantage of eliminating the signal-processing step. Moreover, a model which is able to predict continuous values of emotion (i.e., valence, arousal, and dominance) was built. The experiments showed an accuracy of 0.76 on these three dimensions.
Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020
Virtual Reality (VR) applications are promising solutions in supporting design processes across m... more Virtual Reality (VR) applications are promising solutions in supporting design processes across multiple domains. In complex systems (e.g., machines, cities, interior layouts), VR applications are used alongside Computer Assisted Design (CAD) systems which are (1) rigid (i.e., they lack customization), and (2) limit the design iterations. VR systems need to address these shortcomings so that they can become widespread and adaptable across design domains. We thus propose a new VR Framework based on scenarios and a new generic theoretical design model to assist developers in creating versatile and personalized applications for designers. The new generic theoretical model describes the common design activities shared by many design domains, and the scenario depicts the design model to allow design iterations in VR. Through scenarios, the VR Framework enables creating customized copies of the generic design process to fulfill the needs of each design domain. The customization capability of our solution is illustrated on a use case.
Virtual reality (VR) applications support design processes across multiple domains by providing s... more Virtual reality (VR) applications support design processes across multiple domains by providing shared environments in which the designers refine solutions. Given the different needs specific to these domains, the number of VR applications is increasing. Therefore, we propose to support their development by providing a new VR framework based on scenarios. Our VR framework uses scenarios to structure design activities dedicated to collaborative design in VR. The scenarios incorporate a new generic and theoretical collaborative design model that describes the designers’ activities based on external representations. The concept of a common object of design is introduced to enable collaborations in VR and the synchronization of the scenarios between the designers. Consequently, the VR Framework enables the configuration of scenarios to create customized and versatile VR collaborative applications that meet the requirements of each stakeholder and domain.
Live simulation exercises and computer-based simulations have an important role to play in emerge... more Live simulation exercises and computer-based simulations have an important role to play in emergency management. The needs and requirements of the stakeholders vary according to the different phases of emergency management. In this chapter we discuss how simulation may be used to support the stakeholders in each phase of the emergency. Several technologies exist for developing computer-based simulations. We focus on two technologies that have recently become popular in emergency management: agent-based and virtual reality. Developing a simulator requires understanding how emergency personnel make decisions, how they diagnose a situation, how they communicate and cooperate with other emergency personnel, and how these things are affected by the constantly changing crisis environment. In short, we need to know the cognitive activities of emergency personnel. Based on a cognitive engineering approach, we describe a methodology for developing a computer-based simulator. We show how this...
Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology, 2017
In a co-located collaborative virtual environment, multiple users share the same physical tracked... more In a co-located collaborative virtual environment, multiple users share the same physical tracked space and the same virtual workspace. When the virtual workspace is larger than the real workspace, navigation interaction techniques must be deployed to let the users explore the entire virtual environment. When a user navigates in the virtual space while remaining static in the real space, his/her position in the physical workspace and in the virtual workspace are no longer the same. Thus, in the context where each user is immersed in the virtual environment with a Head-Mounted-Display, a user can still perceive where his/her collaborators are in the virtual environment but not where they are in real world. In this paper, we propose and compare three methods to warn users about the position of collaborators in the shared physical workspace to ensure a proper cohabitation and safety of the collaborators. The first one is based on a virtual grid shaped as a cylinder, the second one is based on a ghost representation of the user and the last one displays the physical safe-navigation space on the floor of the virtual environment. We conducted a user-study with two users wearing a Head-Mounted-Display in the context of a collaborative First-Person-Shooter game. Our three methods were compared with a condition where the physical tracked space was separated into two zones, one per user, to evaluate the impact of each condition on safety, displacement freedom and global satisfaction of users. Results suggest that the ghost avatar and the cylinder grid can be good alternatives to the separation of the tracked space. CCS CONCEPTS • Human-centered computing → Virtual reality; Collaborative interaction; User studies;
Impact of virtual reality headset use on eye blinking and lipid layer thickness
Journal Français d'Ophtalmologie, 2021
PURPOSE Blinking plays an important role in protecting the eyes, and the use of computers has bee... more PURPOSE Blinking plays an important role in protecting the eyes, and the use of computers has been associated with a reduction in the blink rate. The goal of this study was to evaluate the effect of a virtual reality headset on blinking and lipid layer thickness and to compare these data to those associated with a conventional desktop monitor. METHODS Two experiments were performed to compare the effect of 20minutes of use of a virtual reality headset (FOVE) and 20minutes of use of a desktop monitor on the frequency and length of blinks (experiment 1, 15 participants) and on the thickness of the lipid layer as measured by Lipiview (experiment 2, 12 participants). RESULTS In the first experiment, the blink rate [F(1.83)=4.3, P=0.04, β=0.36] and duration [F(1.83)=13, P=0.001, β=0.35] increased with time under both conditions, but no statistical difference was found between the two conditions (headset vs. desktop monitor) either for blink rate [rmANOVA F(1.11)=0.01, P=0.92; headset: 15.1 blinks, 95% CI: 12.6 to 17.6 blinks; desktop: 14.6 blinks, 95% CI: 13.6 to 15.7 blinks] or for blink duration [rmANOVA F(1.11)=4.534, P=0.06; headset: 205.75ms, 95% CI: 200.9 to 210.6ms; desktop: 202.82ms, 95% CI: 198.2 to 207.5ms]. However, strong individual variations were observed. Evaluation of simulator sickness and visual fatigue by questionnaire showed no significant differences between the two conditions (SSQ simulator sickness questionnaire: V=46, P=0.62; VFQ visual fatigue questionnaire: V=15.5, P=0.13). In the second experiment, the lipid layer thickness increased significantly after use of the VR headset [F(1.18)=11.03, P=0.004, headset: 76.2nm, desktop: 58.8nm]. CONCLUSION In terms of recommendations, the effect of virtual reality headsets on blink duration and frequency during a moderate exposure (20minutes) is comparable to that of a conventional desktop monitor. However, the strong individual variations observed, the lack of reliable tests to evaluate this individual sensitivity, and the significant increase in lipid layer thickness in experiment 2 suggest the value of a more detailed investigation, in particular with consideration of a longer exposure time and other tear film parameters.
Driving simulators are increasingly integrated into the training process by driving schools, but ... more Driving simulators are increasingly integrated into the training process by driving schools, but doubts still exist concerning the face validity of such tools, in particular concerning accident prevention. To get some enlighten on the usefulness of this training method we investigate which factors are correlated with driving accidents in virtual training. The factors we decide to consider are: 1) the excessive speed, 2) the violation of safety distance 3) the incapability to predict other drivers' behaviour and 4) the perceived "feeling of presence" that often influences the performances in virtual tasks. The results of our study involving 36 participants pointed out that no statistically significant correlation can be established between any of these factors and the number of cars' accidents during the virtual driving task.
An assessment of a mixed reality environment: Toward an ethnomethodological approach
Simulation & Gaming, 2006
Training firefighters is a difficult process in which emotions and nonverbal behaviors play an im... more Training firefighters is a difficult process in which emotions and nonverbal behaviors play an important role. The authors have developed a mixed reality environment for training a small group of firefighters, which takes into account these aspects. The assessment of the environment was made up of three phases: assessing the virtual agents to gauge their expressiveness, assessing the contextual virtual environment to see if it provides the same decision-making support as that found in the real world, and verifying indexicality to support agent interaction.
Interactions sociales en univers virtuels vers une recherche d’immersion émotionnelle
Pour générer un sentiment d’immersion dans une simulation avec des personnages virtuels, il est f... more Pour générer un sentiment d’immersion dans une simulation avec des personnages virtuels, il est fondamental que ceux ci puissent exprimer un comportement interactionnel et émotionnel réaliste. Notre objectif est de provoquer un sentiment d’immersion sociale et culturelle chez des sujets humains en interaction dans l’univers virtuel en reproduisant des comportements corporels et gestuels dépendant de leurs états émotionnels propres. Une importance particulière a également été accordée à la dimension indexicale de l’interaction notamment à l’usage de gestes de désignation d’objets extérieurs et à la gestion du dialogue entre individus.
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