An Architecture for Personality-based, Nonverbal Behavior in Affective Virtual Humanoid Character

Saberi, Maryam; Bernardet, Ulysses; DiPaola, Steve

doi:10.1016/j.procs.2014.11.104

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…Gestures are a type of nonverbal movements used to convey an explicit message or accompany a verbal message. Gestures not only express a command or an instruction but a specific execution of the gesture can be an indicator of the person's psychophysiological states [57]. Although the use of nonverbal cues such as gestures make the interaction simpler and natural [58], culturally diverse gesture styles and the variety of words and pointing gestures used to indicate the same objects makes gesture recognition a challenge in ASA interaction.…”

Section: Interaction Through Gestures and Facial Expressionmentioning

confidence: 99%

When Agents Become Partners: A Review of the Role the Implicit Plays in the Interaction with Artificial Social Agents

Dar

Bernardet

2020

MTI

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The way we interact with computers has significantly changed over recent decades. However, interaction with computers still falls behind human to human interaction in terms of seamlessness, effortlessness, and satisfaction. We argue that simultaneously using verbal, nonverbal, explicit, implicit, intentional, and unintentional communication channels addresses these three aspects of the interaction process. To better understand what has been done in the field of Human Computer Interaction (HCI) in terms of incorporating the type channels mentioned above, we reviewed the literature on implicit nonverbal interaction with a specific emphasis on the interaction between humans on the one side, and robot and virtual humans on the other side. These Artificial Social Agents (ASA) are increasingly used as advanced tools for solving not only physical but also social tasks. In the literature review, we identify domains of interaction between humans and artificial social agents that have shown exponential growth over the years. The review highlights the value of incorporating implicit interaction capabilities in Human Agent Interaction (HAI) which we believe will lead to satisfying human and artificial social agent team performance. We conclude the article by presenting a case study of a system that harnesses subtle nonverbal, implicit interaction to increase the state of relaxation in users. This “Virtual Human Breathing Relaxation System” works on the principle of physiological synchronisation between a human and a virtual, computer-generated human. The active entrainment concept behind the relaxation system is generic and can be applied to other human agent interaction domains of implicit physiology-based interaction.

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Section: Interaction Through Gestures and Facial Expressionmentioning

confidence: 99%

When Agents Become Partners: A Review of the Role the Implicit Plays in the Interaction with Artificial Social Agents

Dar

Bernardet

2020

MTI

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“…Currently, the following sensors are supported: all native YARP devices (serial, video, audio, etc. ), Microsoft Kinect (version 1 and 2), Leap Motion 17 , AnTS Overheard tracking [3], several motion capture systems (organic motion 17 http://leapmotion.com 21 , MAX (cycling74.com/products/max/), large-scale neuronal system simulator iqr 22 [4], and openFrameworks. 18 http://credo-interactive.com 19 http://unrealengine.com 20 http://mathworks.com…”

Section: M+m Componentsmentioning

confidence: 99%

“…[8]. In the concrete case elaborated here, the system is used to develop a biologically and psychologically grounded cognitive architecture for the control of nonverbal behavior of a virtual humanoid character during dynamic interactions with human users [21]. Figure 4 illustrates how the scenario integrates heterogeneous sensing and data processing with state-of-the art virtual human technology, and psychology and cognitive science grounded control models.…”

Section: System For Real-time Real-world Interaction Between Humans mentioning

confidence: 99%

m+m

Bernardet

Adhia²,

Jaffe³

et al. 2016

Proceedings of the 3rd International Symposium on Movement and Computing

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Embodied interaction has the potential to provide users with uniquely engaging and meaningful experiences. m+m: Movement + Meaning middleware is an open source software framework that enables users to construct real-time, interactive systems that are based on movement data. The acquisition, processing, and rendering of movement data can be local or distributed, real-time or off-line. Key features of the m+m middleware are a small footprint in terms of computational resources, portability between different platforms, and high performance in terms of reduced latency and increased bandwidth. Examples of systems that can be built with m+m as the internal communication middleware include those for the semantic interpretation of human movement data, machine-learning models for movement recognition, and the mapping of movement data as a controller for online navigation, collaboration, and distributed performance.

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“…In this manuscript, we present the "RealAct" (Saberi et al, 2015) model of personality-based behaviour regulation with the focus on the empirical evaluation of the model in realtime interaction between a simulated "Virtual Human" and a participant. In its current form, the model is purely non-verbal, meaning that it does not produce any verbal output or take any verbal input into account.…”

Section: Introductionmentioning

confidence: 99%

Expressing Personality Through Non-verbal Behaviour in Real-Time Interaction

2021

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The attribution of traits plays an important role as a heuristic for how we interact with others. Many psychological models of personality are analytical in that they derive a classification from reported or hypothesised behaviour. In the work presented here, we follow the opposite approach: Our personality model generates behaviour that leads an observer to attribute personality characteristics to the actor. Concretely, the model controls all relevant aspects of non-verbal behaviour such as gaze, facial expression, gesture, and posture. The model, embodied in a virtual human, affords to realistically interact with participants in real-time. Conceptually, our model focuses on the two dimensions of extra/introversion and stability/neuroticism. In the model, personality parameters influence both, the internal affective state as well as the characteristic of the behaviour execution. Importantly, the parameters of the model are based on empirical findings in the behavioural sciences. To evaluate our model, we conducted two types of studies. Firstly, passive experiments where participants rated videos showing variants of behaviour driven by different personality parameter configurations. Secondly, presential experiments where participants interacted with the virtual human, playing rounds of the Rock-Paper-Scissors game. Our results show that the model is effective in conveying the impression of the personality of a virtual character to users. Embodying the model in an artificial social agent capable of real-time interactive behaviour is the only way to move from an analytical to a generative approach to understanding personality, and we believe that this methodology raises a host of novel research questions in the field of personality theory.

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An Architecture for Personality-based, Nonverbal Behavior in Affective Virtual Humanoid Character

Cited by 12 publications

References 21 publications

When Agents Become Partners: A Review of the Role the Implicit Plays in the Interaction with Artificial Social Agents

When Agents Become Partners: A Review of the Role the Implicit Plays in the Interaction with Artificial Social Agents

m+m

Expressing Personality Through Non-verbal Behaviour in Real-Time Interaction

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