Humanoid Avatars Research Articles

Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction.

Modern digital anthropometry applications utilize smartphone cameras to rapidly construct three-dimensional humanoid avatars, quantify relevant anthropometric variables, and estimate body composition. In the present study, 131 participants ([73 M, 58 F] age 33.7 ± 16.0 y; BMI 27.3 ± 5.9 kg/m2, body fat 29.9 ± 9.9%) had their body composition assessed using dual-energy X-ray absorptiometry (DXA) and a smartphone 3D scanning application using non-rigid avatar reconstruction. The performance of two new body fat % estimation equations was evaluated through reliability and validity statistics, Bland-Altman analysis, and equivalence testing. In the reliability analysis, the technical error of the measurement and intraclass correlation coefficient were 0.5-0.7% and 0.996-0.997, respectively. Both estimation equations demonstrated statistical equivalence with DXA based on ±2% equivalence regions and strong linear relationships (Pearson's r 0.90; concordance correlation coefficient 0.89-0.90). Across equations, mean absolute error and standard error of the estimate values were ~ 3.5% and ~ 4.2%, respectively. No proportional bias was observed. While continual advances are likely, smartphone-based 3D scanning may now be suitable for implementation for rapid and accessible body measurement in a variety of applications.

Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling.

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient body composition analysis and metabolic disease risk stratification in clinical settings. Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software. Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%Δ ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05-0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements. 3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings. Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software. Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements. 3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Accurate Prediction of 3D Humanoid Avatars for Anthropometric Modeling

Accurate Prediction of 3D Humanoid Avatars for Anthropometric Modeling

Virtual Agents in Immersive Virtual Reality Environments: Impact of Humanoid Avatars and Output Modalities on Shopping Experience

Embodied virtual agents (EVAs) are used in various online shopping scenarios to enhance consumer experience. Humanoid avatars and output modalities of EVAs are two important factors affecting online shopping experience, which have been widely studied in shopping websites before. Moreover, immersive virtual reality (IVR) shopping is a future trend in online shopping, but research on EVAs in IVR environments is limited. The impact of them in an IVR shopping environment is not yet fully understood. Therefore, this study aims to investigate the effect of an EVA's humanoid avatar and output modalities on the online shopping experience in an IVR environment. First, 66 participants were invited to participate in a 2 (humanoid avatar: presence vs. absence) × 3 (output modalities: text-only, voice-only, and text and voice) within-group experiment to complete the specified purchase task. After each purchase task was completed, participants were required to fill in a subjective questionnaire to score the six different combinations of virtual agents (VAs). After all purchase tasks were completed, a follow-up interview was conducted to better obtain the participants’ preferences and detailed reasons for the six different combinations of VAs. The results showed that humanoid avatars significantly enhanced participants’ perceptions of warmth, communication, trust, and satisfaction. Additionally, output modalities significantly enhanced participants’ perception of warmth, communication, trust, comfort, and satisfaction. Finally, the humanoid avatar and output modalities had significant interactions in terms of comfort. The results of this research provide theoretical reference and guidance for the design of VAs in IVR shopping environment, and also provide inspiration for the design and research of VAs in other online shopping environments.

Emergence of the obesity epidemic: 6-decade visualization with humanoid avatars

Visualizations of the emerging obesity epidemic, such as with serial US color prevalence maps, provide graphic images that extend informative public health messages beyond those in written communications. Advances in low-cost 3D optical technology now allow for development of large image databases that include participants varying in race/ethnicity, body mass, height, age, and circumferences. When combined with contemporary statistical methods, these data sets can be used to create humanoid avatar images with prespecified anthropometric features. The current study aimed to develop a humanoid avatar series with characteristics of representative US adults extending over the past 6 decades. 3D optical scans were conducted on a demographically diverse sample of 570 healthy adults. Image data were converted to principal components and manifold regression equations were then developed with body mass, height, age, and waist circumference as covariates. Humanoid avatars were generated for representative adults with these 4 characteristics as reported in CDC surveys beginning in 1960-1962 up to 2015-2018. There was a curvilinear increase in adult US population body mass, waist circumference, and BMI in males and females across the 9 surveys spanning 6 decades. A small increase in average adult population age was present between 1960 and 2018; height changes were inconsistent. A series of 4 avatars developed at ∼20-y intervals for representative males and females reveal the changes in body size and shape consistent with the emergence of the obesity epidemic. An additional series of developed avatars portray the shapes and sizes of males and females at key BMI cutoffs. New mathematical approaches and accessible 3D optical technology combined with increasingly available large and diverse data sets across the life span now make unique visualization of body size and shape possible on a previously unattainable scale. This study is registered at https://clinicaltrials.gov/ct2/show/NCT03637855 as NCT03637855.

The effects of body direction and posture on taking the perspective of a humanoid avatar in a virtual environment.

Visual perspective taking is inferring how the world looks to another person. To clarify this process, we investigated whether employing a humanoid avatar as the viewpoint would facilitate an imagined perspective shift in a virtual environment, and which factor of the avatar is effective for the facilitation effect. We used a task that involved reporting how an object looks by a simple direction judgment, either from the avatar’s position or from the position of an empty chair. We found that the humanoid avatar’s presence improved task performance. Furthermore, the avatar’s facilitation effect was observed only when the avatar was facing the visual stimulus to be judged; performance was worse when it faced backwards than when there was only an empty chair facing forwards. This suggests that the avatar does not simply attract spatial attention, but the posture of the avatar is crucial for the facilitation effect. In addition, when the directions of the head and the torso were opposite (i.e., an impossible posture), the avatar’s facilitation effect disappeared. Thus, visual perspective taking might not be facilitated by the avatar when its posture is biomechanically impossible because we cannot embody it. Finally, even when the avatar’s head of the possible posture was covered with a bucket, the facilitation effect was found with the forward-facing avatar rather than the backward-facing avatar. That is, the head/gaze direction cue, or presumably the belief that the visual stimulus to be judged can be seen by the avatar, was not required. These results suggest that explicit perspective taking is facilitated by embodiment towards humanoid avatars.

Re]moving Bodies - A Shared Diminished Reality Installation for Exploring Relational Movement.

In this article we explore an epistemic approach we name dis/embodiment and introduce “Articulations,” an interdisciplinary project bringing together Virtual Reality (VR) designers, cognitive scientists, dancers, anthropologists, and human–machine interaction specialists. According to Erin Manning, our sense of self and other emerges from processes of bodying and relational movement (becoming oneself by moving in relation with the world). The aim of the project is to exploit the potential of multi-person VR in order to explore the intersubjective dynamics of relational movement and bodying, and to do so with scientific, artistic and therapeutic purposes in mind. To achieve this bridge, we bring up a novel paradigm we name “Shared Diminished Reality”. It consists in using minimalist representation to instantiate users’ bodies in the virtual space. Instead of using humanoid avatars or full body skeletons, we reduce the representation of the moving bodies to three spheres whose trajectories reflect the tracking of the head and the two wrists. This “diminished”virtual rendition of the body-in-movement, we call dis/embodiment. It provides a simple but clear experience of one’s own responsive movement in relation to the world and other bodies. It also allows for subtle manipulations of bodies’ perceptual and cross-perceptual feedback and simplifies the tracking and the analysis of movements. After having introduced the epistemic framework, the basic architecture, and the empirical method informing the installation, we present and discuss, as a proof-of-concept, some data collected in a situated experiment at a science-art event. We investigate motion patterns observed in different experimental conditions (in which participants either could or could not see the representation of their own hands in the virtual space) and their relation with subjective reports collected. We conclude with reflection on further possibilities of our installation in exploring bodying and relational movement.

Despite Appearances: Comparing Emotion Recognition in Abstract and Humanoid Avatars Using Nonverbal Behavior in Social Virtual Reality

The ability to perceive emotional states is a critical part of social interactions, shaping how people understand and respond to each other. In face-to-face communication, people perceive others’ emotions through observing their appearance and behavior. In virtual reality, how appearance and behavior are rendered must be designed. In this study, we asked whether people conversing in immersive virtual reality (VR) would perceive emotion more accurately depending on whether they and their partner were represented by realistic or abstract avatars. In both cases, participants got similar information about the tracked movement of their partners’ heads and hands, though how this information was expressed varied. We collected participants’ self-reported emotional state ratings of themselves and their ratings of their conversational partners’ emotional states after a conversation in VR. Participants’ ratings of their partners’ emotional states correlated to their partners’ self-reported ratings regardless of which of the avatar conditions they experienced. We then explored how these states were reflected in their nonverbal behavior, using a dyadic measure of nonverbal behavior (proximity between conversational partners) and an individual measure (expansiveness of gesture). We discuss how this relates to measures of social presence and social closeness.

Contagious yawning in virtual reality is affected by actual, but not simulated, social presence.

Contagious yawning occurs in humans and a few other highly social animals following the detection of yawns in others, yet the factors influencing the propagation of this response remain largely unknown. Stemming from earlier laboratory research, we conducted five experiments to investigate the effects of social presence on contagious yawning in virtual reality (VR). We show that, similar to a traditional laboratory setting, having a researcher present during testing significantly inhibited contagious yawning in VR, even though participants were viewing a virtual environment and unable to see the researcher. Unlike previous research, however, manipulating the social presence in VR (i.e., embedding recording devices and humanoid avatars within the simulation) did not affect contagious yawning. These experiments provide further evidence that social presence is a powerful deterrent of yawning in humans, which warrants further investigation. More generally, these findings also have important applications for the use of VR in psychological research. While participants were quite sensitive to social stimuli presented in VR, as evidenced by contagious yawning, our results suggest a major difference in the influence of social factors within real-world and virtual environments. That is, social cues in actual reality appear to dominate and supersede those in VR.

Experiences of a Motivational Interview Delivered by a Robot: Qualitative Study

BackgroundMotivational interviewing is an effective intervention for supporting behavior change but traditionally depends on face-to-face dialogue with a human counselor. This study addressed a key challenge for the goal of developing social robotic motivational interviewers: creating an interview protocol, within the constraints of current artificial intelligence, which participants will find engaging and helpful.ObjectiveThe aim of this study was to explore participants’ qualitative experiences of a motivational interview delivered by a social robot, including their evaluation of usability of the robot during the interaction and its impact on their motivation.MethodsNAO robots are humanoid, child-sized social robots. We programmed a NAO robot with Choregraphe software to deliver a scripted motivational interview focused on increasing physical activity. The interview was designed to be comprehensible even without an empathetic response from the robot. Robot breathing and face-tracking functions were used to give an impression of attentiveness. A total of 20 participants took part in the robot-delivered motivational interview and evaluated it after 1 week by responding to a series of written open-ended questions. Each participant was left alone to speak aloud with the robot, advancing through a series of questions by tapping the robot’s head sensor. Evaluations were content-analyzed utilizing Boyatzis’ steps: (1) sampling and design, (2) developing themes and codes, and (3) validating and applying the codes.ResultsThemes focused on interaction with the robot, motivation, change in physical activity, and overall evaluation of the intervention. Participants found the instructions clear and the navigation easy to use. Most enjoyed the interaction but also found it was restricted by the lack of individualized response from the robot. Many positively appraised the nonjudgmental aspect of the interview and how it gave space to articulate their motivation for change. Some participants felt that the intervention increased their physical activity levels.ConclusionsSocial robots can achieve a fundamental objective of motivational interviewing, encouraging participants to articulate their goals and dilemmas aloud. Because they are perceived as nonjudgmental, robots may have advantages over more humanoid avatars for delivering virtual support for behavioral change.

Implementing Advanced Characteristics of X3D Collaborative Virtual Environments for Supporting e-Learning

Three dimensional Collaborative Virtual Environments are a powerful form of collaborative telecommunication applications, enabling the users to share a common three-dimensional space and interact with each other as well as with the environment surrounding them, in order to collaboratively solve problems or aid learning processes. Such an environment is “EVE Training Area tool” which is supported by “EVE platform”. This tool is a three-dimensional space where participants, represented by three-dimensional humanoid avatars, can use a variety of e-collaboration tools. This paper presents advanced functionality that has been integrated on “EVE Training Area tool” in order to support: (a) multiple collaborative learning techniques (b) Spatial audio conferencing, which is targeted to support principle 3 (augmenting user's representation and awareness). Furthermore the paper presents technological and implementation issues concerning the evolution of “EVE platform” in order to support this functionality.

Human Tails: Ownership and Control of Extended Humanoid Avatars

This paper explores body ownership and control of an 'extended' humanoid avatar that features a distinct and flexible tail-like appendage protruding from its coccyx. Thirty-two participants took part in a between-groups study to puppeteer the avatar in an immersive CAVE™ -like system. Participants' body movement was tracked, and the avatar's humanoid body synchronously reflected this motion. However, sixteen participants experienced the avatar's tail moving around randomly and asynchronous to their own movement, while the other participants experienced a tail that they could, potentially, control accurately and synchronously through hip movement. Participants in the synchronous condition experienced a higher degree of body ownership and agency, suggesting that visuomotor synchrony enhanced the probability of ownership over the avatar body despite of its extra-human form. Participants experiencing body ownership were also more likely to be more anxious and attempt to avoid virtual threats to the tail and body. The higher task performance of participants in the synchronous condition indicates that people are able to quickly learn how to remap normal degrees of bodily freedom in order to control virtual bodies that differ from the humanoid form. We discuss the implications and applications of extended humanoid avatars as a method for exploring the plasticity of the brain's representation of the body and for gestural human-computer interfaces.

Emotion Walking for Humanoid Avatars Using Brain Signals

Interaction between humans and humanoid avatar representations is very important in virtual reality and robotics, since the humanoid avatar can represent either a human or a robot in a virtual environment. Many researchers have focused on providing natural interactions for humanoid avatars or even for robots with the use of camera tracking, gloves, giving them the ability to speak, brain interfaces and other devices. This paper provides a new multimodal interaction control for avatars by combining brain signals, facial muscle tension recognition and glove tracking to change the facial expression of humanoid avatars according to the user's emotional condition. The signals from brain activity and muscle movements are used as the emotional stimulator, while the glove acts as emotion intensity control for the avatar. This multimodal interface can determine when the humanoid avatar needs to change their facial expression or their walking power. The results show that humanoid avatar have different timelines of walking and facial expressions when the user stimulates them with different emotions. This finding is believed to provide new knowledge on controlling robots' and humanoid avatars' facial expressions and walking.

Effects of Interactive Real‐Time Simulations and Humanoid Avatars on Consumers’ Responses in Online House Products Marketing

AbstractInteractive product presentation techniques have recently gained importance in online marketing of house products. Real‐time simulation offers a high level interactive presentation with more powerful features to present product functions and variations. This article presents a novel computational approach for engaging and supporting consumers in making informed choices about purchasing house products. It involved the development and testing of an online interactive real‐time simulation for house product marketing (the Virtual House Showroom‐VHS). This model also included optional humanoid avatars, representing a virtual buyer–seller experience. The research assessed the effectiveness of the VHS on consumers’ cognitive and affective responses. In particular, it examined whether consumers’ responses to real‐time simulation with a humanoid avatar are significantly different from their responses to the model without the avatar. Participants (n = 110) were organized into two groups. Group 1 (n = 56) evaluated VHS without the humanoid avatar and group 2 (n = 54) appraised the model with the avatar. The results confirmed that interactive online real‐time simulation technologies without the avatar have significant effects on consumers’ cognitive learning, affect, and behavior. However, it was found that the humanoid avatar had no significant effect on respondents’ decision‐making. Further research was suggested to improve the usability of real‐time simulation in the marketing of house products.

RECOGNIZING BEHAVIOR IN HAND-EYE COORDINATION PATTERNS

Modeling human behavior is important for the design of robots as well as human-computer interfaces that use humanoid avatars. Constructive models have been built, but they have not captured all of the detailed structure of human behavior such as the moment-to-moment deployment and coordination of hand, head and eye gaze used in complex tasks. We show how this data from human subjects performing a task can be used to program a dynamic Bayes network (DBN) which in turn can be used to recognize new performance instances. As a specific demonstration we show that the steps in a complex activity such as sandwich making can be recognized by a DBN in real time.

Exploiting Virtual Environments to Support Collaborative E-Learning Communities

The main goal of this article is to facilitate educational designers and developers by providing a point of reference for making decisions on how to incorporate 3D environments into the applications they develop as well as for extending their capabilities by integrating more functionality. Therefore, this article presents the design principles for virtual spaces, which aim at supporting multi-user communication in Web-based learning communities. In addition the implementation of these principles is presented, using as point of reference EVE Training Area. This environment constitutes a three-dimensional space where participants, represented by 3D humanoid avatars, have the ability to use a variety of 3D e-collaboration tools for learning together. Furthermore, this article presents collaborative e-learning usage scenarios that could be realized by exploiting collaborative virtual environments.

Online Consumer Trust and Live Help Interfaces: The Effects of Text-to-Speech Voice and Three-Dimensional Avatars

With the increasing prevalence of online shopping, many companies have begun to provide "live help" functions, through instant messaging or text chatting, on their Web sites to facilitate interactions between online consumers and customer service representatives (CSRs). The continuing reliance of these functions on text-based communication limits nonverbal communication with consumers and the social contexts for the information conveyed. However, with the help of emerging multimedia technologies, companies can now use computer-generated voice and humanoid avatars to embody CSRs, thus enriching the interactive experiences of their customers. In this study, a laboratory experiment was conducted to empirically test the effects of text-to-speech (TTS) voice and 3-dimensional (3D) avatars on consumer trust toward CSRs. TTS voice was implemented to deliver answers aloud. A 3D avatar served as the humanoid representation of a CSR. The results demonstrated that the presence of TTS voice significantly increases consumers' cognitive and emotional trust toward the CSR. These findings offer practitioners guidelines to improve the interface design of real-time human-to-human communications for e-commerce Web sites.

LEARNING MOTOR SKILLS BY IMITATION: A BIOLOGICALLY INSPIRED ROBOTIC MODEL

This article presents a biologically inspired model for motor skills imitation. The model is composed of modules whose functinalities are inspired by corresponding brain regions responsible for the control of movement in primates. These modules are high-level abstractions of the spinal cord, the primary and premotor cortexes (M1 and PM), the cerebellum, and the temporal cortex. Each module is modeled at a connectionist level. Neurons in PM respond both to visual observation of movements and to corresponding motor commands produced by the cerebellum. As such, they give an abstract representation of mirror neurons. Learning of new combinations of movements is done in PM and in the cerebellum. Premotor cortexes and cerebellum are modeled by the DRAMA neural architecture which allows learning of times series and of spatio-temporal invariance in multimodal inputs. The model is implemented in a mechanical simulation of two humanoid avatars, the imitator and the imitatee. Three types of sequences learning are presented: (1) learning of repetitive patterns of arm and leg movements; (2) learning of oscillatory movements of shoulders and elbows, using video data of a human demonstration; 3) learning of precise movements of the extremities for grasp and reach.