Recognizing Emotional Body Language Displayed by a Human-like Social Robot

137

105

Research in the area of robotics has made available numerous possibilities for further innovation in the education of children, especially in the rehabilitation of those with learning difficulties and/or intellectual disabilities. Despite the scientific evidence, there is still a strong scepticism against the use of robots in the fields of education and care of people. Here we present a study on the acceptance of robots by experienced practitioners (specialized in the treatment of intellectual disabilities) and university students in psychology and education sciences (as future professionals). The aim is to examine the factors, through the Unified Theory of Acceptance and Use of Technology (UTAUT) model, that may influence the decision to use a robot as an instrument in the practice. The overall results confirm the applicability of the model in the context of education and care of children, and suggest a positive attitude towards the use of the robot. The comparison highlights some scepticism among the practitioners, who perceive the robot as an expensive and limited tool, while students show a positive perception and a significantly higher willingness to use the robot. From this experience, we formulate the hypothesis that robots may be accepted if more integrated with standard rehabilitation protocols in a way that benefits can outweigh the costs.

Section: Related Workmentioning

confidence: 99%

Robots in Education and Care of Children with Developmental Disabilities: A Study on Acceptance by Experienced and Future Professionals

Conti

Nuovo

Buono

et al. 2016

137

105

“…To be effective social interaction partners, robots must also exploit several channels (i.e., auditory, visual) and mechanisms (e.g., body posture, facial expressions, vocal prosody, touch, gaze) to communicate their internal emotional states and intentions in an authentic and clear way [10,11]. Many researchers, explore the design space of anthropomorphic or zoomorphic robots equipped with expressive faces (e.g., [5,[12][13][14][15][16]), emotional voices (see [17] for a survey), body language (e.g., [18][19][20][21]), and other features and capacities to make human-robot social interactions more human-like. While initially, Human-Robot Interaction (HRI) research on the emotional expressions of robots had largely focused on single modalities in isolation [22], more recently, researchers have begun to integrate multiple channels, in order to approach the richness of human emotional communication.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Integration of Emotional Signals from Voice, Body, and Context: Effects of (In)Congruence on Emotion Recognition and Attitudes Towards Robots

Tsiourti

Weiss

Wac

et al. 2019

125

Humanoid social robots have an increasingly prominent place in today's world. Their acceptance in social and emotional human-robot interaction (HRI) scenarios depends on their ability to convey well recognized and believable emotional expressions to their human users. In this article, we incorporate recent findings from psychology, neuroscience, human-computer interaction, and HRI, to examine how people recognize and respond to emotions displayed by the body and voice of humanoid robots, with a particular emphasis on the effects of incongruence. In a social HRI laboratory experiment, we investigated contextual incongruence (i.e., the conflict situation where a robot's reaction is incongrous with the socio-emotional context of the interaction) and cross-modal incongruence (i.e., the conflict situation where an observer receives incongruous emotional information across the auditory (vocal prosody) and visual (whole-body expressions) modalities). Results showed that both contextual incongruence and cross-modal incongruence confused observers and decreased the likelihood that they accurately recognized the emotional expressions of the robot. This, in turn, gives the impression that the robot is unintelligent or unable to express "empathic" behaviour and leads to profoundly harmful effects on likability and believability. Our findings reinforce the need of proper design of emotional expressions for robots that use several channels to communicate their emotional states in a clear and effective way. We offer recommendations regarding design choices and discuss future research areas in the direction of multimodal HRI.

“…In order to design systems that achieve a better understanding of humans, and interact with them more intelligently, it is necessary to strengthen our understanding of human affects and to develop algorithms that can capture this emotional information and to use this information to generate robot motions [18,19]. The aim of the present paper is to provide an objective study that tackles the psychological and computational aspects of affect recognition from gait.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Emotions Conveyed by Human Gait

Venture

Kadone

Zhang

et al. 2014

Humans convey emotions through different ways. Gait is one of them. Here we propose to use gait data to highlight features that characterize emotions. Gait analysis study usually focuses on stance phase, frequency, footstep length.Here the study is based on the joint angles obtained from inverse kinematics computation from the 3D motion-capture data using a combination of degrees of freedom (DOF) out of a 34DOF human body model obtained from inverse kinematics of markers 3D position. The candidates are four professional actors, and five emotional states are simulated: Neutral, Joy, Anger, Sadness, and Fear. The paper presents first a psychological approach which results are used to propose numerical approaches. The first study provides psychological results on motion perception and the possibility of emotion recognition from gait by 32 observers. Then, the motion data is studied using a feature vector approach to verify the numerical identifiability of the emotions. Finally each motion is tested against a database of reference motions to identify the conveyed emotion. Using the first and second study results, we utilize a 6DOF model then a 12DOF model. The experimental results show that by using the gait characteristics it is possible to characterize each emotion with good accuracy for intra-subject data-base. For inter-subject database results show that recognition is more prone to error, suggesting strong inter-personal differences in emotional features.