Emotion expression in a socially assistive robot for persons with Parkinson's disease

Valenti, Andrew; Block, Avram; Chita-Tegmark, Meia; Gold, Michael; Scheutz, Matthias

doi:10.1145/3389189.3389190

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Since pleasure mainly depends on the robot's current motivation and goals [29], and it significantly informs the expresser's strategic choices in subsequent social interactions [30], the display of valence has a decisive impact on the overall emotional recognition results. Many studies on emotional expression also mainly focus on the expression of pleasure [31] [9]. In light of these considerations, we pose the question: in scenarios where both vertical and horizontal directions are concurrently present, which direction will more affect valence recognition, and how do these combined movements collectively convey emotional content?…”

Section: Conceptual Model and Hypotheses Developmentmentioning

confidence: 99%

“…For instance, robots were programmed to convey positive emotions through human-like gestures [8], such as cheering, applauding, and wave dancing for positive emotion, while negative emotions were represented using gestures like shrugging and crossing the arms. Similarly, Valenti et al employed the Nao [9] robot to devise gestures based on human arm movements for expressing basic emotions. Johnson and Cuijpers conducted network experiments to investigate changes in the head position of robots, and found that people expect robots to lower their heads and gaze downward when expressing anger, sadness, fear, or disgust [10].…”

Section: Introductionmentioning

confidence: 99%

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The Holistic Expression Factors of Emotional Motion for Non-humanoid Robots

Xie,

Luh

2023

IJACSA

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The development of technology and the increasing prevalence of solitary living have transformed non-humanoid robots, such as robotic sweepers and mechanical pets, into potential sources of emotional support for individuals. Nevertheless, the majority of non-humanoid robots currently in existence are task-oriented and lack features such as facial expressions and sound. Existing research primarily emphasizes the details of human motion in robot motion design, while devoting less attention to the analysis of universal emotional expression factors and methods rooted in human recognition patterns. In our initial step, a theoretical framework and holistic expression factors were proposed based on Gestalt theory and SOR theory. These factors encompass vertical and horizontal motion direction, stimulation, and vertical repetition. Subsequently, animation simulation tests were conducted to confirm and examine the contributions of each factor to the recognition of emotional expressions. The results indicate that both vertical and horizontal movements can convey emotional valence. However, if both of them exist, there is no leading direction to the valence recognition result. When both vertical and horizontal movements are present, valence recognition is influenced by the combined effects of stimulation, vertical repetition, and movement direction. Simultaneously, nonhumanoid robots can display recognizable emotional content when influenced by holistic expression factors. This framework can serve as a universal guide for emotional expression tasks in non-humanoid robots, proving the hypothesis that Gestalt theory is applicable in dynamic emotional recognition tasks. At the same time, these findings propose a new holistic perspective for designing emotional expression methods for robots.

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Section: Conceptual Model and Hypotheses Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Holistic Expression Factors of Emotional Motion for Non-humanoid Robots

Xie,

Luh

2023

IJACSA

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“…Expressive body language ( Marmpena et al, 2018 ; Zhang et al, 2018 ) influences humans’ arousal, their attitude toward robots, and perceptions from their interaction. The robot’s speech is another important factor, both auditory and semantic, that is used for expressing emotional behavior in the human–robot interaction ( Valenti et al, 2020 ). However, it is not clear which modality (personality, body language, or speech) is more effective when a social robot performs an educational storytelling activity.…”

Section: Introductionmentioning

confidence: 99%

Which One? Choosing Favorite Robot After Different Styles of Storytelling and Robots’ Conversation

2021

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The influence of human-care service robots in human–robot interaction is becoming of great importance, because of the roles that the robots are taking in today’s and future society. Thus, we need to identify how humans can interact, collaborate, and learn from social robots more efficiently. Additionally, it is important to determine the robots’ modalities that can increase the humans’ perceived likeness and knowledge acquisition and enhance human–robot collaboration. The present study aims to identify the optimal social service robots’ modalities that enhance the human learning process and level of enjoyment from the interaction and even attract the humans’ attention to choosing a robot to collaborate with it. Our target group was college students, pre-service teachers. For this purpose, we designed two experiments, each one split in two parts. Both the experiments were between groups, and human participants had the chance to watch the Nao robot performing a storytelling exercise about the history of robots in a museum-educational activity via video annotations. The robot’s modalities were manipulated on its body movements (expressive arm and head gestures) while performing the storytelling, friendly attitude expressions and storytelling, and personality traits. After the robot’s storytelling, participants filled out a knowledge acquisition questionnaire and a self-reported enjoyment level questionnaire. In the second part, we introduce the idea of participants witnessing a conversation between the robots with the different modalities, and they were asked to choose the robot with which they want to collaborate in a similar activity. Results indicated that participants prefer to collaborate with robots with a cheerful personality and expressive body movements. Especially when they were asked to choose between two robots that were cheerful and had expressive body movements, they preferred the one which originally told them the story. Moreover, participants did not prefer to collaborate with a robot with an extremely friendly attitude and storytelling style.

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