Humanoid and android robots in the imaginary of adolescents, young adults and seniors

This paper investigates effects of participants’ gender and age (adolescents, young adults, and seniors), robots’ gender (male and female robots) and appearance (humanoid vs android) on robots’ acceptance dimensions. The study involved 6 differently aged groups of participants (two adolescents, two young adults and two seniors’ groups, for a total of 240 participants) requested to express their willingness to interact and their perception of robots’ usefulness, pleasantness, appeal, and engagement for two different sets of females (Pepper, Erica, and Sophia) and male (Romeo, Albert, and Yuri) humanoid and android robots. Participants were also requested to express their preferred and attributed age ranges and occupations they entrusted to robots among healthcare, housework, protection and security and front office. Results show that neither the age nor participants and robots’ gender, nor robots’ human likeness univocally affected robots’ acceptance by these differently aged users. Robots’ acceptance appeared to be a nonlinear combination of all these factors

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

Movement Acts in Breakdown Situations : How a Robot’s Recovery Procedure Affects Participants’ Opinions

Funding Information: Funding information : This research was partly funded by the Research Council of Norway as part of the Multimodal Elderly Care Systems (MECS) project, under grant agreement 247697. Publisher Copyright: © 2021 Trenton Schulz et al., published by De Gruyter.Recovery procedures are targeted at correcting issues encountered by robots. What are people’s opinions of a robot during these recovery procedures? During an experiment that examined how a mobile robot moved, the robot would unexpectedly pause or rotate itself to recover from a navigation problem. The serendipity of the recovery procedure and people’s understanding of it became a case study to examine how future study designs could consider breakdowns better and look at suggestions for better robot behaviors in such situations. We present the original experiment with the recovery procedure. We then examine the responses from the participants in this experiment qualitatively to see how they interpreted the breakdown situation when it occurred. Responses could be grouped into themes of sentience, competence, and the robot’s forms. The themes indicate that the robot’s movement communicated different information to different participants. This leads us to introduce the concept of movement acts to help examine the explicit and implicit parts of communication in movement. Given that we developed the concept looking at an unexpected breakdown, we suggest that researchers should plan for the possibility of breakdowns in experiments and examine and report people’s experience around a robot breakdown to further explore unintended robot communication.Peer reviewe

Communicative Robot Signals: Presenting a New Typology for Human-Robot Interaction

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defining the typology. The typology is based on communicative signals that consist of five properties: the origin where the signal comes from, the deliberateness of the signal, the signal's reference, the genuineness of the signal, and its clarity (i.e. how implicit or explicit it is). Using the accompanying worksheet, the typology is straightforward to use to examine communicative signals from previous human-robot interactions and provides guidance for designers to use the typology when designing new robot behaviours

Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

Engagement is crucial to designing intelligent systems that can adapt to the characteristics of their users. This paper focuses on automatic analysis and classification of engagement based on humans’ and robot’s personality profiles in a triadic human-human-robot interaction setting. More explicitly, we present a study that involves two participants interacting with a humanoid robot, and investigate how participants’ personalities can be used together with the robot’s personality to predict the engagement state of each participant. The fully automatic system is firstly trained to predict the Big Five personality traits of each participant by extracting individual and interpersonal features from their nonverbal behavioural cues. Secondly, the output of the personality prediction system is used as an input to the engagement classification system. Thirdly, we focus on the concept of “group engagement”, which we define as the collective engagement of the participants with the robot, and analyse the impact of similar and dissimilar personalities on the engagement classification. Our experimental results show that (i) using the automatically predicted personality labels for engagement classification yields an F-measure on par with using the manually annotated personality labels, demonstrating the effectiveness of the automatic personality prediction module proposed; (ii) using the individual and interpersonal features without utilising personality information is not sufficient for engagement classification, instead incorporating the participants’ and robot’s personalities with individual/interpersonal features increases engagement classification performance; and (iii) the best classification performance is achieved when the participants and the robot are extroverted, while the worst results are obtained when all are introverted.This work was performed within the Labex SMART project (ANR-11-LABX-65) supported by French state funds managed by the ANR within the Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02. The work of Oya Celiktutan and Hatice Gunes is also funded by the EPSRC under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref.: EP/L00416X/1).This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers via http://dx.doi.org/10.1109/ACCESS.2016.261452

Interaction Analysis and Cognitive Infocommunications

Cognitive infocommunications encompasses both scientific and engineering oriented approaches to examining extensions of human cognitive capabilities that may be assimilated within the concept of humanity. Necessary (but not sufficient) conditions for the success of any candidate technology include solving problems within private and public spheres of existence, in thought and communication. Exemplar cognitive infocommunication technologies that have been assimilated in to the concept of humanitiy are examined: emotion, gesture, language. Implications for research programmes conducted within the cognitive infocommunications discipline are outlined