Trusting Intentions Towards Robots in Healthcare: A Theoretical Framework

Within the next decade, robots (intelligent agents that are able to perform tasks normally requiring human intelligence) may become more popular when delivering healthcare services to patients. The use of robots in this way may be daunting for some members of the public, who may not understand this technology and deem it untrustworthy. Others may be excited to use and trust robots to support their healthcare needs. It is argued that (1) context plays an integral role in Information Systems (IS) research and (2) technology demonstrating anthropomorphic or system-like features impact the extent to which an individual trusts the technology. Yet, there is little research which integrates these two concepts within one study in healthcare. To address this gap, we develop a theoretical framework that considers trusting intentions towards robots based on the interaction of humans and robots within the contextual landscape of delivering healthcare services. This article presents a theory-based approach to developing effective trustworthy intelligent agents at the intersection of IS and Healthcare

A Framework to Generate and Label Synthetic/Real Video Data to Feed Temporal Segment Networks

In this project, we propose an action prediction and a data generation pipeline. While, the former makes use of Deep Learning, the latter results in a pipeline that makes possible the generation of real and synthetic data. Moreover, to feed the deep learning method a large amount of annotated data is needed. For this purpose an action tagging tool is also featured. Furthermore, in order to supply the lack of data, we have also proposed a video data augmentation pipeline for action recognition purposes. While the 3DPLab team developed a photorealistic synthetic data generator called UnrealRox, we will use this system working with some sequences recorded with a mocap to generate the necessary synthetic data. We have generated a total of 5 different useful sequences with a complex setup of 3 kinects and a capture motion suit. Finally, we have deployed and tested the novel Temporal Segment Network with the state of the art Action Recognition dataset UCF-101

Does the personality of consumers influence the assessment of the experience of interaction with social robots?

In recent years, in response to the effects of Covid-19, there has been an increase in the use of social robots in service organisations, as well as in the number of interactions between consumers and robots. However, it is not clear how consumers are valuing these experiences or what the main drivers that shape them are. Furthermore, it is an open research question whether these experiences undergone by consumers can be affected by their own personality. This study attempts to shed some light on these questions and, to do so, an experiment is proposed in which a sample of 378 participants evaluate a simulated front-office service experience delivered by a social robot. The authors investigate the underlying process that explains the experience and find that cognitive-functional factors, emphasising efficiency, have practically the same relevance as emotional factors, emphasising stimulation. In addition, this research identifies the personality traits of the participants and explores their moderating role in the evaluation of the experience. The results reveal that each personality trait, estimated between high and low poles, generates different responses in the evaluation of the experience.Peer ReviewedPostprint (published version

Robotics in Germany and Japan

This book comprehends an intercultural and interdisciplinary framework including current research fields like Roboethics, Hermeneutics of Technologies, Technology Assessment, Robotics in Japanese Popular Culture and Music Robots. Contributions on cultural interrelations, technical visions and essays are rounding out the content of this book