Computational Commensality: from theories to computational models for social food preparation and consumption in HCI

Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as “commensality,” positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of “Computational Commensality,” that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective

JUNO Project: deployment and validation of a low-cost cloud-based robotic platform for reliable smart navigation and natural interaction with humans in an Elderly Institution

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered.This research was funded by Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia, grant number 21668/PDC/21 and grant number 21593/FPI/21, provided by the same funding institution

Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review

Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient’s activity monitoring models. Then, improving such systems’ performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area

You are welcome and we value you, Guiding the co-design of a revised telepractice delivery model with the disability community: an embedded researcher approach

Telepractice was introduced rapidly in the wake of the COVID-19 pandemic and many organisations struggled to integrate it into clinical practice. This study worked in partnership with Rocky Bay, a disability service provider, to co-design improvements to their telepractice. It aimed to improve the experience of telepractice for both customers and clinicians. This work was co-produced with people with disability and clinicians, to support the development of a fit for purpose research program

Digital Transformation

The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing.The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing