User-centered design of a dynamic-autonomy remote interaction concept for manipulation-capable robots to assist elderly people in the home

In this article, we describe the development of a human-robot interaction concept for service robots to assist elderly people in the home with physical tasks. Our approach is based on the insight that robots are not yet able to handle all tasks autonomously with sufficient reliability in the complex and heterogeneous environments of private homes. We therefore employ remote human operators to assist on tasks a robot cannot handle completely autonomously. Our development methodology was user-centric and iterative, with six user studies carried out at various stages involving a total of 241 participants. The concept is under implementation on the Care-O-bot 3 robotic platform. The main contributions of this article are (1) the results of a survey in form of a ranking of the demands of elderly people and informal caregivers for a range of 25 robot services, (2) the results of an ethnography investigating the suitability of emergency teleassistance and telemedical centers for incorporating robotic teleassistance, and (3) a user-validated human-robot interaction concept with three user roles and corresponding three user interfaces designed as a solution to the problem of engineering reliable service robots for home environments

Can We Agree on What Robots Should be Allowed to Do? An Exercise in Rule Selection for Ethical Care Robots

Future Care Robots (CRs) should be able to balance a patient’s, often conflicting, rights without ongoing supervision. Many of the trade-offs faced by such a robot will require a degree of moral judgment. Some progress has been made on methods to guarantee robots comply with a predefined set of ethical rules. In contrast, methods for selecting these rules are lacking. Approaches departing from existing philosophical frameworks, often do not result in implementable robotic control rules. Machine learning approaches are sensitive to biases in the training data and suffer from opacity. Here, we propose an alternative, empirical, survey-based approach to rule selection. We suggest this approach has several advantages, including transparency and legitimacy. The major challenge for this approach, however, is that a workable solution, or social compromise, has to be found: it must be possible to obtain a consistent and agreed-upon set of rules to govern robotic behavior. In this article, we present an exercise in rule selection for a hypothetical CR to assess the feasibility of our approach. We assume the role of robot developers using a survey to evaluate which robot behavior potential users deem appropriate in a practically relevant setting, i.e., patient non-compliance. We evaluate whether it is possible to find such behaviors through a consensus. Assessing a set of potential robot behaviors, we surveyed the acceptability of robot actions that potentially violate a patient’s autonomy or privacy. Our data support the empirical approach as a promising and cost-effective way to query ethical intuitions, allowing us to select behavior for the hypothetical CR

Conversational affective social robots for ageing and dementia support

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation

Enabling personalised medical support for chronic disease management through a hybrid robot-cloud approach

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Social Robots in Hospitals: A Systematic Review

Hospital environments are facing new challenges this century. One of the most important is the quality of services to patients. Social robots are gaining prominence due to the advantages they offer; in particular, several of their main uses have proven beneficial during the pandemic. This study aims to shed light on the current status of the design of social robots and their interaction with patients. To this end, a systematic review was conducted using WoS and MEDLINE, and the results were exhaustive analyzed. The authors found that most of the initiatives and projects serve the el- derly and children, and specifically, that they helped these groups fight diseases such as dementia, autism spectrum disorder (ASD), cancer, and diabetes

Socially Assistive Robots in Smart Environments to Attend Elderly People—A Survey.

Assistive environments for daily living (Ambient Assisted Living, AAL) include the deployment of sensors and certain actuators in the home or residence where the person to be cared for lives so that, with the help of the necessary computational management and decision-making mechanisms, the person can live a more autonomous life. Such technologies are becoming more affordable and popular. However, despite the undoubted potential of the services offered by these AAL systems, there are serious problems of acceptance today. In part, these problems arise from the design phase, which often does not sufficiently take into account the end users. On the other hand, it is complex for these older people to interact with interfaces that are sometimes not very natural or intuitive. The use of a socially assistive robot (SAR) that serves as an interface to the AAL system and takes responsibility for the interaction with the person is a possible solution. The robot is a physical entity that can operate with a certain degree of autonomy and be able to bring features to the interaction with the person that, obviously, a tablet or smartphone will not be able to do. The robot can benefit from the recent popularization of artificial intelligence-based solutions to personalize its attention to the person and to provide new services. Their inclusion in an AAL ecosystem should, however, also be carefully assessed. The robot’s mission should not be to replace the person but to be a tool to facilitate the elderly person’s daily life. Its design should consider the AAL system in which it is integrated, the needs and preferences of the people with whom it will interact, and the services that, in conjunction with this system, the robot can offer. The aim of this article is to review the current state of the art in the integration of SARs into the AAL ecosystem and to determine whether an initial phase of high expectations but very limited results have been overcome.This work has been supported by grants PDC2022-133597-C42, TED2021-131739B-C21 and PID2022-137344OB-C32, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR (for the first two grants), and “ERDF A way of making Europe” (for the third grant). Furthermore, this work has also been supported by the “Vivir en Casa” project (8.07/5.14.6298), funded by the European Union Next Generation/PRTR and by the Government of Andalusia

Robotics services at home support

This article proposes a robotic system that aims to support the elderly, to comply with the medication regimen to which they are subject. The robot uses its locomotion system to move to the elderly and through computer vision detects the packaging of the medicine and identifies the person who should take it at the correct time. For the accomplishment of the task, an application was developed supported by a database with information about the elderly, the medicines that they have prescribed and the respective timetable of taking. The experimental work was done with the robot NAO, using development tools like MySQL, Python, and OpenCV. The elderly facial identification and the detection of medicine packing are performed through computer vision algorithms that process the images acquired by the robot’s camera. Experiments were carried out to evaluate the performance of object recognition, facial detection, and facial recognition algorithms, using public databases. The tests made it possible to obtain qualitative metrics about the algorithms’ performance. A proof of concept experiment was conducted in a simple scenario that recreates the environment of a dwelling with seniors who are assisted by the robot in the taking of medicines.info:eu-repo/semantics/publishedVersio