Analyzing the Impact of Responding to Joint Attention on the User Perception of the Robot in Human-Robot Interaction.

The concept of joint attention holds significant importance in human interaction and is pivotal in establishing rapport, understanding, and effective communication. Within social robotics, enhancing user perception of the robot and promoting a sense of natural interaction with robots becomes a central element. In this sense, emulating human-centric qualities in social robots, such as joint attention, defined as the ability of two or more individuals to focus on a common event simultaneously, can increase their acceptability.

Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.

Purpose: Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health.

A Bio-Inspired Endogenous Attention-Based Architecture for a Social Robot.

A robust perception system is crucial for natural human-robot interaction. An essential capability of these systems is to provide a rich representation of the robot's environment, typically using multiple sensory sources. Moreover, this information allows the robot to react to both external stimuli and user responses.

An adaptive decision-making system supported on user preference predictions for human-robot interactive communication.

Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this context, a social robot that adapts its behaviour to different users and proactively suggests their favourite activities may produce a more successful interaction. In this work, we describe how the autonomous decision-making system embedded in our social robot Mini can produce a personalised interactive communication experience by considering the preferences of the user the robot interacts with.

Effectiveness and cost-effectiveness of a virtual community of practice to improve the empowerment of patients with ischaemic heart disease: study protocol of a randomised controlled trial.

Introduction: Virtual Communities of Practice (VCoP) or knowledge-sharing virtual communities offer ubiquitous access to information and exchange possibilities for people in similar situations, which might be especially valuable for the self-management of patients with chronic diseases. In view of the scarce evidence on the clinical and economic impact of these interventions on chronic conditions, we aim to evaluate the effectiveness and cost-effectiveness of a VCoP in the improvement of the activation and other patient empowerment measures in patients with ischaemic heart disease (IHD).

Methods And Analysis: A pragmatic randomised controlled trial will be performed in Catalonia, Madrid and Canary Islands, Spain.

The effectiveness of a web-based decision aid for patients with hip osteoarthritis: study protocol for a randomized controlled trial.

Background: Osteoarthritis (OA) is a health condition sensitive to patient's preferences and values regarding the benefits and risks of the different treatment options. In this sense, patient decision aids (PtDA) can play an important role in helping patients to incorporate their values, needs, and preferences into the decision-making process, thus improving person-centered care. Previous research has focused almost exclusively on knee OA, and therefore, the aim of this study is to develop and evaluate the effectiveness of a PtDA for patients with hip OA.

A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User.

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot.

Using unmanned aerial vehicle (UAV) surveys and image analysis in the study of large surface-associated marine species: a case study on reef sharks Carcharhinus melanopterus shoaling behaviour.

A novel image analysis-based technique applied to unmanned aerial vehicle (UAV) survey data is described to detect and locate individual free-ranging sharks within aggregations. The method allows rapid collection of data and quantification of fine-scale swimming and collective patterns of sharks. We demonstrate the usefulness of this technique in a small-scale case study exploring the shoaling tendencies of blacktip reef sharks Carcharhinus melanopterus in a large lagoon within Moorea, French Polynesia.

Social Robotics in Therapy of Apraxia of Speech.

Apraxia of speech is a motor speech disorder in which messages from the brain to the mouth are disrupted, resulting in an inability for moving lips or tongue to the right place to pronounce sounds correctly. Current therapies for this condition involve a therapist that in one-on-one sessions conducts the exercises. Our aim is to work in the line of robotic therapies in which a robot is able to perform partially or autonomously a therapy session, endowing a social robot with the ability of assisting therapists in apraxia of speech rehabilitation exercises.

Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning.

An important aspect in Human-Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot's shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot's shell is processed using machine learning techniques to distinguish between different types of touches.

Smart environment architecture for emotion detection and regulation.

This paper introduces an architecture as a proof-of-concept for emotion detection and regulation in smart health environments. The aim of the proposal is to detect the patient's emotional state by analysing his/her physiological signals, facial expression and behaviour. Then, the system provides the best-tailored actions in the environment to regulate these emotions towards a positive mood when possible.

Real-time accumulative computation motion detectors.

The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving.