As the aging population increases, so does the demand for personalized care and rehabilitation for individuals with neurological disorders. Effective recovery programs require intensive, task-oriented training, yet delivering continuous and individualized care remains a major challenge. Technological innovations such as wearable sensors and socially interactive robots can enhance patient monitoring and improve medical teams’ situational awareness. This paper presents a preliminary evaluation of a robot-based architecture deployed in a real clinical setting, designed to support rehabilitation tasks and patient monitoring. The results demonstrate the system’s feasibility in providing therapists with timely and accurate data, facilitating natural interactions with patients, and minimizing the need for technical interventions during use.

Social robots are increasingly being explored as interactive coaches capable of delivering personalized physical and cognitive training sessions. Improving their effectiveness entails personalized interventions through adaptive robotic systems with continuous workload quantification. This study presents a workload estimation methodology based on physiological and kinematic monitoring, designed for integration into a social robotic coach. Physical, mental, and dual-task activities were administered to 15 healthy participants, and Support Vector Regression was used to model their perceived workload levels. Physical workload was estimated with a mean absolute error (MAE) of 0.12 ± 0.01 and a correlation of 0.75 ± 0.02, demonstrating high reliability across conditions. Mental workload estimation, however, showed greater variability (MAE: 0.18 ± 0.01, correlation: 0.62 ± 0.03), particularly in cognitively demanding and high-intensity tasks. This is likely due to overlapping physiological responses to cognitive and physical demands, which introduce ambiguity in signal interpretation. The continuous workload estimation provided by the model can be leveraged to define thresholds offering a discrete interpretation of workload levels.

Abstract Socially assistive robotics (SAR) aims at designing robots capable of guaranteeing social interaction to human users in a variety of assistance scenarios that range, e.g., from giving reminders for medications to monitoring of Activity of Daily Living, from giving advices to promote an healthy lifestyle to psychological monitoring. Among possible users, frail older adults deserve a special focus as they present a rich variability in terms of both alternative possible assistive scenarios (e.g., hospital or domestic environments) and caring needs that could change over time according to their health conditions. In this perspective, robot behaviors should be customized according to properly designed user models. One of the long-term research goals for SAR is the realization of robots capable of, on the one hand, personalizing assistance according to different health-related conditions/states of users and, on the other, adapting behaviors according to heterogeneous contexts as well as changing/evolving needs of users. This work proposes a solution based on a user model grounded on the international classification of functioning, disability and health (ICF) and a novel control architecture inspired by the dual-process theory. The proposed approach is general and can be deployed in many different scenarios. In this paper, we focus on a social robot in charge of the synthesis of personalized training sessions for the cognitive stimulation of older adults, customizing the adaptive verbal behavior according to the characteristics of the users and to their dynamic reactions when interacting. Evaluations with a restricted number of users show good usability of the system, a general positive attitude of users and the ability of the system to capture users personality so as to adapt the content accordingly during the verbal interaction.

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.

Socially assistive robots should provide users with personalized assistance within a wide range of scenarios such as hospitals, home or social settings and private houses. Different people may have different needs both at the cognitive/physical support level and in relation to the preferences of interaction. Consequently the typology of tasks and the way the assistance is delivered can change according to the person with whom the robot is interacting. The authors' long-term research goal is the realization of an advanced cognitive system able to support multiple assistive scenarios with adaptations over time. We here show how the integration of model-based and model-free AI technologies can contextualize robot assistive behaviors and dynamically decide what to do (assistive plan) and how to do it (assistive plan execution), according to the different features and needs of assisted persons. Although the approach is general, the paper specifically focuses on the synthesis of personalized therapies for (cognitive) stimulation of users.

The deployment of assistive robots in everyday life scenarios and their capability of providing an effective and useful support for independent living is an open and challenging research problem. The development of suitable robot control systems requires effective solutions for addressing issues concerning performance, reliability, flexibility and proactivity. In this work, we propose an AI-based cognitive architecture aiming at integrating knowledge representation with automated planning and execution techniques in order to endow assistive robots with proactivity and self-configuration capabilities.

Early detection and adaptive support to changing individual needs related to ageing is an important challenge in today's society. In this paper we present a system called GiraffPlus that aims at addressing such a challenge and is developed in an on-going European project. The system consists of a network of home sensors that can be automatically configured to collect data for a range of monitoring services; a semi-autonomous telepresence robot; a sophisticated context recognition system that can give high-level and long term interpretations of the collected data and respond to certain events; and personalized services delivered through adaptive user interfaces for primary users. The system performs a range of services including data collection and analysis of long term trends in behaviors and physiological parameters (e.g. relating to sleep or daily activity); warnings, alarms and reminders; and social interaction through the telepresence robot. The latter is based on the Giraff telepresence robot, which is already in place in a number of homes. A distinctive aspect of the project is that the GiraffPlus system will be installed and evaluated in at least 15 homes of elderly people. This paper provides a general overview of the GiraffPlus system and its evaluation.

Telepresence robots can become a beneficial tool in home care assistance and rehabilitation services by helping elderly people to remain in their homes longer. They can represent an additional means to assist older adults and facilitate social interaction by creating a support network through which nursing staff and family members can collaborate. This article describes a feasibility study relatively to the use of such robots in the interaction with elderly people affected by Mild Cognitive Impairment (MCI). The paper aims at assessing the psychophysiological response of such users to the presence of the robotic platform in order to use it as an indication of the level of tolerance toward the platform. To this purpose, we have involved 9 healthy and 8 MCI older adults in the participation of an experimental study where they have been asked to perform repeated interactions with and without the telepresence robot. We based our analysis on a combination of psychological tests to assess anxiety, positive/negative effects of the interaction with the robot, and we performed physiological measurements (heart rate and heart rate variability) to obtain an objective measure of the actual psychological state. Results seem to suggest that the robot presence is satisfactorily tolerated by MCI and that it does not cause adverse effects in term of cardiovascular response, thus encouraging further investigation on telepresence robots for rehabilitation and care experimental studies.

Robotic telepresence, also known as telerobotics is a subfield of telepresence whose aim is to increase presence via embodiment in a robotic platform. In particular, robotic telepresence can be an effective tool to enhance social interaction suited to certain groups of users such as the elderly. The aim of this workshop is to address various aspects important for social robotic telepresence which include but are not limited to, (1) the mechanical design, (2) the user interface design, (3) the interaction between the remotely embodied person and the locally embodied person and (4) the perception of social robotic telepresence systems. Furthermore, we are interested in discovering the added value of spatial presence in the context of social telepresence and comparisons between robotic and non-robotic systems are of interest. We welcome contributions concerning results reached from the above mentioned areas of interest, user evaluation and methodologies, as well as reports from the deployment of social robotic solutions into real world contexts.

This article elaborates around a recent effort to build a planning system that helps human mission planning in a space mission. Specifically, the article describes the steps that brought us to develop MrSPOCK, the MARS EXPRESS Science Plan Opportunities Coordination Kit, a tool for supporting long‐term planning in the MARS EXPRESS mission of the European Space Agency. In showing our effort for creating MrSPOCK, we will underscore the key ingredients for developing complete applications and how they are connected to a stable line of research on planning and scheduling with timelines.

This article describes aspects of a fully implemented artificial intelligence (AI) system that integrates multiple intelligent components to actively assist an elderly person at home. Specifically, we describe how constraint‐based scheduling technology is used to actively monitor a pattern of activities executed by the person and how detected temporal constraint violations are used to trigger meaningful and contextualized proactive interactions. This article also presents a psychological evaluation of the system focusing on elderly people's attitudes, in which system acceptability, perceived utility, interaction modality, and emotional response are considered.

This paper describes RAXEM, an AI-based system developed to support human mission planners in the daily task to plan uplink commands for an interplanetary spacecraft. The intelligent environment of RAXEM has been designed to support the users in analyzing the problem and taking planning decisions as a result of an interactive process. The system combines different ingredients like integrating flexible automated algorithms, promoting user active participation during problem solving, and guaranteeing continuity of work practice. The paper touches upon all these aspects and comments on how a key factor for success has been the integration of intelligent technology to continuously support mission plan management.