Introduction: This article describes an enhanced telepresence robot named ROBIN, part of a telecare system derived from the GIRAFFPLUS project for supporting and monitoring older adults at home. ROBIN is integrated in a sensor-rich environment that aims to continuously monitor physical and psychological wellbeing of older persons living alone. The caregivers (formal/informal) can communicate through it with their assisted persons. Long-term trials in real houses highlighted several user requirements that inspired improvements on the robotic platform. The enhanced telepresence robot was assessed by users to test its suitability to support social interaction and provide motivational feedback on health-related aspects. Methods: Twenty-five users (n = 25) assessed the new multimodal interaction capabilities and new communication services. A psychophysiological approach was adopted to investigate aspects like engagement, usability, and affective impact, as well as the possible role of individual differences on the quality of human-robot interaction. Results: ROBIN was overall judged usable, the interaction with/through it resulted pleasant and the required workload was limited, thus supporting the idea of using it as a central component for remote assistance and social participation. Open-minded users tended to have a more positive interaction with it. Conclusions: This work describes an enabling technology for remote assistance and social communication. It highlights the importance of being compliant with users' needs to develop solutions easy to use and able to foster their social connections. The role of personality appeared to be relevant for the interaction, underscoring a clear role of the service personalization.

In the last years, social robots have become a trending topic. Indeed, robots which communicate with us and mimic human behavior patterns are fascinating. However, while there is a massive body of research on their design and acceptance in different fields of application, their market potential has been rarely investigated. As their future integration in society may have a vast disruptive potential, this work aims at shedding light on the market potential, focusing on the assistive health domain. A study with 197 persons from Italy (age: M = 67.87; SD = 8.87) and Germany (age: M = 62.15; SD = 6.14) investigates cultural acceptance, desired functionalities, and purchase preferences. The participants filled in a questionnaire after watching a video illustrating some examples of social robots. Surprisingly, the individual perception of health status, social status as well as nationality did hardly influence the attitude towards social robots, although the German group was somewhat more reluctant to the idea of using them. Instead, there were significant correlations with most dimensions of the Almere model (like perceived enjoyment, sociability, usefulness and trustworthiness). Also, technology acceptance resulted strongly correlated with the individual readiness to invest money. However, as most persons consider social robots as “Assistive Technological Devices” (ATDs), they expected that their provision should mirror the usual practices followed in the two Countries for such devices. Thus, to facilitate social robots’ future visibility and adoption by both individuals and health care organisations, policy makers would need to start integrating them into official ATDs databases.

This article describes a systematic investigation on the expectations of potential users of Ambient Assisted Living (AAL) technologies. More specifically it describes the efforts made to elicit users expectations for services of an AAL system that ai

One of the main challenges of social robots concerns the ability to guarantee robust, contextualized and intelligent behavior capable of supporting continuous and personalized interaction with different users over time. This implies that robot behaviors should consider the specificity of a person (e.g., personality, preferences, assistive needs), the social context as well as the dynamics of the interaction. Ideally, robots should have a “mind" to properly interact in real social environments allowing them to continuously adapt and exhibit engaging behaviors. The authors’ long-term research goal is to create an advanced mind-inspired system capable of supporting multiple assistance scenarios fostering personalization of robot’s behavior. This article introduces the idea of a dual process-inspired cognitive architecture that integrates two reasoning layers working on different time scales and making decisions over different temporal horizons. The general goal is also to support an empathetic relationship with the user through a multi-modal interaction inclusive of verbal and non-verbal expressions based on the emotional-cognitive profile of the person. The architecture is exemplified on a cognitive stimulation domain where some experiments show personalization capabilities of the approach as well as the joint work of the two layers. In particular, a feasibility assessment shows the customization of robot behaviors and the adaptation of robot interactions to the online detected state of a user. Usability sessions were performed in laboratory settings involving 10 healthy participants to assess the user interaction and the robot’s dialogue performance.

Workload estimation is essential for artificial systems designed to assist users across various domains. These systems can provide personalized support by continuously assessing the user’s state and optimizing intervention strategies. Physiological data acquisition through advanced sensors enables objective and real-time workload estimation, offering a more reliable alternative to self-reported measures. Despite the growing interest in workload estimation, existing literature reviews are often domain-specific or focus on cognitive workload only, without providing a comprehensive analysis of methodologies for estimating both physical and cognitive workload across different applications. To address this gap, this systematic review analyzes 35 studies on multimodal physiological monitoring, examining feature extraction methodologies and supervised learning models used for workload estimation. The review identifies key challenges, including the need for standardized protocols, improved generalization across real-world scenarios, and the integration of adaptive artificial intelligence models. It underscores the role of sensor-based workload estimation in healthcare, rehabilitation, and assistive technologies, positioning it as a fundamental component for developing intelligent, user-centered, and adaptive human–machine interaction systems.

Background: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms. Recently, dance has started to be considered an effective intervention for people with PD. Several findings in the literature emphasize the necessity for deeper exploration into the synergistic impacts of dance therapy and exergaming for PD management. Moreover, socially engaging robotic platforms equipped with advanced interaction and perception features offer potential for monitoring patients’ posture and enhancing workout routines with tailored cues. Objective: This paper presents the results of the Social Robotics for Active and Healthy Ageing (SI-Robotics) project, aimed at designing an innovative rehabilitation program targeted at seniors affected by (early-stage) PD. This study therefore aims to assess the usefulness of a dance-based rehabilitation program enriched by artificial intelligence–based exergames and contextual robotic assistance in improving motor function, balance, gait, and quality of life in patients with PD. The acceptability of the system is also investigated. Methods: The study is designed as a technical feasibility pilot to test the SI-Robotics system. For this study, 20 patients with PD were recruited. A total of 16 Irish dance–based rehabilitation sessions of 50 minutes were conducted (2 sessions per week, for 8 wks), involving 2 patients at a time. The designed rehabilitation session involves three main actors: (1) a therapist, (2) a patient, and (3) a socially interacting robot. To stimulate engagement, sessions were organized in the shape of exergames where an avatar shows patients the movements they should perform to correctly carry out a dance-based rehabilitation exercise. Results: Statistical analysis reveals a significant difference on the Performance-Oriented Mobility Assessment scale, both on balance and gait aspects, together with improvements in Short Physical Performance Battery, Unified Parkinson Disease Rating Scale–III, and Timed Up and Go test, underlying the usefulness of the rehabilitation intervention on the motor symptoms of PD. The analysis of the Unified Theory of Acceptance and Use of Technology subscales provided valuable insights into users’ perceptions and interactions with the system. Conclusions: This research underscores the promise of merging dance therapy with interactive exergaming on a robotic platform as an innovative strategy to enhance motor function, balance, gait, and overall quality of life for patients grappling with PD.