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Technology supported assistance is a research area dedicated to support both older adults and, at some level, their caregivers in a variety of situations and contexts. A number of projects doing detailed evaluation both with robots and/or ICT-based intelligent devices have identified as open challenges the need to guarantee both continuity and variability of service according to context interpretation. This paper starts from the willingness to study how both continuity and variability can be pursued by leveraging and integrating results from research areas like artificial intelligence (AI), cognitive systems, psychology and sensor networks. Some of these technological skills are needed for example by an assistive robot and still represent open challenges in AI. This paper presents a medium term research initiative aiming at synthesizing an enhanced (intelligent) control architecture for assistive robots that take advantage from the continuous flow of information provided by a sensor network. The paper presents two main results: (a)starting from the analysis of requirements coming from the real world, it envisages a conceptual cognitive architecture highlighting the functional requirements and the key capabilities characterizing an “ideal” intelligent assistive robot; (b)it presents a prototype of a testbed architecture called KOaLa (Knowledge-based cOntinuous Loop) which integrates sensor data representation, knowledge reasoning and decision making capabilities showing its novelty in a realistic scenario.

Socially assistive robotics aims at providing users with continuous support and personalized assistance, through appropriate social interactions. The design of robots capable of supporting people in heterogeneous tasks, raises several challenges among which the most relevant are the need to realise intelligent and continuous behaviours, robustness and flexibility of services and, furthermore, the ability to adapt to different contexts and needs. Artificial intelligence plays a key role in realizing cognitive capabilities like e.g., learning, context reasoning or planning that are highly needed in socially assistive robots. The integration of several of such capabilities is an open problem. This paper proposes a novel “cognitive approach” integrating ontology-based knowledge reasoning, automated planning and execution technologies. The core idea is to endow assistive robots with intelligent features in order to reason at different levels of abstraction, understand specific health-related needs and decide how to act in order to perform personalized assistive tasks. The paper presents such a cognitive approach pointing out the contribution of different knowledge contexts and perspectives, presents detailed functioning traces to show adaptation and personalization features, and finally discusses an experimental assessment proving the feasibility of the approach.

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 personalized fruition of cultural heritage, especially with the health crisis we are currently experiencing, poses challenges to the learning and educational processes that could benefit from the use of intelligent tools. By emphasizing the interactivity of the learning process, in particular, the recent Technology Enhanced Learning (TEL) methodologies can represent an enabling factor for the construction of the common knowledge. This paper pursues the idea of integrating different heterogeneous artificial intelligence techniques for the personalized administration of stimuli in a dynamic learning environment. With this in mind, this paper describes a system, called WikiTEL and its first instantiation to Cultural Heritage visits in the Matera city.

Background: Despite the evidence on the positive role of self-management, the adoption of health coaching strategies for older people is still limited. To address these gaps, recent efforts have been made in the ICT sector in order to develop systems for delivering coaching and overcoming barriers relating to scarcity of resources. The aim of this review is to examine the efficacy of personal health coaching systems for older adults using digital virtual agents. Methods: A systematic review of the literature was conducted in December 2019 analyzing manuscripts from four databases over the last 10 years. Nine papers were included. Results: Despite the low number of studies, there was evidence that technology-integrated interventions can deliver benefits for health over usual care. However, the review raises important questions about how to maintain benefits and permanence of behavior change produced by short-term interventions. Conclusion: These systems offer a potential tool to reduce costs, minimize therapist burden and training, and expand the range of clients who can benefit from them. It is desirable that in the future the number of studies will grow, considering other aspects such as the role of the virtual coaches’ characteristics, social-presence, empathy, usability, and health literacy.

IoT technology integrated in smart home environments produces a huge and heterogeneous amount of data that can be used to characterize events that occur inside an environment as well as activities that a person is performing. The knowledge that can be extracted from such data can be used to proactively facilitate the daily living of the person with physical and/or cognitive weaknesses. This paper describes work aiming at better taking advantage of those data by integrating a cognitive loop composed by a semantic representation and a decision making functionality. In particular, the paper presents results of the Knowledge-based cOntinuous Loop (KOaLa) research initiative particularly focusing on the use of semantic technology to represent and reason on sensor data to enable the triggering of proactive services toward the users in an intelligent home.

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 aims to both monitor older people fostering their independent living and preventing dangerous situations and also offers a valid help to their caregivers. The specific target of our work is older adults, with no critical pathological conditions, who wish to stay home as long as possible. The work proposes a plan for a rigorous systematization of the users needs and their validation based on a combination of qualitative and quantitative research techniques involving both the primary users (older users at home) and the secondary users (formal and informal caregivers) who constitute the network of persons around them. Four main areas relevant to the realization of personalized services have been identified that could help to support independent living and improve Quality of Life (QoL) which include the monitoring of Social Interaction, Physiological Data, Daily Activities and Environmental Data. The presented results are also consistent with the global theoretical framework of International Classification of the Functioning, Disability and Health (ICF) which highlights the main factors contributions to independent living. This study also investigates the perceived usefulness and acceptance of specific services (considering both primary and secondary users perspective) that have been listed inside each category with the aim to indicate a clear priority level and preferences of users. The article concludes by providing insights on practical implications for the systems development, presenting concrete examples of possible use cases relevant for the AAL domain linked to the elicited user requirements.

The deployment of autonomous robots capable to both socially interact and proactively offer support to human users in a common life scenario remains challenging despite the recent technical advancements. For instance, research for endowing autonomous robots with the capability of acting in “non-ideal” and partially observable environments as well as socially interacting with humans is still an area in which improvements are specifically needed. To this aim, this paper elaborates on the need for integrating different Artificial Intelligence (AI) techniques to foster the development of personal robotic assistants continuously supporting older adults. Recently, the authors have been working on proposing an AI-based cognitive architecture that integrates knowledge representation and automated planning techniques in order to endow assistive robots with proactive and context situated abilities. This paper particularly describes a goal triggering mechanism to allow a robot to reason over the status of the user and the living environment with the aim of dynamically generating high-level goals to be planned accordingly.

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.

Despite the growth of advanced communication technologies, smart devices etc. the main approach to training strategic planners for crisis management (Gold Commanders) continues to be a paper-based, collective group dynamic exercise. The Pandora project has developed an advanced smart environment for the training of Gold Commanders which uses AI planning techniques to provide a crisis scenario modelled as an event network. This includes points of decision for trainees managed by automated rules from a knowledge base, behavioural modelling of the trainees, and ambient management of the environment to provide affective inputs to control and manage trainee stress. In this context, the system controls and reacts to trainee performance in relation to the events and decision points and can dynamically remodel and reconfigure the event network to respond appropriately to trainee decisions. Trainees can also be pressurised through compression of the timelines or ambient management of the multimedia information presented within the environment, causing them to make decisions under stress or with inadequate information. The environment can also represent any missing trainees within the scenario, which provides the potential to offer a completely autonomous facility for scenario design and test, and potentially a decision support facility, based on a build-up of empirical evidence from real world and training situations. In summary, the Pandora system integrates its computational intelligence, with the intelligence of the trainer and the trainees, to provide an emotionally engaging, augmented reality/virtual reality training environment for crisis managers.

Telepresence robotic systems are proposed in different contexts and specifically in the area of social robotics for assisting older adults at home. Similarly to other robotic systems, such robots are often designed and then evaluated in laboratory settings for a limited period of time. Lab-based evaluations present limitations because they do not take into account the different challenges imposed by the fielding of robotic solutions into real contexts for longer periods. In order to perform long-term experiments in real ecological settings it is very important to define a structured approach to assess the impact of a prolonged and constant use of the telepresence robot. This paper proposes a methodology in the area of elderly people support, called MARTA, for M ultidimensional A ssessment of telepresence R obo T for older A dults. It introduces the main variables of interest as well as the instruments and administration timeline for assessing relevant changes that may occur over time. MARTA is also validated in a one year-long case study during which a telepresence robot, called Giraff, has been deployed and iteratively assessed. The paper also provides remarks on the technology readiness and suggestions for its improvements.

Ambient Assisted Living (AAL) systems should offer services to the users that should be designed, tested, and delivered in real scenarios to gain robustness. This paper explores the issues of creating end-to-end services for older adults and their caregivers deploying an AAL system at home. It describes the work done for designing added value services starting from a state-of-the-art continuous data gathering infrastructure. The paper presents the general idea and its basic ingredients, then it focuses on the work done for creating services on top of the data gathering layer. In particular, it dwells on the used deployment approach and provides the results from one particular test site.

In Ambient Assisted Living scenarios, elderly people are supported by pervasive and smart environments in their daily mobility and activities, with which they interact by means of a variety of devices and services deployed in the house. These devices, in addition to their particular function, can provide useful context information about the user behavior. In this paper we propose an indoor localization technique that exploits the presence of commercially available devices in the GiraffPlus AAL environment. GiraffPlus is a EU funded project, running in several test sites across Europe, that combines social interaction and long-term monitoring to help people live independently. In this context, the proposed localization algorithm is able to provide the position information in terms of last room visited by the user improving considerably the reaction of a user's caregiver in emergency situations. This paper also aims at providing a performance characterization of the commercially available devices used in the GiraffPlus AAL environments. We prove that, in order to guarantee an high level of reliability, this kind of device show a considerable transmission delay that makes the localization process non-trivial.

This paper explores the issue of creating end-to-end services for older adults and their caregivers starting from a continuous gathering of sensor data from their living environment. The described work is part of the GiraffPlus project in which an intelligent AAL environment has been developed. The whole system relies on a state-of-the-art middleware for sensor data gathering and the pursued perspective is the one of designing added value services based on such data. The paper presents the general project concept and its different ingredients then presents two different services: a reasoner for person tracking, and an interaction service that connects the human network involved in a given application environment.

This chapter presents a telehealth system called GiraffPlus supporting independent living of elderly in their own home. GiraffPlus system is a complex system which monitors activities and physiological parameters in the home using a network of sensors. The elaborated information is presented to the primary user, the elderly, and to secondary users like health care and home care providers and possibly to family members as a help to assess possible health and wellbeing dete- rioration, provide acute alarms, and support health procedure. The secondary users can also visit the elderly via the Giraff, a teleoperated robot that can communicate and move in the home under the control of the secondary user. The chapter fo- cusses in particular on the deployment of the system in six real homes in Sweden, Italy and Spain. The chapter outlines the technological various components used, the expectations of the users and the evaluation method.

This paper presents an ongoing effort to create added value complete services for end users on top of an AAL continuous data gathering environment. It first presents the general architecture proposed by the GiraffPlus project for long term monitoring of older users at home, then describes the main choices concerning the middleware (universAAL compliant), the long-term data storage, and a user-oriented component, called DVPIS, dedicated to interaction with different users of the AAL environment. The current status of a deployed version of the system is then shortly described.

The human ability to take the right decisions is very important in real world critical situations. An interesting problem always worth being investigated concerns how to teach decision making skills to humans. The real nature of taking decisions is extremely difficult to describe in detail and, as a consequence, training it according to fixed protocols is also challenging. This is because it comes out as a combination of natural talent, competence from previous experience, ability to quick reasoning, leadership, resilience to stress, and so on. We have addressed this problem while building a new learning environment to train crisis decision makers. The environment, called Pandora, is grounded on Artificial Intelligence planning techniques known as ''timeline-based''. This technology is used to create and manipulate segments of lesson's content over time. Planning a lesson corresponds to logically organize events over time that are then rendered in front of trainees during the lesson's actual enactment. This paper shows how the machinery of continuous plan adaptation is functional to create variety and novelty in the lessons thus engaging the trainees during the teaching interaction. In particular, it shows the different uses of plan adaptation to take into account the basic reactivity of the trainees, the background deductions from user modeling, and the mixed-initiative interactions guided by the trainer.

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. Particular emphasis is put on user evaluation outside the laboratories. A distinctive aspect of the project is that the GiraffPlus system will be installed and evaluated in at least 15 homes of elderly people. The concept of "useworthiness" is central in order to assure that the GiraffPlus system provides services that are easy to use and worth using. In addition, by using existing and affordable components we strive to achieve a system that is affordable and close to commercialization.