This article reports a study aiming to determine the perceptions of older adults needing formal care about the usefulness, satisfaction, and ease of use of CaMeLi, a virtual companion based on an embodied conversational agent, and the perceptions of formal caregivers about the potential of virtual companions to support care provision. An observational study involving older adults needing formal care was conducted to assess CaMeLi using a multi-method approach (i.e., an auto-reported questionnaire—the Usefulness, Satisfaction, and Ease of use questionnaire; a scale for the usability assessment based on the opinion of observers—the International Classification of Functioning Disability and Health-based Usability Scale; and critical incident registration). Moreover, a focus group was conducted to collect data regarding the perceived utility of virtual companions to support care provision. The observational study was conducted with 46 participants with an average age of 63.6 years, and the results were associated with a high level of usefulness, satisfaction, and ease of use of CaMeLi. Furthermore, the focus group composed of four care providers considered virtual companions a promising solution to support care provision and to prevent loneliness and social isolation. The results of both the observational study and the focus group revealed good perceptions regarding the role of virtual companions to support the care provision for older adults.

The purpose of this paper was to understand how an agent's performance is affected when interaction workflows are incorporated in its information model and decision-making process. Our expectation was that this incorporation could reduce errors and faults on agent's operation, improving its interaction performance. We based this expectation on the existing challenges in designing and implementing artificial social agents, where an approach based on predefined user scenarios and action scripts is insufficient to account for uncertainty in perception or unclear expectations from the user. Therefore, we developed a framework that captures the expected behavior of the agent into descriptive scenarios and then translated these into the agent's information model and used the resulting representation in probabilistic planning and decision making to control interaction. Our results indicated an improvement in terms of specificity while maintaining precision and recall, suggesting that the hypothesis being proposed in our approach is plausible. We believe the presented framework will contribute to the field of cognitive robotics, e.g., by improving the usability of artificial social companions, thus overcoming the limitations imposed by approaches that use predefined static models for an agent's behavior resulting in non-natural interaction.

This paper aims to contribute to situation, activity, and goal awareness in cyber-physical human-machine systems (HMS) by presenting a new information model and specifications for a decision-making component that can be integrated in current system architectures. The objective of this work is to improve the efficacy, acceptance, adaptability, and overall performance of HMS and human-system interaction (HSI) applications using a context-based approach. Our hypothesis is that we can enhance current interaction functionalities by integrating context and interaction information models into a decision-making component that behaves as a supervision process for controlling interaction. In HSI, we aim to define a general human model that may lead to principles and algorithms, allowing more natural and effective interaction between humans and artificial agents. The approach was implemented and tested targeting application in the domain of active and assisted living. The challenge of user acceptance is of vital importance for future solutions and is still one of the major reasons for reluctance to adopt cyber-physical systems in this domain.

This paper proposes an approach for an automated system composed by mobile robots and a smart-room following service oriented architecture, aiming to undertake complex and heavily computational tasks to aid the user in the execution of determined tasks.

This paper proposes a new approach for the Power Spectrum (PS)-based feature extraction applied to probabilistic Laban Movement Analysis (LMA), for the sake of human behaviour understanding. A Bayesian network is presented to understand human action and behaviour based on 3D spatial data and using the LMA concept which is a known human movement descriptor. We have two steps for the classification process. The first step is estimating LMA parameters which are built to describe human motion situation by using some low level features. Then by having these parameters, it is possible to classify different human actions and behaviours. Here, a sample of using 3D acceleration data of six body parts to obtain some LMA parameters and understand some performed actions by human is shown. A new approach is applied to extract features from a signal data such as acceleration using the PS technique to achieve some of LMA parameters. A number of actions are defined, then a Bayesian network is used in learning and classification process. The experimental results prove that the proposed method is able to classify actions.