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Auditory hallucinations are common and distressing symptoms of the schizophrenia disease. It is commonly treated with pharmacological approaches but, unfortunately, such an approach is not effective in all patients. In the cases in which the use of antipsychotic drugs is not possible or not recommended, psychotherapeutic interventions are used to help patients gain power and control against hearing voices. Recently, virtual reality technologies have been incorporated to this type of therapies. A virtual representation of their voice (avatar) is created in a controlled computer-based environment, and the patient is encouraged to confront it. Unfortunately, the software tools used in these therapies are not described in depth and, even more important, to the best of our knowledge, their usability, utility and intention to use by therapists, and patients have not been evaluated enough. The involvement of end users in the software development is beneficial in obtaining useful and usable tools. Hence, the two contributions of this paper are (1) the description of an avatar creation system and the main technical details of the configuration of auditory hallucination avatars, and (2) its evaluation from both the therapists’ and the patients’ viewpoints. The evaluation does not only focus on usability, but also assesses the acceptance of the technology as an important indicator of the future use of a new technological tool. Moreover, the most important results, the lessons learned and the main limitations of our study are discussed.

The increased life expectancy poses certain challenges such as bringing the required facilities to the elderly or infirm to help them keep their autonomy as long as possible. At any age, we are all prone to diseases or accidents that can affect our autonomy for activities we perform daily, the so-called Activities of Daily Living (ADL). After the ability to perform some ADLs is lost or reduced to some extent, a rehabilitation process is required. Doing rehabilitation in a controlled environment, such as a virtual one, brings interesting benefits, such as precise monitoring, a safe environment or a rehabilitation setting customized to each user’s progress. In this work, we present UrbanRehab, a tool designed in collaboration with specialists aimed at building customized rehabilitation experiences especially for Instrumental Activities of Daily Living that runs in a virtual urban environment (VUE) that can be built by the specialist visually by adding new buildings, street junctions, traffic lights or even dynamic objects such as people or cars. Billboards can be placed in the virtual scenario to provide further assistance to the user whenever required. The user interacts with the VUE by moving around in a natural manner. To assess the tool, an acceptance evaluation was conducted inspired by an UAUT2 model. Thirty specialists took part in this evaluation. The specialists gave the tool a score greater than 5 out of 7 for Behavioral Intention, thus showing the interest of these specialists in using our tool in a clinical environment.

This paper focuses on the validation of a tool designed to improve affect recognition, a fundamental aspect of social cognition as it greatly affects the functionality and quality of life of patients with mental disorders. The presented tool facilitates the generation of multi-modal avatar-based therapies by mental health professionals in this important clinical domain. Moreover, the tool for remediation of social cognitive deficits may be customised to each patient’s impairment. This paper describes how the tool was assessed by therapists after viewing a video explaining its most relevant aspects. The participants were asked to fill in a questionnaire based on UTAUT2 for the study of the acceptance and use of this technology. In light of the results obtained from 41 therapists about their intention of use, the most important statement is that their interest for this kind of tools is high. Nonetheless, there are some factors that negatively affect their behavioural intention.

People with schizophrenia have difficulty recognizing the emotions in the facial expressions of others, which affects their social interaction and functioning in the community. Static stimuli such as photographs have been used traditionally to examine deficiencies in the recognition of emotions in patients with schizophrenia, which has been criticized by some authors for lacking the dynamism that real facial stimuli have. With the aim of overcoming these drawbacks, in recent years, the creation and validation of virtual humans has been developed. This work presents the results of a study that evaluated facial recognition of emotions through a new set of dynamic virtual humans previously designed by the research team, in patients diagnosed of schizophrenia. The study included 56 stable patients, compared with 56 healthy controls. Our results showed that patients with schizophrenia present a deficit in facial affect recognition, compared to healthy controls (average hit rate 71.6% for patients vs 90.0% for controls). Facial expressions with greater dynamism (compared to less dynamic ones), as well as those presented from frontal view (compared to profile view) were better recognized in both groups. Regarding clinical and sociodemographic variables, the number of hospitalizations throughout life did not correlate with recognition rates. There was also no correlation between functioning or quality of life and recognition. A trend showed a reduction in the emotional recognition rate as a result of increases in Positive and Negative Syndrome Scale (PANSS), being statistically significant for negative PANSS. Patients presented a learning effect during the progression of the task, slightly greater in comparison to the control group. This finding is relevant when designing training interventions for people with schizophrenia. Maintaining the attention of patients and getting them to improve in the proposed tasks is a challenge for today’s psychiatry.

Unmanned aerial vehicles (UAVs) represent a new model of social robots for home care of dependent persons. In this regard, this article introduces a study on people’s feeling of safety and comfort while watching the monitoring trajectory of a quadrotor dedicated to determining their condition. Three main parameters are evaluated: the relative monitoring altitude, the monitoring velocity and the shape of the monitoring path around the person (ellipsoidal or circular). For this purpose, a new trajectory generator based on a state machine, which is successfully implemented and simulated in MATLAB/Simulink®, is described. The study is carried out with 37 participants using a virtual reality (VR) platform based on two modules, UAV simulator and VR Visualiser, both communicating through the MQTT protocol. The participants’ preferences have been a high relative monitoring altitude, a high monitoring velocity and a circular path. These choices are a starting point for the design of trustworthy socially assistive UAVs flying in real homes.

The threshold of detection is the minimum intensity of a signal to be detected by human sense of touch. Touch is a great challenge in virtual reality research. Virtual reality systems are nothing but telepresence systems where the remote environment is a digital simulation, so it can benefit from the haptic technology developed for remote manipulation. The tactile and force information people daily get from their hands in the real world is hard to provide with one single technology. Many projects can be found in literature in which vibro-tactile technology is used to transmit haptic information in different areas. The automotive industry is also benefiting from vibration technology to create warning systems for drivers. Cadillac, for example, has a system of vibro-tactile actuators in the seat that warn the driver of possible road hazards, which are collected through a system of radar, ultrasonic, and vision sensors.

This paper presents a Mixed Reality system that results from the integration of a telepresence system and an application to improve collaborative space exploration. The system combines free viewpoint video with immersive projection technology to support non-verbal communication, including eye gaze, inter-personal distance and facial expression. Importantly, these can be interpreted together as people move around the simulation, maintaining natural social distance. The application is a simulation of Mars, within which the collaborators must come to agreement over, for example, where the Rover should land and go.\ud The first contribution is the creation of a Mixed Reality system supporting contextualization of non-verbal communication. Tw technological contributions are prototyping a technique to subtract a person from a background that may contain physical objects and/or moving images, and a light weight texturing method for multi-view rendering which provides balance in terms of visual and temporal quality. A practical contribution is the demonstration of pragmatic approaches to sharing space between display systems of distinct levels of immersion. A research tool contribution is a system that allows comparison of conventional authored and video based reconstructed avatars, within an environment that encourages exploration and social interaction. Aspects of system quality, including the communication of facial expression and end-to-end latency are reported.

This paper presents the system architecture of a collaborative virtual environment in which distributed multidisciplinary teams involved in space exploration activities come together and explore areas of scientific interest of a planet for future missions. The aim is to reduce the current challenges of distributed scientific and engineering meetings that prevent the exploitation of\ud their collaborative potential, as, at present, expertise, tools and datasets are fragmented. This paper investigates the functional characteristics of a software framework that addresses these challenges following the design science research methodology in the context of the space industry and research.\ud An implementation of the proposed architecture and a validation process with end users, based on the execution of different use cases, are described. These use cases cover relevant aspects of real science analysis and operation, including planetary data visualization, as the system aims at being used in future European missions. This validation suggests that the system has the \ud potential to enhance the way space scientists will conduct space science research in the future.

The increase in the number of devices equipped with physiological sensors and their low price mean that they can be used in many fields. One of these fields is health-care and home-care for the elderly or people with disabilities. The development of such devices makes it possible to monitor their condition continuously and at all times. A continuous monitoring not only establishes an image of the user’s status, but also detects possible anomalies. Therefore, it is necessary to develop a distributed architecture that allows expert analysts to access the data provided by the sensors at all times and from anywhere. This paper introduces the development and implementation of the concept of distributed architecture, focusing on the minimum requirements needed to carry it out. All the necessary modules are described for different stages: acquisition, communication and processing of physiological signals. The last stage is carried out by a machine learning system. The complete reporting and storage system is also described. Finally, the most important conclusions that have emerged during the development are reported.

This article introduces a framework for assisting dependent people at home through a vision-based autonomous unmanned aerial vehicle (UAV). Such an aircraft equipped with onboard cameras can be useful for monitoring and recognizing a dependent's activity. This work is focused on the problem of planning the flight path of a quadrotor to perform monitoring tasks. The objective is to design a trajectory planning algorithm that allows the UAV to position itself for the sake of capturing images of the dependent person's face. These images will be later treated by a base station to evaluate the persons emotional state, together with his/her behavior, this way determining the assistance needed in each situation. Numerical simulations have been carried out to validate the proposed algorithms. The results show the effectiveness of the trajectory planner to generate smooth references to our previously designed GPI (generalized proportional integral) controller. This demonstrates that a quadrotor is able to perform monitoring flights with a high motion precision., This work has been partially supported by Spanish Ministerio de Ciencia, Innovacion y Universidades, Agencia Estatal de Investigacion (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant. Lidia M. Belmonte holds FPU014/05283 scholarship from Spanish Ministerio de Educacion y Formacion Profesional.

This paper introduces the design process of facial expressions on virtual humans to play basic emotions. The design of the emotions is grounded on the Facial Action Coding System that enables describing facial expressions based on Action Units. All the tools employed to attain the final human avatar expressions are detailed. Then, an experiment with healthy volunteers is carried out to validate the designed virtual human facial emotions. As result, we obtained that the faces are correctly interpreted by healthy people with an accuracy of \(83.56\%\). Thus, as recognition works quite well with this small sample of healthy people, this paper is a first step towards validating and enhancing the avatar characters generated, experimenting with a sufficient number of healthy persons, and, then, designing therapies based on human avatars to enhance facial affect recognition in patients with deficits in facial affect recognition.

The ability to recognise facial emotions is essential for successful social interaction. The most common stimuli used when evaluating this ability are photographs. Although these stimuli have proved to be valid, they do not offer the level of realism that virtual humans have achieved. The objective of the present paper is the validation of a new set of dynamic virtual faces (DVFs) that mimic the six basic emotions plus the neutral expression. The faces are prepared to be observed with low and high dynamism, and from front and side views. For this purpose, 204 healthy participants, stratified by gender, age and education level, were recruited for assessing their facial affect recognition with the set of DVFs. The accuracy in responses was compared with the already validated Penn Emotion Recognition Test (ER-40). The results showed that DVFs were as valid as standardised natural faces for accurately recreating human-like facial expressions. The overall accuracy in the identification of emotions was higher for the DVFs (88.25%) than for the ER-40 faces (82.60%). The percentage of hits of each DVF emotion was high, especially for neutral expression and happiness emotion. No statistically significant differences were discovered regarding gender. Nor were significant differences found between younger adults and adults over 60 years. Moreover, there is an increase of hits for avatar faces showing a greater dynamism, as well as front views of the DVFs compared to their profile presentations. DVFs are as valid as standardised natural faces for accurately recreating human-like facial expressions of emotions.

Estimation of human emotions plays an important role in the development of modern brain-computer interface devices like the Emotiv EPOC+ headset. In this paper, we present an experiment to assess the classification accuracy of the emotional states provided by the headset’s application programming interface (API). In this experiment, several sets of images selected from the International Affective Picture System (IAPS) dataset are shown to sixteen participants wearing the headset. Firstly, the participants’ responses in form of a self-assessment manikin questionnaire to the emotions elicited are compared with the validated IAPS predefined valence, arousal and dominance values. After statistically demonstrating that the responses are highly correlated with the IAPS values, several artificial neural networks (ANNs) based on the multilayer perceptron architecture are tested to calculate the classification accuracy of the Emotiv EPOC+ API emotional outcomes. The best result is obtained for an ANN configuration with three hidden layers, and 30, 8 and 3 neurons for layers 1, 2 and 3, respectively. This configuration offers 85% classification accuracy, which means that the emotional estimation provided by the headset can be used with high confidence in real-time applications that are based on users’ emotional states. Thus the emotional states given by the headset’s API may be used with no further processing of the electroencephalogram signals acquired from the scalp, which would add a level of difficulty.

This paper focuses on improving facial affect recognition, which is a fundamental aspect of social cognition. This is an important issue, as deficits in social cognition influence the functionality and quality of life of patients with brain disorders. In this regard, a proposal for designing avatar-based therapies dedicated to impaired patients is introduced. In addition, the validation of the tool by expert therapists is presented.