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Peripheral diabetic neuropathy (PDN) is a complication of type 2 diabetes (T2DM) that impairs posture control and increases the risk of falling. The aim of this study was to characterize the anteroposterior center of pressure (COP-AP) in the time and the frequency domains in the T2DM/PDN group in relation to the control group. To that end we: (1) evaluated the efficacy of using both linear and non-linear discrete wavelet transform (DWT) analyses to evaluate oscillation patterns in the anteroposterior center of pressure (COP-AP) in the bipedal position in terms of time and frequency and (2) established input parameters for a model for predicting the risk of falling. This study included an experimental sample of 30 people with T2DM/PDN matched by gender, age, weight and height with a control sample of 30 healthy individuals. Unreported techniques for analyzing the COP-AP literature were assessed for their capacity to model patient bodily stability in the proprioceptive, visual and vestibular systems. To measure COP-AP, five tests were performed under different conditions as outlined in the Romberg Test using the “PEDAR System” for measuring plantar pressure. DWTs were used to calculate excursion parameters, average speeds, range, RMS values, the average maximum and minimum amplitude, power spectral densities and energy percentages in 11 frequency bands (D1 to D10 and A10). There were significant differences between the two study groups in terms of the calculated linear parameters (p

The unceasing aging of the population is leading to new problems in developed countries. Robots represent an opportunity to extend the period of independent living of the elderly as well as to ameliorate their economic burden and social problems. We present a new social robot, Mini, specifically designed to assist and accompany the elderly in their daily life either at home or in a nursing facility. Based on the results of several meetings with experts in this field, we have built a robot able to provide services in the areas of safety, entertainment, personal assistance and stimulation. Mini supports elders and caregivers in cognitive and mental tasks. We present the robot platform and describe the software architecture, particularly focussing on the human–robot interaction. We give in detail how the robot operates and the interrelation of the different modules of the robot in a real use case. In the last part of the paper, we evaluated how users perceive the robot. Participants reported interesting results in terms of usability, appearance, and satisfaction. This paper describes all aspects of the design and development of a new social robot that can be used by other researchers who face the multiple challenges of creating a new robotic platform for older people. The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economía y Competitividad; and Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), funded by the Ministerio de Ciencia, Innovación y Universidades. Publicado

Although previous studies revealed the positive perception effect of corporate social responsibility (CSR) on consumer behavior, the influence mechanism and the impact of individual characteristics on the effect have been underexplored. The purpose of this study is not only to examine the mediating role of the emotion of admiration in the relationship between consumer perception of CSR and advocacy behaviors but also to explore the moderating role of the consumer moral virtue of integrity in the relationship between CSR perception and feelings of admiration. Data was collected from customers of a food company located in Spain, and the results show that the company's CSR practices positively influence consumer advocacy behaviors both directly and indirectly through consumer admiration for the company. Consumer integrity moderates the relationship between consumer perception of CSR and admiration. Specifically, CSR practices have a stronger admiration effect on consumers who are high in integrity. The overall importance of consumer perceptions of CSR suggest that, in the food industry, the perception of appropriate CSR practices has a positive impact on consumer attitudes and behaviors, which are also conditioned by individuals' moral virtues.

The aim of this study was to evaluate the energy potential of the Prados de la Montana (PM) landfill in Mexico City. The quality of biogas generated by this landfill was monitored in 2013 under passive and forced extraction conditions across 112 extraction wells forming an integrated biogas extraction network. Based on the preliminary results, we can conclude the following: (1) the methane content of biogas produced at the PM landfill was around 45–69%. (2) Forced extraction favored air intrusion through superficial cracks and reduced the methane content of the biogas mixture. (3) Assuming a recovery efficiency of 75%, the biogas flux was estimated at 1081 m3 h−1 (at least 1.8 MW) according to the Mexican model of biogas v. 2.0 for the year 2013. This flux value was less than the value obtained from direct measurement (1505 m3 h−1). A biogas recovery of 908 m3 h−1 is currently estimated, equivalent to 1.5 MW. In conclusion, a good quality and high quantity of biogas is produced at the PM landfill. The characteristics and location of the PM landfill make this landfill an opportune site for biogas production.

This paper comprises recent theoretical advances for the design and analysis of stone columns. This second part comprises some comments about the estimation of column diameter, about groups of columns and about excessive settlements induced by soil secondary compression. The number of columns, if the area replacement ratio is kept constant, has a negligible influence on the settlement reduction and the column position has a small influence on the settlement reduction. The critical column length is about twice the footing width or diameter. The paper also shows the lower effectiveness of stone columns in reducing the settlements induced by soil secondary compression. When secondary compression settlements are expected to be important, soil overconsolidation by preloading is advisable., Este artículo recoge algunos aspectos teóricos recientes relativos a columnas de grava que pueden resultar útiles para su diseño y cálculo. En esta segunda parte, se recogen comentarios sobre la estimación del diámetro de las columnas, sobre grupos de columnas, y sobre la problemática específica causada por asientos de consolidación secundaria. Se muestra que el número de columnas mientras se mantenga el área de sustitución bajo la cimentación no tiene influencia en el valor del asiento y que la posición de las columnas dentro de la cimentación tiene una influencia muy pequeña. La longitud crítica de las columnas es del orden de 2 veces el ancho o diámetro de la cimentación superficial. También se muestra la menor eficacia de las columnas de grava en la reducción de asientos por consolidación secundaria. Cuando se estime que los asientos por consolidación secundaria pueden ser importantes, lo recomendable es realizar una sobrecarga.

This study aims to explore and understand whether corporate social responsibility (CSR) could be considered as an indirect cue of certain product characteristics, turning it into an effective instrument to encourage credibility and trust in a food brand and thereby influence consumers’ purchase intention; the study also considers how consumers’ integrity influences these effects of trust and credibility. The proposed model and the mediation and moderation hypotheses were tested with conditional process analysis and were generally supported by data collected from 252 consumers. The results have important implications for companies, revealing that CSR dimensions impact consumer purchase intention by generating greater credibility and trust. Moreover, among consumers with higher integrity, the effects are more significant. Finally, it is the economic dimension of CSR that has the clearest impact on consumer intentions.

This paper comprises recent theoretical advances that may be useful for the design and analysis of stone columns. In this first part, a calculation example is developed using different methods to study the settlement reduction and the consolidation process. The calculation methods used for this example have generally provided satisfactory results, considering the possibilities and limitations of each method. Semi-empirical methods are easy to use because the settlement reduction is obtained directly, or almost directly, from the area replacement ratio. Recently developed analytical methods based on the unit cell model consider the influence of other parameters, but information about the value of those input parameters is usually limited. Finally, finite element analyses are highly flexible, but a deep understanding of the calculation hypotheses, constitutive models and input parameters is required., Este artículo recoge algunos aspectos teóricos recientes relativos a columnas de grava que pueden resultar útiles para su diseño y cálculo. En esta primera parte, se presenta un ejemplo de cálculo de reducción de asientos y aceleración de tiempos de consolidación utilizando diferentes métodos. De forma general, los métodos de cálculo utilizados han proporcionado resultados adecuados si se tienen en cuenta las limitaciones y posibilidades de cada una de ellos. Los métodos semi-empíricos son sencillos porque proporcionan la reducción de asiento en función principalmente del factor de sustitución. Los métodos analíticos desarrollados recientemente basados en la celda unidad permiten considerar la influencia de otros parámetros, pero normalmente la información disponible respecto a su valor es limitada. Finalmente, los cálculos por elementos finitos proporcionan una gran flexibilidad en el análisis, pero es necesaria una adecuada comprensión de las hipótesis, modelos constitutivos y parámetros empleados.

We are developing a social robot that should autonomously interact long-term with pediatric oncology patients. The child and the robot need to get acquainted with one another before a long-term interaction can take place. We designed five interaction design patterns and two sets of robot behaviors to structure a getting acquainted interaction. We discuss the results of a user study (N = 75, 8–11 y.o.) evaluating these patterns and robot behaviors. Specifically, we are exploring whether the children successfully got acquainted with the robot and to what extent the children bonded with the robot. Results show that children effectively picked up how to talk to the robot. This is important, because the better the performance the more comfortable the children are, the more socially attractive the robot is, and the more intimate the conversation gets. The evaluation furthermore revealed that it is important for children, in order to get familiar with the robot, to have shared interests with the robot. Finally, most children did initiate a bond with the robot.

The success of social robotics is directly linked to their ability of interacting with people. Humans possess verbal and non-verbal communication skills, and, therefore, both are essential for social robots to get a natural human&ndash, robot interaction. This work focuses on the first of them since the majority of social robots implement an interaction system endowed with verbal capacities. In order to do this implementation, we must equip social robots with an artificial voice system. In robotics, a Text to Speech (TTS) system is the most common speech synthesizer technique. The performance of a speech synthesizer is mainly evaluated by its similarity to the human voice in relation to its intelligibility and expressiveness. In this paper, we present a comparative study of eight off-the-shelf TTS systems used in social robots. In order to carry out the study, 125 participants evaluated the performance of the following TTS systems: Google, Microsoft, Ivona, Loquendo, Espeak, Pico, AT&amp, T, and Nuance. The evaluation was performed after observing videos where a social robot communicates verbally using one TTS system. The participants completed a questionnaire to rate each TTS system in relation to four features: intelligibility, expressiveness, artificiality, and suitability. In this study, four research questions were posed to determine whether it is possible to present a ranking of TTS systems in relation to each evaluated feature, or, on the contrary, there are no significant differences between them. Our study shows that participants found differences between the TTS systems evaluated in terms of intelligibility, expressiveness, and artificiality. The experiments also indicated that there was a relationship between the physical appearance of the robots (embodiment) and the suitability of TTS systems.

In recent years, the role of social robots is gaining popularity in our society but still learning from humans is a challenging problem that needs to be addressed. This paper presents an experiment where, after teaching poses to a robot, a group of users are asked several questions whose answers are used to create feature filters in the robot’s learning space. We study how the answers to different types of questions affect the learning accuracy of a social robot when it is trained to recognize human poses. We considered three types of questions: “Free Speech Queries”, “Yes/No Queries”, and “Rank Queries”, building a feature filter for each type of question. Besides, we provide another filter to help the robot to reduce the effects of inaccurate answers: the Extended Filter. We compare the performance of a robot that learned the same poses with Active Learning (using the four feature filters) versus Passive Learning (without filters). Our results show that, despite the fact that Active Learning can improve the robot’s learning accuracy, there are some cases where this approach, using the feature filters, achieves significant worse results than Passive Learning if the user provides inaccurate feedback when asked. However, the Extended Filter has proven to maintain the benefits of Active Learning even when the user answers are not accurate.

The aim of this study was to determine the hazard ratio (HQ), the risk index (HI), and the cancer risk index (CRI) for populations of adults and children exposed to ingestion, dermal contact and inhalation of heavy metals in agricultural soil. For these, the contents of Cd, Pb, Ni, Cu, Co, Cr, Zn, and the metalloid As were determined in soils of four zones of the sub-basin of Alto Balsas, during two different periods of the year. The average content of metals in the soil was 1.24, 14.77, 14.80, 13.06, 5.50, 17.65, 22.89, and 5.32 mg kg−1 for Cd, Pb, Ni, Cu, Co, Cr, Zn, and As, respectively. The highest risk in terms of HQ and HI was for adults, especially for men who are affected through the skin, with Cd and Cr being the most dangerous. CRI values were within the allowable range, without posing problems for adult and child populations.

In this paper is reported the “in vitro” release kinetic studies of antiepileptic drugs released from an inorganic, titanium oxide (TiO2) porous matrix. In order to determine the drug release mechanism, the experimental values were fitted to different mathematical models: zero-order, firs-order, Higuchi, Hixson-Crowel and Peppas. TiO2 was prepared by the sol-gel method adding valproic acid (VPA) or phenytoine (DHP) during the titanium n-butoxide hydrolysis step. The drug-TiO2 systems were observed by scanning electron microscopy. The “in vitro” release experiments were performed at laboratory scale following the United States Pharmacopeia (USP) standards. The obtained materials have a morphology of nanoparticle agglomerates. The particles have different sizes with some roughness and spherical shape. Peppas model suggests for both systems, that the release mechanism is controlled by two parallel processes. The first one is by diffusion of the drug through the matrix and the second is related to a gradient of constant diffusion by ingress of the solvent in the matrix.

Nowadays, robots and humans coexist in real settings where robots need to interact autonomously making their own decisions. Many applications require that robots adapt their behavior to different users and remember each user’s preferences to engage them in the interaction. To this end, we propose a decision making system for social robots that drives their actions taking into account the user and the robot’s state. This system is based on bio-inspired concepts, such as motivations, drives and wellbeing, that facilitate the rise of natural behaviors to ease the acceptance of the robot by the users. The system has been designed to promote the human-robot interaction by using drives and motivations related with social aspects, such as the users’ satisfaction or the need of social interaction. Furthermore, the changes of state produced by the users’ exogenous actions have been modeled as transitional states that are considered when the next robot’s action has to be selected. Our system has been evaluated considering two different user profiles. In the proposed system, user’s preferences are considered and alter the homeostatic process that controls the decision making system. As a result, using reinforcement learning algorithms and considering the robot’s wellbeing as the reward function, the social robot Mini has learned from scratch two different policies of action, one for each user, that fit the users’ preferences. The robot learned behaviors that maximize its wellbeing as well as keep the users engaged in the interactions. The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economía y Competitividad (DPI2014-57684-R); and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU (S2013/MIT-2748). Publicado

The highly toxic species of Chromium in its hexavalent state is an important hazard to the flora and fauna, causing a rupture in balance especially in aquatic environments. The removal of Cr(VI) ions from aqueous solutions using fungal biomass of Rhizopus sp. was investigated under batch experiments. The biomass was produced and treated with NaCl to compare pre-treated and untreated biosorbents capacity. Adsorption of Cr(VI) was investigated with a 23 experimental design to determine the best operational parameters including pH [2.0–4.0], temperature [20–40 °C] and agitation [50–150 rpm]. Maximum Cr(VI) uptake (99%) indicated that pH 2.0 is the optimal for Cr(VI) removal. Linear and non-linear kinetic models were evaluated. The best fitting for linear kinetics was the pseudo-second order linear equation and the Elovich model in its non-linear form, suggesting chemisorption as the controlling step of adsorption. Results followed Langmuir isotherm equation, the q m was 9.95 (mg·g−1) for Rhizopus sp. + NaCl. Thermodynamic parameters were calculated using the adsorption equilibrium constant obtained from Langmuir isotherm and indicated that the adsorption process was spontaneous and endothermic. The surface characteristics of the biomass were analyzed by Fourier transform infrared (FTIR) spectra; the analysis showed the involvement of amino groups in the bonding with Cr(VI). SEM and EDX analysis confirmed the presence of Cr in the biomass after adsorption. The results of these experiments may be utilized for modeling, simulation, and scale-up processes in the future.

In many scenarios where robots could assist humans, handover situations are essential. But they are still challenging for robots, especially if these are initiated by the human interaction partner. Human-human handover studies report average reaction times of 0.4 s, which is only achievable for robots, if they are able to predict the object transfer point (OTP) sufficiently early and then adapt to the human movement. In this paper, we propose a hand tracking system that can be used in the context of human initiated handover as a basis for human reaching motion prediction. The OTP prediction implemented is based on the minimum jerk model and combines a static estimation utilizing the human’s initial pose and a dynamic estimation from the current hand trajectory. Results are generated and analyzed for a broad spectrum of human initiated scenarios. For these cases we examine the dynamics of different variants of the proposed prediction algorithm, i.e., how early is a robot’s prediction of the OTP within a certain error range? The tracking delivers results with an average delay, after the initialization, of 0.07 s. We show that the OTP prediction delivers results after 75 % of the movement within a 10 cm precision box.

Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interaction. Inline with this natural, effective communication, robots need to perceive and manage multimodal information, both as input and output, and respond accordingly. Consequently, dialogue design is a key factor in creating an engaging multimodal interaction. These dialogues need to be flexible enough to adapt to unforeseen circumstances that arise during the conversation but should also be easy to create, so the development of new applications gets simpler. In this work, we present our approach to dialogue modelling based on basic atomic interaction units called Communicative Acts. They manage basic interactions considering who has the initiative (the robot or the user), and what is his/her intention. The two possible intentions are either ask for information or give information. In addition, because we focus on one-to-one interactions, the initiative can only be taken by the robot or the user. Communicative Acts can be parametrised and combined in a hierarchical manner to fulfil the needs of the robot&rsquo, s applications, and they have been equipped with built-in functionalities that are in charge of low-level communication tasks. These tasks include communication error handling, turn-taking or user disengagement. This system has been integrated in Mini, a social robot that has been created to assist older adults with cognitive impairment. In a case of use, we demonstrate the operation of our system as well as its performance in real human&ndash, robot interactions.

The literature highlights the growing dissatisfaction of consumers with foods from industrial production systems. Their concern for health and the desire to support local producers led them to search for other alternatives: products that are local, traditional and typical of a particular region, such as many Geographical Indication (GI) food products. This paper explores the relationship between the purchase of GI food products, the attitudes towards proximity, tradition, and ethnicity, and consumer ethnocentric tendencies. The results, obtained through a mediation analysis, show that aspects such as products made by small companies and transparency (in terms of their production process) attain greater significance among consumers with higher levels of sub-national ethnocentrism. Moreover, these consumers appreciate that: (1) the products have been made using traditional methods, and (2) in addition to be typical of the region, these products can be bought directly from the producer, without intermediaries. Consumers who value these aspects, closely related to each other, present a greater purchase intention and a higher purchase frequency of food products with protected geographical status.

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way. The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU.

Social robots are becoming more prevalent in our daily environments. In these contexts, many researchers have studied how robots communicate with people. However, not enough attention has been paid to social interaction between robots and people with disabilities. In particular, little attention has been devoted to deaf and blind children interacting with robots. In this work we propose a physical haptic interaction mode that allows the communication between anthropomorphic robots and people with visual-auditory impairments. We have developed a scenario where this new interaction mode will be evaluated.

In Social Robotics, being able to interact with users in a natural way is a key feature. To achieve this, we need to model dialogues that allow the robot to complete its tasks and to adapt to unforeseen changes in the conversation. We present an approach where these dialogues are modelled as a combination of basic interaction units, called Communicative Acts or CAs. With this, our system aims to provide all the necessary tools so each of the robot’s applications can tailor their own dialogues in a simplier way. These applications make the decisions that need task-related information and request the activation of the CAs in order to create complex dialogues. The CAs handle decisions that require communication-related information (e.g. giving the user some information, or asking a question). They also manage some of the problems that can appear in any interaction, like not being able to understand the other peer, or not getting an answer to a question. A case study associated to a cognitive stimulation exercise is presented in this paper to validate our system.

In recent years, the demand for caregivers and therapists has raised together with the life expectancy. Elder people demand adapted environments and specialized care that allows for an active ageing. In order to assist in their daily tasks, social robots appear as a complement to caregivers. In this demo we introduce a robot capable to conduct cognitive stimulation sessions, complementing the therapists or even conducting the sessions autonomously. This robot has been designed to work both at home and daycare facilities.

Social robots are intended to coexist and to communicate with humans in a natural way. This requires these robots to be able to identify people (and objects) around them to use that information during human-robot dialogs. In this work we present how electronic beacons can benefit the interactions between humans and social robots. In particular, Bluetooth 4.0 Low Energy beacons are presented as the most suitable option, among the up-to-date available technologies. In order to show the advantages of the system during human-robot interaction, first, we present the integration of the information provided by these devices in the robot’s dialog system; and after, a hidden toy hunt game is described as a case study of a scenario where electronic beacons ease the interaction between humans and a social robot. The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad (DPI2014-57684-R); and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU (S2013/MIT-2748).

User detection, recognition, and tracking is at the heart of human–robot interaction, and yet, to date, no universal robust method exists for being aware of the people in a robot’s surroundings. The present article imports into existing social robotic platforms different techniques, some of them classical, and other novel, for detecting, recognizing, and tracking human users. The outputs from the parallel execution of these algorithms are then merged, creating a modular, expandable, and fast architecture. This results in a local user mapping through fusion of multiple user recognition techniques. The different people detectors comply with a common interface called PeoplePoseList Publisher, while the people recognition algorithms meet an interface called PeoplePoseList Matcher. The fusion of all these different modules is based on the Unscented Kalman Filtering technique. Extensive benchmarks of the subcomponents and of the whole architecture demonstrate the validity and interest of all levels of the architecture. In addition, all the software and data sets generated in this work are freely available.

This paper introduces a proposal for integrating personal assistant robots with social capacities in smart environments. The personal robot will be a fundamental element for the detection and healthy regulation of the affect of the environment’s inhabitants. A full description of the main features of the proposed personal assistant robot are introduced. Also, the multi-modal emotion detection and emotion regulation modules are fully described. Machine learning techniques are employed for emotion recognition from voice and images and both outputs are merged to achieve the detected emotion.

This study aimed to determine the heavy metal content in cow’s milk produced in areas irrigated with waste water and to evaluate the health risk with daily consumption of milk for children. The sample consisted of four zones in which small farmers were selected and the milk of 160 cows in two seasons of the year. On average, the metals in the milk in decreasing order were 0.36; 0.046; 0.035; 0.029; 0.015; 0.012, and 0.002 mg kg−1 for Zn, Pb, As, Cu, Cr, Ni, and Cd, respectively. The Pb exceeded the limits allowed by Codex. The values shown in the hazard quotient for the As of more than 1 and HI were higher. On the other hand, the individual risk of cancer showed a descending order As> Cd> Cr> Pb, while the total risk indicated that the combined effect of metals put girls and boys at serious risk.

Non-verbal sounds (NVS) constitute an appealing communicative channel for transmitting a message during a dialog. They provide two main benefits, such as they are not linked to any particular language, and they can express a message in a short time. NVS have been successfully used in robotics, cell phones, and science fiction films. However, there is a lack of deep studies on how to model NVS. For instance, most of the systems for NVS expression are ad hoc solutions that focus on the communication of the most prominent emotion. Only a small number of papers have proposed a more general model or dealt directly with the expression of pure communicative acts, such as affirmation, denial, or greeting. In this paper we propose a system, referred to as the sonic expression system (SES), that is able to generate NVS on the fly by adapting the sound to the context of the interaction. The system is designed to be used by social robots while conducting human robot interactions. It is based on a model that includes several acoustic features from the amplitude, frequency, and time spaces. In order to evaluate the capabilities of the system, nine categories of communicative acts were created. By means of an online questionnaire, 51 participants classified the utterances according to their meaning, such as agreement, hesitation, denial, hush, question, summon, encouragement, greetings, and laughing. The results showed how very different NVS generated by our SES can be used for communicating. Publicado

Decision-making is the natural way that makes any animal autonomous. To improve the autonomy of social robots, we propose to apply a bio-inspired decision-making system to manage the actions related with the human-robot interaction. In this paper, we present the system and how we apply it to one-by-one entertainment scenarios. Our robot has been equipped with several devices for interaction such as an interactive tablet, a microphone, a speaker, and a camera. Thus, the goal of the system is to have a robot deciding its own actions based on the preferences of the user.

Interpreting users messages, both verbal and non-verbal is essential to achieve a natural Human-Robot Interaction. Traditionally, Social Robots, and particularly Care Robots, rely on interfaces such as voice, touch or images to acquire information from users although the latter is usually used to locate them. This manuscript present the main steps of machine learning-based approach, from the skeleton extraction to the features computation and the classification necessary to detect dynamic gestures, which provide more information than simple poses. 123 classification algorithms have been employed to analyse the performance and accuracy of the system. To train these classifiers a, 30 users were recording while performing 14 dynamic gestures, obtaining 1355 instances of 900 features for each of these. Results indicate that Random Forest classifier achieves the highest F-score using cross-validation.

In order to consider robots as social partners rather than mere machines, they have to be perceived as living entities. In this paper we present a system to improve the robot’s liveliness based on the execution of non-repetitive, unpredictable, subtle actions that are running continuously during the operation of the robot. The so-called Liveliness System uses the existing interfaces, or communicative channels, and combines its actions with other systems working in the robot. The system has been successfully implemented in two social robots and the operation of the system has been showed during a competitive 2 player game played by a robot and a person.

An important aspect in Human-Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot's shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot's shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed among a set of possibilities: stroke, tap, slap, and tickle (touch classification). This proposal is cost-effective since just a few microphones are able to cover the whole robot's shell since a single microphone is enough to cover each solid part of the robot. Besides, it is easy to install and configure as it just requires a contact surface to attach the microphone to the robot's shell and plug it into the robot's computer. Results show the high accuracy scores in touch gesture recognition. The testing phase revealed that Logistic Model Trees achieved the best performance, with an F-score of 0.81. The dataset was built with information from 25 participants performing a total of 1981 touch gestures. The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU. Publicado

Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.

One of robot designers' main goals is to make robots as sociable as possible. Aside from improving robots' actual social functions, a great deal of effort is devoted to making them appear lifelike. This is often achieved by endowing the robot with an anthropomorphic body. However, psychological research on the perception of animacy suggests another crucial factor that might also contribute to attributions of animacy: movement characteristics. In the current study, we investigated how the combination of bodily appearance and movement characteristics of a robot can alter people's attributions of animacy, likability, trustworthiness, and unpleasantness. Participants played games of Tic-Tac-Toe against a robot which (1) either possessed a human form or did not, and (2) either exhibited smooth, lifelike movement or did not. Naturalistic motion was judged to be more animate than mechanical motion, but only when the robot resembled a human form. Naturalistic motion improved likeability regardless of the robot's appearance. Finally, a robot with a human form was rated as more disturbing when it moved naturalistically. Robot designers should be aware that movement characteristics play an important role in promoting robots' apparent animacy. HighlightsMovement characteristics influenced the robot's animacy, likability, and unpleasantness.Baxter was considered to be more likeable when it exhibited naturalistic motion.A full-bodied robot executing mechanistic movement was considered particularly inanimate.A full-bodied robot executing naturalistic movements was particularly unpleasant.

Biogas technology represents an option to enhance sustainable energy use in developing nations particularly in the rural context. However, the production and use of biogas could also take place in urban settings in these countries with potential benefits for their sustainability. The present work introduces a set of nine indicators in the economic, social and environmental sustainability dimensions for assessing the impact of a small-scale biogas plant on the energy sustainability of a restaurant located in Mexico City. Indicators were evaluated before (base scenario) and after (biogas scenario) biogas plant installation and then they were linearly normalized using a scale between 0 and 1 corresponding to a growing level of energy sustainability. Economic dimension indicators averaged 0.67 in the base scenario and 0.68 in the biogas scenario; those of the social dimension, 0.52 and 0.54; and those of the environmental dimension, 0.17 and 0.49, respectively. Results indicate a positive impact of biogas plant on restaurant energy sustainability. These indicators provide objective elements to examine in detail biogas contributions in strengthening energy sustainability of cities in developing countries.

Robots have begun to assist elders and patients suffering dementia. In particular, recent studies have shown how robots can benefit Alzheimer's disease (AD) patients. This is a novel area with a promising future but lot of researching needs to be done. The RobAlz project is aimed to assist AD patients and their caregivers by social robots. This project is divided in three phases: the definition of the requirements and scenarios, the development of a new robotic platform, and the evaluation. This work presents the results obtained in the first phase, in which several meetings were conducted with a set of subject-matter experts in the areas of Alzheimer's Disease and social robotics. The meetings were classified according to the application areas they covered: general aspects, safety, entertainment, personal assistance, and stimulation. The meetings ended up with a repertory of scenarios where robots can be applied to Alzheimer's patients and their caregivers at their home or in longterm care facilities. These scenarios present different psychological, social and technical concerns that must be addressed for the design of the robot. In this work we perform an analysis on the scenarios and present the technical requirements for the development of a first robotic prototype. This prototype will be constructed and tested in real environments in the subsequent phases of the RobAlz project. The authors gratefully acknowledge the collaboration of the Spanish Alzheimer Foundation (FAE) and the funds provided by the Spanish Government through the project Aplicaciones de los robots sociales DPI2011-26980, from the Spanish Ministry of Economy and Competitiveness.

This paper introduces the architecture of an emotion-aware ambient intelligent and gerontechnological project named “Improvement of the Elderly Quality of Life and Care through Smart Emotion Regulation”. The objective of the proposal is to find solutions for improving the quality of life and care of the elderly who can or want to continue living at home by using emotion regulation techniques. A series of sensors is used for monitoring the elderlies’ facial and gestural expression, activity and behaviour, as well as relevant physiological data. This way the older people’s emotions are inferred and recognized. Music, colour and light are the stimulating means to regulate their emotions towards a positive and pleasant mood. Then, the paper proposes a gerontechnological software architecture that enables real-time, continuous monitoring of the elderly and provides the best-tailored reactions of the ambience in order to regulate the older person’s emotions towards a positive mood. After describing the benefits of the approach for emotion recognition and regulation in the elderly, the eight levels that compose the architecture are described. This work was partially supported by Spanish Ministerio de Economía y Competitividad/FEDER under TIN2013-47074-C2-1-R grant. José Carlos Castillo was partially supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism, operated by Universidad Complutense de Madrid. Publicado

One of the main concerns in developed countries is population ageing. Elder people are susceptible of suffering conditions which reduce quality of life such as apraxia of speech, a burden that requires prolongued therapy. Our proposal is intended to be a first step towards automated solutions that assist speech therapy through detecting mouth poses. This work proposes a system for vowel poses recognition from an RGB-D camera that provides 2D and 3D information. 2D data is fed into a face recognition approach able to accurately locate and characterize the mouth in the image space. The approach also uses 3D real world measures obtained after pairing the 2D detection with the 3D information. Both information sources are processed by a set of classifiers to ascertain the best option for vowel recognition.

This paper assesses how the accuracy in user’s answers influence the learning of a social robot when it is trained to recognize poses using Active Learning. We study the performance of a robot trained to recognize the same poses actively and passively and we show that, sometimes, the user might give simplistic answers producing a negative impact on the robot’s learning. To reduce this effect, we provide a method based on lowering the trust in the user’s responses. We conduct experiments with 24 users, indicating that our method maintains the benefits of AL even when the user answers are not accurate. With this method the robot incorporates domain knowledge from the users, mitigating the impact of low quality answers.

Robots are starting to be applied in areas which involve sharing space with humans. In particular, social robots and people will coexist closely because the former are intended to interact with the...

Proactively perceiving others’ intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments. This work proposes a first step towards embedding this skill in support robots for search and rescue missions. Predicting the responders’ intentions, indeed, will enable exploration approaches which will identify and prioritise areas that are more relevant for the responder and, thus, for the task, leading to the development of safer, more robust and efficient joint exploration strategies. More specifically, this paper presents an active intention recognition paradigm to perceive, even under sensory constraints, not only the target’s position but also the first responder’s movements, which can provide information on his/her intentions (e.g. reaching the position where he/she expects the target to be). This mechanism is implemented by employing an extension of Monte-Carlo-based planning techniques for partially observable environments, where the reward function is augmented with an entropy reduction bonus. We test in simulation several configurations of reward augmentation, both information theoretic and not, as well as belief state approximations and obtain substantial improvements over the basic approach.