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This paper proposes to evaluate the feasibility of obtaining the heart rate (HR) through remote Photoplethysmography (rPPG) while the subject is lying down in an emergency situation. The proposed methodology performs image and signal processing on frames containing part of a person’s skin to infer the blood volume variation signal, and then, estimate the HR. As the study evaluates the accuracy of the methodology in lying down subjects, three poses were chosen for the tests: Ventral Decubitus, Dorsal Decubitus and Lateral Decubitus. The results demonstrate the possibility to obtain reliable heart rate values in these conditions with average Root Mean Square Error (RMSE) of 3.60 bpm. Finally, it is also demonstrated that the method can be embedded in an ordinary smartphone to facilitate its use.

A socially assistive robot (termed N-MARIA – New Mobile Autonomous Robot for Interaction with Autistic) is being developed in UFES/Brazil to help the Autism Spectrum Disorder (ASD) therapy. This robot has speakers and color (RGB) and infrared cameras, allowing it to emit artificial voice (to motivate the children) and capture RGB and thermal images from the children to infer their emotions. The robot can operate autonomously (using its onboard sensors and a behavior-based control system) or be remotely commanded by a therapist. The controller uses data from a laser sensor to move towards the children until a social distance (according to the concepts of proxemics), with a mean error of 1.5% of the distance. Images from the children are used to infer their emotions, and thus adjust the robot behavior. The thermal camera gets information about their facial temperature (through the technique of Infrared Thermal Imaging – IRTI), and the RGB camera gets information about their Heart Rate Variation – HRV (through the technique of remote photoplethysmography – rPPG). The rPPG technique has sensitivity of 3 bpm when the child is static, and 15 bpm when the child is moving. Using HRV, it was possible to discriminate arousal (passive to active emotion) and valence (positive to negative emotion) with mean accuracy of 34% and 45%, respectively. On the other hand, the IRTI technique was used to determine the facial temperature (sensitivity < 0.07 °C) of the children, with a mean accuracy of 85.75% to determine five emotions. Both emotion estimation (based on rPPG and IRTI) are then used as input for a Decentralized Kalman Filter (DKF) and an Information Filter (IF) to infer the children’s emotion, and change the robot’s behaviors using an architecture operating dynamically for behavior selection.

This work consists of a pilot study in which brain signals captured by electroencephalography (EEG) of children diagnosed with Autism Spectrum Disorder (ASD) are evaluated during the interaction with a social robot. Social skills and interaction of children with ASD with the robot are proposed and assessed, using quantitative scale and images recorded by video cameras. During interaction with the robot, results show high activation of alpha and beta rhythms in brain regions important to social skills. Quantitative scales indicate a positive children–robot interaction and point out the social robot as a potential tool to stimulate social skills and facilitate the interaction with other people. Keywords: ASD, EEG, Robot, Social Skills

Child-Robot Interaction (CRI) has become increasingly addressed in research and applications. This work proposes a system for emotion recognition in children, recording facial images by both visual (RGB&mdash, red, green and blue) and Infrared Thermal Imaging (IRTI) cameras. For this purpose, the Viola-Jones algorithm is used on color images to detect facial regions of interest (ROIs), which are transferred to the thermal camera plane by multiplying a homography matrix obtained through the calibration process of the camera system. As a novelty, we propose to compute the error probability for each ROI located over thermal images, using a reference frame manually marked by a trained expert, in order to choose that ROI better placed according to the expert criteria. Then, this selected ROI is used to relocate the other ROIs, increasing the concordance with respect to the reference manual annotations. Afterwards, other methods for feature extraction, dimensionality reduction through Principal Component Analysis (PCA) and pattern classification by Linear Discriminant Analysis (LDA) are applied to infer emotions. The results show that our approach for ROI locations may track facial landmarks with significant low errors with respect to the traditional Viola-Jones algorithm. These ROIs have shown to be relevant for recognition of five emotions, specifically disgust, fear, happiness, sadness, and surprise, with our recognition system based on PCA and LDA achieving mean accuracy (ACC) and Kappa values of 85.75% and 81.84%, respectively. As a second stage, the proposed recognition system was trained with a dataset of thermal images, collected on 28 typically developing children, in order to infer one of five basic emotions (disgust, fear, happiness, sadness, and surprise) during a child-robot interaction. The results show that our system can be integrated to a social robot to infer child emotions during a child-robot interaction.

Assistive robotics is a field that has gained more visibility recently due to the social impacts it can provide. Currently, robots can be used to assist people in mobility, healthcare and even in behavioral issues, and thus, they are also known as social robots. This work shows the first contact of typically developing children with a robot, which will be used for interaction with children with Autism Spectrum Disorder further. The first contact with typically developed children is important to know if the robot is able to catch their interests and, which improvements the children would recommend about the robot shape and behavior. In total, 36 typically developing children had a first contact with the social robot and the experiment took place in elementary schools. Social skills, such as eye gaze, tactile interaction, communication, proximity and social engagement were evaluated. Surveys asked about the facial expressions of the robot and children’s likes, dislikes and feelings. The results showed that the children performed the social abilities satisfactorily, understood the robot emotional facial expressions easily, liked the robot and suggested improvements.

Facial emotion recognition is an important topic in the fields of computer vision and artificial intelligence, since has the potential to be a powerful tool to develop a wide variety of academic and commercial applications, such as human-computer interaction systems. Facial expression communication is especially effective because visual expressions are one of the main information channels in interpersonal communication. This work presents the development of a toolkit for Matlab, which allows online analysis of facial emotion. To facilitate the design of this system, four modules are implemented, which allow: data acquisition, feature extraction, expression classifier and graphic report of analysis. The results show that the six basic emotion classes were recognized by the computational system, with accuracy of 63.0% and 68.8% for LDA and KNN classifiers, respectively. These results are close to the success rates of other systems found in the literature that have an average of 63.2% accuracy. The use of the platform and the methods implemented in this work can benefit automatic emotion recognition applications, which require online processing and evaluation of human emotion objectively and non-intrusively.

Human-machine interaction has growing interest in studies about recognition of human emotions since these are relevant to the establishment of social relationships. Some markers of human emotions are physiological signals, such as heartbeat, brain signals, sweating and skin temperature. Many of them are captured through sensors with contact with the body, however, contact-free (unobtrusive) sensors are currently studied targeting those people who have sensitivity to touch, such as the case of people with Autism Spectrum Disorder (ASD). InfraRed Thermal Imaging (IRTI) is an unobtrusive technique for recording thermal variations on the skin. The goal of this work is to investigate the thermal variation from two emotions (happiness and sadness) on the face of five typically developing children through IRTI. The facial regions of interest (ROI) are forehead, cheeks, tip of nose and periorbital. This investigation is based on the brightness variation between the exposition to two emotional stimuli (evoked by videos) and the absence of stimuli (baseline). Values from Student’s t-test indicated significant differences between both emotional states (positive/happiness, and negative/sadness) and the baseline.

Socially assistive robotic has become increasingly active and it is present in therapies of people affected for several neurobehavioral conditions, such as Autism Spectrum Disorder (ASD). In fact, robots have played a significant role for positive interaction with children with ASD, by stimulating their social and cognitive skills. This work introduces a mobile socially-assistive robot, which was built for interaction with children with ASD, using non-linear control techniques for this interaction., {"references":["A. Tapus, M. J. Mataric and B. Scassellati, \"Socially assistive robotics (Grand Challenges of Robotics),\" in IEEE Robotics & Automation Magazine, vol. 14, no. 1, pp. 35-42, March 2007.","D. Feil-Seifer and M. J. Mataric, \"Defining socially assistive robotics,\" 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005, pp. 465-468.","D. Feil-Seifer and M. J. Matarić, \"Socially Assistive Robotics,\" in IEEE Robotics & Automation Magazine, vol. 18, no. 1, pp. 24-31, March 2011.","A. Duquette, F. Michaud, H. Marcier, \"Exploring the use of a mobile robot as an imitation agent with children with low-functioning autism.\" Autonomous Robot, vol. 24, pp.147-157, February 2008.","B. Robins, F. Amirabdollahian, Z. Ji and K. Dautenhahn, \"Tactile interaction with a humanoid robot for children with autism: A case study analysis involving user requirements and results of an initial implementation,\" 19th International Symposium in Robot and Human Interactive Communication, Viareggio, 2010, pp. 704-711.","B. Robins, E. Ferrari, K. Dautenhahn, G. Kronreif, B. Prazak-Aram, G. Gilderblom, B. Tanja, F. Caprino, E. Laudanna, P. Marti, \"Human-centred design methods: development scenarios for robot assisted play informed by user panels and field trials.\" International Journal of Human-Computer Studies, vol. 68, pp. 873-898, December 2010.","B. Scasselati, H. Admoni and M Mataric, \"Robots for use in Autism Research.\" Annual Review of Biomedical Engineering, vol. 14, pp. 275-294, August 2012.","J. J. Cabibihan, H. Javed, M. Ang, S. M. Aljunied, \"Why robots? A survey on the roles and benefits of social robots in the therapy of children with autism.\" International Journal of Social Robotics, vol. 5, pp. 593-618, November 2013.","E. S. Kim, L. D. Berkovits, E. P. Bernier, D. Leyzberg, F. Shic, R. Paul, B. Scassellati, \"Social Robots as Embedded Reinforcers of Social Behavior in Children with Autism.\" Journal of Autism and Developmental Disorders, vol. 43, pp. 1038-1049, May 2013.\n[10]\tH. Kozima, C. Nakagawa and Y. Yasuda, \"Interactive robots for communication-care: a case-study in autism therapy,\" ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005, 2005, pp. 341-346.\n[11]\tH. Kozima, M. P. Michalowski, and C. Nakagawa, \"Keepon: A playful robot for research, therapy, and entertainment,\" International Journal of Social Robotics, vol. 1, n. 1, pp. 3-18, January, 2009.\n[12]\tF. Michaud, C. Théberge-Turmel, \"Mobile robotic toys and autism.\" Socially Intelligent Agents. Kluwer Academic Publishers, Boston, 2002.\n[13]\tF. Michaud and S. Caron, \"Roball – an autonomous toy-rolling robot.\" Proceedings of the Workshop on Interactive Robotics and Entertainment, 2000.\n[14]\tC. Goulart, C. Valadão, E. M. O. Caldeira, T. F. Bastos-Filho, \"Maria: um robô para interação com crianças com autismo.\" XII Simpósio Brasileiro de Automação Inteligente 2015, 2015.\n[15]\tLow Cost 360 degree 2D Laser Scanner (LIDAR) System. Introduction to Standard SDK. RPLidar, 2014.\n[16]\tH. Secchi, \"Control de vehículos autoguiados con realimentación sensorial.\" Control de Vehículos Autoguiados con Realimentación Sensorial, 1998.\n[17]\tJ. M. Toibero, R. Carelli, B. Kuchen and L. Canali, \"Switching controllers for navigation with obstacles in unknown environments.\" IV Jornadas Argentinas de Robótica (JAR), 2006."]}

This paper presents the development of a smart walker that uses a formation controller in its displacements. Encoders, a laser range finder and ultrasound are the sensors used in the walker. The control actions are based on the user (human) location, who is the actual formation leader. There is neither a sensor attached to the user’s body nor force sensors attached to the arm supports of the walker, and thus, the control algorithm projects the measurements taken from the laser sensor into the user reference and, then, calculates the linear and angular walker’s velocity to keep the formation (distance and angle) in relation to the user. An algorithm was developed to detect the user’s legs, whose distances from the laser sensor provide the information necessary to the controller. The controller was theoretically analyzed regarding its stability, simulated and validated with real users, showing accurate performance in all experiments. In addition, safety rules are used to check both the user and the device conditions, in order to guarantee that the user will not have any risks when using the smart walker. The applicability of this device is for helping people with lower limb mobility impairments. Fil: Valadão, Carlos. Universidade Federal Do Espirito Santo. Centro Tecnológico; Brasil Fil: Caldeira, Eliete. Universidade Federal Do Espirito Santo. Centro Tecnológico; Brasil Fil: Bastos-Filho, Teodiano. Universidade Federal Do Espirito Santo. Centro Tecnológico; Brasil Fil: Frizera Neto, Anselmo. Universidade Federal Do Espirito Santo. Centro Tecnológico; Brasil Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina

Introduction: Autism Spectrum Disorder is a set of developmental disorders that imply in poor social skills, lack of interest in activities and interaction with people. Treatments rely on teaching social skills and in such therapies robotics may offer aid. This work is a pilot study, which aims to show the development and usage of a ludic mobile robot for stimulating social skills in ASD children. Methods: A mobile robot with a special costume and a monitor to display multimedia contents was designed to interact with ASD children. A mediator controls the robot’s movements in a room prepared for interactive sessions. Sessions are recorded to assess the following social skills: eye gazing, touching the robot and imitating the mediator. The interaction is evaluated using the Goal Attainment Scale and Likert scale. Ten children were evaluated (50% with ASD), using as inclusion criteria children with age 7-8, without use of medication, and without tendency to aggression or stereotyped movements. Results: It was observed that the ASD group touched the robot about twice more in average than the control group (CG). They also looked away and imitated the mediator in a quite similar way as the CG, and showed extra social skills (verbal and non-verbal communication). These results are considered an advance in terms of improvement of social skills in ASD children. Conclusions: Our studies indicate that the robot stimulated social skills in 4/5 of the ASD children, which shows that its concepts are useful to improve socialization and quality of life. Fil: Valadão, Carlos Torturella. Universidade Federal do Espírito Santo; Brasil Fil: Goulart, Christiane. Universidade Federal do Espírito Santo; Brasil Fil: Rivera, Hamilton. Universidade Federal do Espírito Santo; Brasil Fil: Caldeira, Eliete. Universidade Federal do Espírito Santo; Brasil Fil: Bastos Filho, Teodiano Freire. Universidade Federal do Espírito Santo; Brasil Fil: Frizera-Neto, Anselmo. Universidade Federal do Espírito Santo; Brasil Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan; Argentina

The use of optical flow to provide the sensorial information a mobile robot needs to navigate is addressed. An optical flow-based sensing subsystem is proposed to continuously detect obstacles, and a control subsystem is implemented to update the heading angle of the robot accordingly. All the computations are performed onboard the robot, due to the low memory and processing time demanded by the algorithms programmed, thus guaranteeing to the robot full autonomy. An experiment using such system is discussed, whose results validate the sensorial subsystem proposed.

This paper presents an implementation with mobile robot to generate actions to interact with children with autism. The robot is able to detect the child localization and approach him/her, keeping a safe proximity minimum (interaction distance). The mobile robot is equipped with laser sensor to obtain distances and an embedded control system for the interaction. The implementation allows two modes of interaction, depending on the degree of interaction with the child. The system here developed can help children with autism in the process of social evolution and be a tool for professionals and researchers of the area.

Manifestations of cognitive and neurobehavioral disorders such as repetitive movements, impaired communication and social interaction level characterize Autism Spectrum Disorder - ASD, a condition of unknown etiology and still without cure. Currently, many researches integrate robotics to the treatment of ASD with varied functions that allow interaction with children with this disorder. Therefore, evaluation criteria become important to analyze the efficiency the use of robotics in this interaction. The methods here proposed are Goal Attainment Scaling (GAS), System Usability Scale (SUS) and Fitts' Law. The use of such methods of evaluation allows transpose qualitative assessments into quantitative, making the work more robust and reliable.

This work addresses a coordinate control scheme for a helicopter and a formation of ground robots, based on artificial vision. First, a control scheme previously proposed is implemented to guide a formation of three ground mobile robots while they track a desired trajectory. In the sequel, a nonlinear controller based on the inverse dynamics of the aerial vehicle, focusing on its under actuated character, is used to guide its navigation. The mission to be accomplished by the helicopter consists in tracking the centroid of the ground formation. An important aspect to be emphasized is that the two controllers adopted operate totally independently, thus characterizing a decentralized control scheme. An artificial vision system onboard the rotorcraft is used to capture the positions of the ground vehicles, which are used to define the 3D-path to be followed by the helicopter. Simulation results validating the proposed system are presented and discussed.

Submitted by Patricia Barros (patricia.barros@ufes.br) on 2011-02-10T17:06:31Z No. of bitstreams: 2 a01v18n3.pdf: 272122 bytes, checksum: b6805381fad265455dd25dd59765931e (MD5) license_rdf: 21504 bytes, checksum: cfe16b28a928230b24b965de15e8a5ff (MD5) Approved for entry into archive by Patricia Barros(patricia.barros@ufes.br) on 2011-02-10T17:21:08Z (GMT) No. of bitstreams: 2 a01v18n3.pdf: 272122 bytes, checksum: b6805381fad265455dd25dd59765931e (MD5) license_rdf: 21504 bytes, checksum: cfe16b28a928230b24b965de15e8a5ff (MD5) Made available in DSpace on 2011-02-10T17:21:08Z (GMT). No. of bitstreams: 2 a01v18n3.pdf: 272122 bytes, checksum: b6805381fad265455dd25dd59765931e (MD5) license_rdf: 21504 bytes, checksum: cfe16b28a928230b24b965de15e8a5ff (MD5) Previous issue date: 2007-07 This work discusses the use of optical flow to generate the sensorial information a mobile robot needs to react to the presence of obstacles when navigating in a non-structured environment. A sensing system based on optical flow and time-to-collision calculation is here proposed and experimented, which accomplishes two important paradigms. The first one is that all computations are performed onboard the robot, in spite of the limited computational capability available. The second one is that the algorithms for optical flow and time-to-collision calculations are fast enough to give the mobile robot the capability of reacting to any environmental change in real-time. Results of real experiments in which the sensing system here proposed is used as the only source of sensorial data to guide a mobile robot to avoid obstacles while wandering around are presented, and the analysis of such results allows validating the proposed sensing system.

This work addresses the use of optical flow to supervise the navigation of mobile robots. The amount of computation, the number of image frames to be stored and the reliability of the optical flow vectors generated should be acquainted for by programming a suitable algorithm for the onboard calculation of the optical flow. Two good candidates to be this algorithm are selected from the literature and are here considered. A slight modification of one of them is here proposed, and thus three algorithms are evaluated. This evaluation is based on two experiments whose results are reported. The conclusion is that the new algorithm here proposed is more suitable for the application.

Background Autism or “Autism Spectrum Disorder” is characterized by manifestations of impairments in social behavior, stereotyped movements, difficulty in communication and interaction with people. This paper presents a system composed of a mobile robot to generate interactive tasks with autistic children, and an EEG (electroencephalography) equipment to get brain information to characterize their emotions.