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23 results on '"Eliete Caldeira"'

1. COVID-19 respiratory sound analysis and classification using audio textures

Author

Leticia Silva, Carlos Valadão, Lucas Lampier, Denis Delisle-Rodríguez, Eliete Caldeira, Teodiano Bastos-Filho, and Sridhar Krishnan

Subjects
COVID-19, local binary pattern, local ternary pattern, speech, cough, breath, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Since the COVID-19 outbreak, a major scientific effort has been made by researchers and companies worldwide to develop a digital diagnostic tool to screen this disease through some biomedical signals, such as cough, and speech. Joint time–frequency feature extraction techniques and machine learning (ML)-based models have been widely explored in respiratory diseases such as influenza, pertussis, and COVID-19 to find biomarkers from human respiratory system-generated acoustic sounds. In recent years, a variety of techniques for discriminating textures and computationally efficient local texture descriptors have been introduced, such as local binary patterns and local ternary patterns, among others. In this work, we propose an audio texture analysis of sounds emitted by subjects in suspicion of COVID-19 infection using time–frequency spectrograms. This approach of the feature extraction method has not been widely used for biomedical sounds, particularly for COVID-19 or respiratory diseases. We hypothesize that this textural sound analysis based on local binary patterns and local ternary patterns enables us to obtain a better classification model by discriminating both people with COVID-19 and healthy subjects. Cough, speech, and breath sounds from the INTERSPEECH 2021 ComParE and Cambridge KDD databases have been processed and analyzed to evaluate our proposed feature extraction method with ML techniques in order to distinguish between positive or negative for COVID-19 sounds. The results have been evaluated in terms of an unweighted average recall (UAR). The results show that the proposed method has performed well for cough, speech, and breath sound classification, with a UAR up to 100.00%, 60.67%, and 95.00%, respectively, to infer COVID-19 infection, which serves as an effective tool to perform a preliminary screening of COVID-19.

Published
2022
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2. Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms

Author

Ana Cecilia Villa-Parra, Ismael Criollo, Carlos Valadão, Leticia Silva, Yves Coelho, Lucas Lampier, Luara Rangel, Garima Sharma, Denis Delisle-Rodríguez, John Calle-Siguencia, Fernando Urgiles-Ortiz, Camilo Díaz, Eliete Caldeira, Sridhar Krishnan, and Teodiano Bastos-Filho

Subjects
COVID-19, respiratory diseases, telemedicine, diagnosis, biomedical sensors, machine learning, Chemical technology, TP1-1185
Abstract

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

Published
2022
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3. A New Socially Assistive Robot with Integrated Serious Games for Therapies with Children with Autism Spectrum Disorder and Down Syndrome: A Pilot Study

Author

João Antonio Campos Panceri, Éberte Freitas, Josiany Carlos de Souza, Sheila da Luz Schreider, Eliete Caldeira, and Teodiano Freire Bastos

Subjects
socially assistive robot, autistic spectrum disorder, Down syndrome, Serious Games, Chemical technology, TP1-1185
Abstract

This work introduces a new socially assistive robot termed MARIA T21 (meaning “Mobile Autonomous Robot for Interaction with Autistics”, with the addition of the acronym T21, meaning “Trisomy 21”, which is used to designate individuals with Down syndrome). This new robot is used in psychomotor therapies for children with Down syndrome (contributing to improve their proprioception, postural balance, and gait) as well as in psychosocial and cognitive therapies for children with autism spectrum disorder. The robot uses, as a novelty, an embedded mini-video projector able to project Serious Games on the floor or tables to make already-established therapies funnier to these children, thus creating a motivating and facilitating effect for both children and therapists. The Serious Games were developed in Python through the library Pygame, considering theoretical bases of behavioral psychology for these children, which are integrated into the robot through the robot operating system (ROS). Encouraging results from the child–robot interaction are shown, according to outcomes obtained from the application of the Goal Attainment Scale. Regarding the Serious Games, they were considered suitable based on both the “Guidelines for Game Design of Serious Games for Children” and the “Evaluation of the Psychological Bases” used during the games’ development. Thus, this pilot study seeks to demonstrate that the use of a robot as a therapeutic tool together with the concept of Serious Games is an innovative and promising tool to help health professionals in conducting therapies with children with autistic spectrum disorder and Down syndrome. Due to health issues imposed by the COVID-19 pandemic, the sample of children was limited to eight children (one child with typical development, one with Trisomy 21, both female, and six children with ASD, one girl and five boys), from 4 to 9 years of age. For the non-typically developing children, the inclusion criterion was the existence of a conclusive diagnosis and fulfillment of at least 1 year of therapy. The protocol was carried out in an infant psychotherapy room with three video cameras, supervised by a group of researchers and a therapist. The experiments were separated into four steps: The first stage was composed of a robot introduction followed by an approximation between robot and child to establish eye contact and assess proxemics and interaction between child/robot. In the second stage, the robot projected Serious Games on the floor, and emitted verbal commands, seeking to evaluate the child’s susceptibility to perform the proposed tasks. In the third stage, the games were performed for a certain time, with the robot sending messages of positive reinforcement to encourage the child to accomplish the game. Finally, in the fourth stage, the robot finished the games and said goodbye to the child, using messages aiming to build a closer relationship with the child.

Published
2021
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4. Emotion analysis in children through facial emissivity of infrared thermal imaging.

Author

Christiane Goulart, Carlos Valadão, Denis Delisle-Rodriguez, Eliete Caldeira, and Teodiano Bastos

Subjects
Medicine, Science
Abstract

Physiological signals may be used as objective markers to identify emotions, which play relevant roles in social and daily life. To measure these signals, the use of contact-free techniques, such as Infrared Thermal Imaging (IRTI), is indispensable to individuals who have sensory sensitivity. The goal of this study is to propose an experimental design to analyze five emotions (disgust, fear, happiness, sadness and surprise) from facial thermal images of typically developing (TD) children aged 7-11 years using emissivity variation, as recorded by IRTI. For the emotion analysis, a dataset considered emotional dimensions (valence and arousal), facial bilateral sides and emotion classification accuracy. The results evidence the efficiency of the experimental design with interesting findings, such as the correlation between the valence and the thermal decrement in nose; disgust and happiness as potent triggers of facial emissivity variations; and significant emissivity variations in nose, cheeks and periorbital regions associated with different emotions. Moreover, facial thermal asymmetry was revealed with a distinct thermal tendency in the cheeks, and classification accuracy reached a mean value greater than 85%. From the results, the emissivity variations were an efficient marker to analyze emotions in facial thermal images, and IRTI was confirmed to be an outstanding technique to study emotions. This study contributes a robust dataset to analyze the emotions of 7-11-year-old TD children, an age range for which there is a gap in the literature.

Published
2019
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5. Visual and Thermal Image Processing for Facial Specific Landmark Detection to Infer Emotions in a Child-Robot Interaction

Author

Christiane Goulart, Carlos Valadão, Denis Delisle-Rodriguez, Douglas Funayama, Alvaro Favarato, Guilherme Baldo, Vinícius Binotte, Eliete Caldeira, and Teodiano Bastos-Filho

Subjects
Viola-Jones, facial emotion recognition, facial expression recognition, facial detection, facial landmarks, infrared thermal imaging, homography matrix, socially assistive robot, Chemical technology, TP1-1185
Abstract

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—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.

Published
2019
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6. A New Controller for a Smart Walker Based on Human-Robot Formation

Author

Carlos Valadão, Eliete Caldeira, Teodiano Bastos-Filho, Anselmo Frizera-Neto, and Ricardo Carelli

Subjects
smart walker, robotic walker, accessibility, mobility, assistive technology, Chemical technology, TP1-1185
Abstract

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.

Published
2016
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9. Remote Vital Sign Monitoring in Accidents

Author

Roger S. Rosa, Alan Floriano, Eliete Caldeira, Lucas Lampier, and Teodiano Bastos-Filho

Subjects
Dorsum, Signal processing, Mean squared error, business.industry, Computer science, Photoplethysmogram, LATERAL DECUBITUS, Computer vision, Artificial intelligence, business, Lying, Signal, Vital sign monitoring
Abstract

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.

Published
2021
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10. Development of a Socially Assistive Robot Controlled by Emotions Based on Heartbeats and Facial Temperature of Children with Autistic Spectrum Disorder

Author

Vinícius Binotte, Denis Delisle, Eliete Caldeira, Carlos Valadão, Christiane Goulart, Lucas Lampier, Teodiano Bastos, and Guilherme Baldo

Subjects
Proxemics, business.industry, Computer science, RGB color model, Robot, Computer vision, Artificial intelligence, Kalman filter, Valence (psychology), business, Autonomous robot, Behavior-based robotics, Information filtering system
Abstract

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.

Published
2020
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11. Applications of BCIs

Author

André Ferreira, Eduardo Henrique Couto Montenegro, Sandra Mara Torres Muller, Vivianne Cardoso, Alan Floriano, Celso De La Cruz Casaño, Jessica Paola Souza Lima, Alexandre Pomer-Escher, Anselmo Frizera-Neto, Teodiano Bastos-Filho, Jeevan K. Pant, Eliete Caldeira, Richard Junior Manuel Godinez-Tello, Alexandre Luís Cardoso Bissoli, Denis Delisle Rodriguez, Ana Cecilia Villa-Parra, Flavia Loterio, Javier Ferney Castillo Garcia, Christiane Goulart, Maria Dolores Pinheiro de Souza, Mario Sarcinelli-Filho, Leandro Bueno, Dharmendra Gurve, Hamilton Rivera-Flor, Sridhar Krishnan, Muhammad Hasan, Alejandra Romero-Laiseca, Anibal Cotrina Atencio, Kevin A. Hernandez-Ossa, Thomaz Rodrigues Botelho, and Berthil Longo

Published
2020
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12. Brain signal evaluation of children with Autism Spectrum Disorder in the interaction with a social robot

Author

Christiane Goulart, Carlos Valadão, Teodiano Bastos, and Eliete Caldeira

Subjects
0106 biological sciences, Social robot, medicine.diagnostic_test, lcsh:Biotechnology, SIGNAL (programming language), technology, industry, and agriculture, High activation, Electroencephalography, medicine.disease, 01 natural sciences, behavioral disciplines and activities, body regions, Social skills, Autism spectrum disorder, 010608 biotechnology, lcsh:TP248.13-248.65, mental disorders, medicine, Robot, Beta Rhythm, Psychology, human activities, 010606 plant biology & botany, Cognitive psychology
Abstract

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

Published
2019

13. Visual and Thermal Image Processing for Facial Specific Landmark Detection to Infer Emotions in a Child-Robot Interaction

Author

Teodiano Bastos-Filho, Christiane Goulart, Vinícius Binotte, Alvaro Favarato, Douglas Funayama, Carlos Valadão, Eliete Caldeira, Guilherme Baldo, and Denis Delisle-Rodriguez

Subjects
Male, Computer science, socially assistive robot, Emotions, 02 engineering and technology, infrared thermal imaging, lcsh:Chemical technology, 01 natural sciences, Biochemistry, Analytical Chemistry, homography matrix, Image Processing, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TP1-1185, Child, Instrumentation, media_common, Discriminant Analysis, Fear, Robotics, Atomic and Molecular Physics, and Optics, Facial Expression, Sadness, Pattern Recognition, Visual, facial landmarks, Principal component analysis, Female, 020201 artificial intelligence & image processing, Viola–Jones object detection framework, Algorithms, Reference frame, facial detection, media_common.quotation_subject, Emotion classification, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Article, Humans, facial expression recognition, Electrical and Electronic Engineering, facial emotion recognition, Landmark, business.industry, Dimensionality reduction, 010401 analytical chemistry, Pattern recognition, Linear discriminant analysis, 0104 chemical sciences, Viola-Jones, Face, Artificial intelligence, business
Abstract

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.

Published
2019

16. Social Robot for Interaction with Children

Author

Carlos Valadão, Douglas Funayama, Alvaro Favaratto, Vinícius Binotte, Christiane Goulart, Eliete Caldeira, and Teodiano Bastos-Filho

Subjects
Facial expression, Social robot, media_common.quotation_subject, Social engagement, medicine.disease, Feeling, Social skills, Autism spectrum disorder, medicine, Robot, Eye tracking, Psychology, media_common, Cognitive psychology
Abstract

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.

Published
2019
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18. Development of a Toolkit for Online Analysis of Facial Emotion

Author

Teodiano Bastos-Filho, Hamilton Rivera, Carlos Valadão, Sridhar Krishnan, and Eliete Caldeira

Subjects
Facial expression, Computer science, business.industry, Feature extraction, Interpersonal communication, Machine learning, computer.software_genre, Expression (mathematics), Facial Action Coding System, Data acquisition, Artificial intelligence, MATLAB, business, computer, Classifier (UML), computer.programming_language
Abstract

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.

Published
2019
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19. Emotional State Analysis Through InfraRed Thermal Imaging

Author

Eliete Caldeira, Teodiano Bastos-Filho, Douglas Funayama Tavares, Denis Delisle-Rodriguez, Christiane Goulart, and Carlos Valadão

Subjects
medicine.medical_specialty, Heartbeat, media_common.quotation_subject, Emotional stimuli, Audiology, medicine.disease, Thermal variation, Sadness, medicine.anatomical_structure, Autism spectrum disorder, medicine, Forehead, Infrared thermal imaging, Happiness, Psychology, media_common
Abstract

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.

Published
2019
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21. Control Strategies for a Robot for Interaction with Children with Autism Spectrum Disorder

Author

Binotte, Vinicius, Baldo, Guilherme, Goulart, Christiane, Valadão, Carlos, Eliete Caldeira, and Teodiano Bastos

Subjects
body regions, autonomous control, Socially assistive robotics, mental disorders, technology, industry, and agriculture, autism, human activities, behavioral disciplines and activities, mobile robot
Abstract

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."]}

Published
2017
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22. A New Controller for a Smart Walker Based on Human-Robot Formation

Author

Teodiano Bastos-Filho, Anselmo Frizera-Neto, Eliete Caldeira, Carlos Valadão, and Ricardo Carelli

Subjects
0209 industrial biotechnology, Engineering, smart walker, Relation (database), robotic walker, Control (management), Stability (learning theory), purl.org/becyt/ford/2.2 [https], 02 engineering and technology, INGENIERÍAS Y TECNOLOGÍAS, Walking, SMART WALKER, lcsh:Chemical technology, Biochemistry, ACCESSIBILITY, Human–robot interaction, Article, Analytical Chemistry, ROBOTIC WALKER, 03 medical and health sciences, 020901 industrial engineering & automation, 0302 clinical medicine, Control theory, assistive technology, Humans, lcsh:TP1-1185, 030212 general & internal medicine, Electrical and Electronic Engineering, Instrumentation, Simulation, Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información, Ingeniería de Sistemas y Comunicaciones, accessibility, mobility, business.industry, ASSISTIVE TECHNOLOGY, Robotics, Atomic and Molecular Physics, and Optics, Range (mathematics), purl.org/becyt/ford/2 [https], MOBILITY, Artificial intelligence, business, Encoder, Algorithms
Abstract

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

Published
2016

23. Analysis of the use of a robot to improve social skills in children with autism spectrum disorder

Author

Ricardo Carelli, Carlos Valadão, Eliete Caldeira, Christiane Goulart, Teodiano Freire Bastos Filho, Anselmo Frizera-Neto, and Hamilton Rivera

Subjects
lcsh:Biotechnology, education, Biomedical Engineering, purl.org/becyt/ford/2.2 [https], INGENIERÍAS Y TECNOLOGÍAS, Social robots, behavioral disciplines and activities, Developmental psychology, Likert scale, 03 medical and health sciences, 0302 clinical medicine, Social skills, lcsh:TP248.13-248.65, medicine, 0501 psychology and cognitive sciences, Autism spectrum disorder, Set (psychology), Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información, lcsh:R5-920, Ingeniería de Sistemas y Comunicaciones, Social robot, Aggression, 05 social sciences, Socialization, Mobile robot, medicine.disease, purl.org/becyt/ford/2 [https], Assistive technology, medicine.symptom, lcsh:Medicine (General), Psychology, 030217 neurology & neurosurgery, 050104 developmental & child psychology
Abstract

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

Published
2016

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