Use of robotics as a learning aid for disabled children

Severe disabled children have little chance of environmental and social exploration and discovery, and due to this lack of interaction and independency, it may lead to an idea that they are unable to do anything by themselves. Trying to help these children on this situation, educational robotics can offer and aid, once it can give them a certain degree of independency in exploration of environment. The system developed in this work allows the child to transmit the commands to a robot. Sensors placed on the child’s body can obtain information from head movement or muscle signals to command the robot to carry out tasks. With the use of this system, the disabled children get a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities

Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Background and Objective: Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). Methods: Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. Results: The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. Conclusion: These findings contribute to the development of more safe and practical BCI.Fil: Floriano, Alan. Universidade Federal do Espírito Santo; BrasilFil: Delisle Rodriguez, Denis. Universidade Federal do Espírito Santo; BrasilFil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Bastos Filho, Teodiano Freire. Universidade Federal do Espírito Santo; Brasi

Reconocimiento en-línea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes

[EN] This paper presents a methodology for online human action recognition on video sequences. It addresses an efficient approach to use invariant moments as image descriptors, applied in processing silhouettes obtained from depth maps. A quick comparison between size-4 windows (equivalent to 4 frames) is performed by computing the Mahalanobis distance, on one of the invariant moment sequences identified as less sensitive to noise and more stable during movement absence. This approach is used for rapid detection of the idle/motion state, which allows the capture of dynamic growth intervals (windows) for further processing, rescuing from the signal contained their temporal and frequential properties. By applying the Haar wavelet transform, three decomposition levels are used for calculating Relative Wavelet Energy (RWE - Relative Wavelet Energy) and SSC (Slope Sign Change), obtaining 11-dimensional patterns. In experiments, 97 % of 4 movements online-captured were recognized correctly, and 10 movements taken from Muhavi-MAS database were recognized with 94.2 % efficiency[ES] En este trabajo se presenta una metodología para el reconocimiento en-línea de acciones humanas en secuencias de vídeo. Se aborda un enfoque eficiente para el uso de momentos invariantes como descriptores de imagen, aplicados en siluetas obtenidas del procesamiento de mapas de profundidad. Una comparación rápida entre ventanas de tamaño 4 (equivalente a 4 frames) es realizada mediante el cómputo de la distancia de Mahalanobis, sobre una de las secuencias de momentos invariantes identificada como la menos sensible al ruido de captura y la más estable durante ausencia de movimiento. Este enfoque es usado para la detección rápida del estado de parada/movimiento, el cual permite la captura de intervalos (ventanas) de crecimiento dinámico para su posterior procesamiento, rescatando de la señal contenida sus propiedades temporales y frecuenciales. Mediante la aplicación de la transformada Wavelet Haar, tres niveles de descomposición son utilizados para el cómputo de la Energía Relativa Wavelet (RWE - Relative Wavelet Energy) y SSC (Slope Sign Change), obteniendo patrones 11-dimensionales. En experimentos realizados, el 97% de 4 movimientos capturados en-línea fueron reconocidos correctamente, y 10 movimientos tomados de la base de datos Muhavi-MAS fueron reconocidos con 94,2% de efectividad.Este proyecto de investigacion es financiado por el Programa Primeros Proyectos, CNPq/FAPES No. 02/2011 y por el CNPq a traves de beca de doctorado para el primer autor.Romero López, D.; Frizera Neto, A.; Freire Bastos, T. (2014). Reconocimiento en-línea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes. Revista Iberoamericana de Automática e Informática industrial. 11(2):202-211. https://doi.org/10.1016/j.riai.2013.09.009OJS202211112Antoniou, A., 2005. Digital Signal Processing. McGraw-Hill.Broggi, A., Bertozzi, M., Fascioli, A., Sechi, M., 2000. Shape-based pedestrian detection. In: Intelligent Vehicles Symposium, 2000. IV 2000. Proceedings of the IEEE. pp. 215-220.Chan, T., Vese, L., feb 2001. Active contours without edges. Image Processing, IEEE Transactions on 10 (2), 266-277.Chen, Q., Petriu, E., Yang, X., may 2004. A comparative study of fourier descriptors and hu's seven moment invariants for image recognition. In: Electrical and Computer Engineering, 2004. Canadian Conference on. Vol. 1. pp. 103-106 Vol.1.Chockalingam, P., Pradeep, N., Birchfield, S., oct. 2009. Adaptive fragments- based tracking of non-rigid objects using level sets. In: Computer Vision, 2009 IEEE 12th International Conference on. pp. 1530-1537.Cifuentes, C., Braidot, A., Rodriguez, L., Frisoli, M., Santiago, A., Frizera, A., june 2012. Development of a wearable zigbee sensor system for upper limb rehabilitation robotics. In: Biomedical Robotics and Biomechatronics (Bio- Rob), 2012 4th IEEE RAS EMBS International Conference on. pp. 1989-1994. DOI: 10.1109/BioRob.2012.6290926.Franke, U., Joos, A., 2000. Real-time stereo vision for urban traffic scene understanding. In: Intelligent Vehicles Symposium. Proceedings of the IEEE. pp. 273-278.Garcia-Costa, C., Egea-Lopez, E., Tomas-Gabarron, J., Garcia-Haro, J., Haas, Z., 2011. A stochastic model for chain collisions of vehicles equipped with vehicular communications. Intelligent Transportation Systems, IEEE Transactions on 13, 503-518.Geronimo, D., Lopez, A., Sappa, D., july 2010. Survey of pedestrian detection for advanced driver assistance systems. Pattern Analysis and Machine Intelligence, IEEE Transactions on 32 (7), 1239-1258.Gonzalez, R.C., 2010. Digital Image Processing, 2nd Edition. McGraw-Hill.Grubb, G., Zelinsky, A., Nilsson, L., Rilbe, M., june 2004. 3d vision sensing for improved pedestrian safety. In: Intelligent Vehicles Symposium, IEEE. pp. 19-24.Haibin, Z., Xu, W., Hong, W., may 2008. Feature selection using relative wavelet energy for brain-computer interface design. In: Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on. pp. 1434-1437.Hu, X., Kong, B., Zheng, F., Wang, S., july 2007. Image recognition based on wavelet invariant moments and wavelet neural networks. In: Information Acquisition, 2007. ICIA ‘07. International Conference on. pp. 275-279.Huang, Z., Leng, J., april 2010. Analysis of hu's moment invariants on image scaling and rotation. In: Computer Engineering and Technology (ICCET), 2010 2nd International Conference on. Vol. 7. pp. V7-476 –V7-480.Itti, L., Koch, C., Niebur, E., nov 1998. A model of saliency-based visual attention for rapid scene analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on 20 (11), 1254-1259.Jones, M., Snow, D., dec. 2008. Pedestrian detection using boosted features over many frames. In: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. pp. 1-4.Knoll, P., 2007. Hdr vision for driver assistance. In: Hoefflinger, B. (Ed.), High- Dynamic-Range (HDR) Vision. Vol. 26. Springer, pp. 123-136.Marsi, S., Impoco, G., Ukovich, A., Ramponi, G., 2007. Video enhancement and dynamic range control of hdr sequences for automotive applications. Advances in Signal Processing (EURASIP) 2007, 9.Mercimek, M., Gulez, K., Mumcu, T., 2005. Real object recognition using moment invariants. Sadhna - Acad. Proc. Eng. Sci. 30, 765-775.Miau, F., Papageorgiou, C.S., Itti, L., 2001. Neuromorphic algorithms for computer vision and attention. Proc.Intl Symp. Optical Science and Technology 01 (46), 12-23.Moeslund, T., Kruger, V., 2006. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 103, 90-126.Nayar, S., Branzoi, V., 2003. Adaptive dynamic range imaging: optical control of pixel exposures over space and time. In: Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on. pp. 1168-1175 vol.2.Park, S., Trivedi, M., 2007. Multi-person interaction and activity analysis: a synergistic track- and body-level analysis framework. Machine Vision and Applications 18, 151-166.Park, S., Trivedi, M., jul 2008. Understanding human interactions with track and body synergies (tbs) captured from multiple views. Computer Vision and Image Understanding 111 (1), 2-20.Phinyomark, A., Limsakul, C., Phukpattaranont, P., 2009. A novel feature extraction for robust emg pattern recognition. CoRR abs/0912.3973.Poppe, R., 2010. A survey on vision-based human action recognition. Image and Vision Computing 28 (6), 976-990.Qiao, Y., Wang, X., Xu, C., june 2011. Learning mahalanobis distance for dtw based online signature verification. In: Information and Automation (ICIA), 2011 IEEE International Conference on. pp. 333-338.Rabiner, L., feb 1989. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77 (2), 257-286.Romero, D., Frizera, A., Bastos, T., jan. 2012a. Movement analysis in learning by repetitive recall. an approach for automatic assistance in physiotherapy. In: Biosignals and Biorobotics Conference (BRC), 2012 ISSNIP. pp. 1-8.Romero, D., Vintimilla, B., Frizera, A., Bastos, T.F., jun 2012b. Rwe patterns extraction for on-line human action recognition through window-based analysis of invariant moments. In: Robocontrol (2012). Bauru - SP, pp. 20-27.Rosso, O., Martin, M., Plastino, A., 2003. Brain electrical activity analysis using wavelet-based informational tools (ii): Tsallis non-extensivity and complexity measures. Physica A: Statistical Mechanics and its Applications 320 (0), 497-511.Rosso, O.A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schurmann, M., Basar, E., 2001. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods 105 (1), 65-75.Salas-Lopez, G., Sandoval-Gonzalez, O., Herrera-Aguilar, I., Martà nez-Sibaja, A., Portillo-Rodriguez, O., Vilchis-Gonzalez, A., 2012. Design and development of a planar robot for upper extremities rehabilitation with visuovibrotactile feedback. Procedia Technology 3, 147-156.Sarvaiya, J.N., 2011. Automatic image registration using mexican hat wavelet, invariant moment, and radon transform. IJACSA - International Journal of Advanced Computer Science and Applications 01 (Special Issue), 75-84.Singh, S., Velastin, S., Ragheb, H., september 2010. Muhavi: A multicamera human action video dataset for the evaluation of action recognition methods. In: Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on. pp. 48-55.Soga, M., Kato, T., Ohta, M., Ninomiya, Y., april 2005. Pedestrian detection with stereo vision. In: Data Engineering Workshops. 21st International Conference on. Vol. 01. pp. 20-28.Viola, P., Jones, M., Snow, D., oct. 2003. Detecting pedestrians using patterns of motion and appearance. In: Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on. Vol. 2. pp. 734-741.Wang, L., Hu, W., T. Tan, 2003. Recent developments in human motion analysis. Pattern Recognition 36, 585-601.Yan, L., Casperson, D., Chen, L., june 2011. Survey: Dimension reduction by pattern decomposition. In: Modelling, Identification and Control (ICMIC), Proceedings of 2011 International Conference on. pp. 69-74

Robotic wheelchair controlled through a vision-based interface

In this work, a vision-based control interface for commanding a robotic wheelchair is presented. The interface estimates the orientation angles of the user's head and it translates these parameters in command of maneuvers for different devices. The performance of the proposed interface is evaluated both in static experiments as well as when it is applied in commanding the robotic wheelchair. The interface calculates the orientation angles and it translates the parameters as the reference inputs to the robotic wheelchair. Control architecture based on the dynamic model of the wheelchair is implemented in order to achieve safety navigation. Experimental results of the interface performance and the wheelchair navigation are presented.Fil: Perez, Elisa. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Nasisi, Oscar Herminio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Bastos, Teodiano Freire. Universidade Federal do Espírito Santo; BrasilFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Molecular dynamics of the COVID-19 pandemic in Espirito Santo (Brazil) and border States

This study represents the first overview of the epidemiological dynamics of SARS-CoV-2 in Espirito Santo (ES) State, Brazil, filling in knowledge on this topic, observing data collected in the State, and aiming at understanding the epidemiological dynamics of the virus in ES, as well as its possible routes of transmission and dissemination. . Our results highlight that, so far, nine lineages have been identified with ES State. The B.1.1.33 lineage was the first with the highest occurrence in ES, remaining predominant until September 2020. The second predominant lineage was Gamma, representing 45% of the samples. The Delta lineage appears on the State scene, proving to be the next dominant lineage. This research allowed us to understand how the lineages advanced and were distributed in the State, which is important for future work, also making it possible to guide sanitary control measures. Data analyses were made through the GISAID database for ES State showed that the pandemic in the State has been evolving dynamically with lineage replacements over the months since the first notification

Use of robotics as a learning aid for disabled children

Severe disabled children have little chance of environmental and social exploration and discovery, and due to this lack of interaction and independency, it may lead to an idea that they are unable to do anything by themselves. Trying to help these children on this situation, educational robotics can offer and aid, once it can give them a certain degree of independency in exploration of environment. The system developed in this work allows the child to transmit the commands to a robot. Sensors placed on the child’s body can obtain information from head movement or muscle signals to command the robot to carry out tasks. With the use of this system, the disabled children get a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities