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Towards the edge intelligence: Robot assistant for the detection and classification of human emotions

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Towards the edge intelligence: Robot assistant for the detection and classification of human emotions

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dc.contributor.author Rincón Arango, Jaime Andrés es_ES
dc.contributor.author Julian Inglada, Vicente Javier es_ES
dc.contributor.author Carrascosa Casamayor, Carlos es_ES
dc.date.accessioned 2021-12-27T08:37:18Z
dc.date.available 2021-12-27T08:37:18Z
dc.date.issued 2020-10-09 es_ES
dc.identifier.isbn 978-3-030-51999-5 es_ES
dc.identifier.uri http://hdl.handle.net/10251/178906
dc.description.abstract [EN] Deep learning is being introduced more and more in our society. Nowadays, there are very few applications that do not use deep learning as a classification tool. One of the main application areas is focused on improving people¿s life quality, allowing to create personal assistants with canned benefits. More recently, with the proliferation of mobile computing and the emergence of the Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet. This allows the generation of millions of bytes of information about sensors, images, sounds, etc. Driven by this trend, there is an urgent need to push the IoT frontiers to the edge of the network, in order to decrease this massive sending of information to large exchanges for analysis. As a result of this trend, a new discipline has emerged: edge intelligence or edge AI, a widely recognised and promising solution that attracts with special interest to the community of researchers in artificial intelligence. We adapted edge AI to classify human emotions. Results show how edge AI-based emotion classification can greatly benefit in the field of cognitive assistants for the elderly or people living alone. es_ES
dc.description.sponsorship This work was partly supported by the Generalitat Valenciana (PROMETEO/2018/002) and by the Spanish Government (RTI2018-095390-B-C31). Universitat Politecnica de Valencia Research Grant PAID-10-19. es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection es_ES
dc.relation.ispartofseries Communications in Computer and Information Science;1233 es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject EDGE AI es_ES
dc.subject Assistant robot es_ES
dc.subject Emotions es_ES
dc.subject Elderly es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Towards the edge intelligence: Robot assistant for the detection and classification of human emotions es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-51999-5_3 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095390-B-C31/ES/HACIA UNA MOVILIDAD INTELIGENTE Y SOSTENIBLE SOPORTADA POR SISTEMAS MULTI-AGENTES Y EDGE COMPUTING/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-10-19//Mejora de prestaciones del páncreas artificial ante ingestas y ejercicio mediante observadores de perturbaciones y técnicas de compensación de retardos/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F002//TECNOLOGIES PER ORGANITZACIONS HUMANES EMOCIONALS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Rincón Arango, JA.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2020). Towards the edge intelligence: Robot assistant for the detection and classification of human emotions. Springer. 31-41. https://doi.org/10.1007/978-3-030-51999-5_3 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 18th International Conference on Practical Applications of Agents and Multiagent Systems (PAAMS 2020). Workshops es_ES
dc.relation.conferencedate Octubre 07-09,2020 es_ES
dc.relation.conferenceplace L'Aquila, Italy es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-51999-5_3 es_ES
dc.description.upvformatpinicio 31 es_ES
dc.description.upvformatpfin 41 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\423502 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Chang, A.: The role of artificial intelligence in digital health. In: Wulfovich, S., Meyers, A. (eds.) Digital Health Entrepreneurship. HI, pp. 71–81. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12719-0_7 es_ES
dc.description.references Yang, L., Henthorne, T.L., George, B.: Artificial intelligence and robotics technology in the hospitality industry: current applications and future trends. In: George, B., Paul, J. (eds.) Digital Transformation in Business and Society, pp. 211–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-08277-2_13 es_ES
dc.description.references Khayyam, H., Javadi, B., Jalili, M., Jazar, R.N.: Artificial intelligence and Internet of Things for autonomous vehicles. In: Jazar, R.N., Dai, L. (eds.) Nonlinear Approaches in Engineering Applications, pp. 39–68. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-18963-1_2 es_ES
dc.description.references Liang, F., Yu, W., Liu, X., Griffith, D., Golmie, N.: Towards edge-based deep learning in industrial Internet of Things. IEEE Internet of Things J. 7, 4329–4341 (2020) es_ES
dc.description.references Nagaraju, P.B., Oliner, A.J., Gilmore, B.M., Dean, E.A., Wang, J.: Data analytics in edge devices. US Patent App. 16/573,745, 9 January 2020 es_ES
dc.description.references Eskandari, M., Janjua, Z.H., Vecchio, M., Antonelli, F.: Passban IDS: an intelligent anomaly based intrusion detection system for IoT edge devices. IEEE Internet of Things J. (2020) es_ES
dc.description.references Harish, A., Jhawar, S., Anisha, B.S., Ramakanth Kumar, P.: Implementing machine learning on edge devices with limited working memory. In: Ranganathan, G., Chen, J., Rocha, Á. (eds.) Inventive Communication and Computational Technologies. LNNS, vol. 89, pp. 1255–1261. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0146-3_123 es_ES
dc.description.references Rincon, J.A., Martin, A., Costa, Â., Novais, P., Julián, V., Carrascosa, C.: EmIR: an emotional intelligent robot assistant. In: AfCAI (2018) es_ES
dc.description.references Ke, R., Zhuang, Y., Pu, Z., Wang, Y.: A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. arXiv preprint arXiv:2001.00269 (2020) es_ES
dc.description.references Mazzia, V., Khaliq, A., Salvetti, F., Chiaberge, M.: Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application. IEEE Access 8, 9102–9114 (2020) es_ES
dc.description.references Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras es_ES
dc.description.references Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017) es_ES


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