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

[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.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.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_3S3141Chang, 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_7Yang, 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_13Khayyam, 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_2Liang, 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)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 2020Eskandari, 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)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_123Rincon, J.A., Martin, A., Costa, Â., Novais, P., Julián, V., Carrascosa, C.: EmIR: an emotional intelligent robot assistant. In: AfCAI (2018)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)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)Chollet, F., et al.: Keras (2015). https://github.com/fchollet/kerasHoward, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017

Competing charge, spin, and superconducting orders in underdoped YBa2Cu3Oy

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Pair production of neutralinos via gluon-gluon collisions

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