Video on demand service based on inference emotions user

Luis Alejandro Solarte Moncayo, Mauricio Sánchez Barragán, Gabriel Elías Chanchí Golondrino, Diego Fabián Durán Dorado, José Luis Arciniegas Herrera


Video traffic on networks increases exponentially, and thus the amount of time that should be used browsing content catalogs. Therefore, systems are needed video on demand [VoD] taking into account the emotions as a parameter for fast access to content. This paper presents the design and implementation of a VoD service based on emotions, whose main components are: the musical content catalog forming and hardware-software system that allows you to set the level of mental stress and inference of emotions of the consumer, while it interacts with the system. The final product was tested for efficiency and stress, with satisfactory results: the time spent by the web server with 200 sequential connections, ranged from 0.050 to 0.675 seconds and between 0.030 and 0.675 seconds when they are simultaneous. It also managed to respond adequately to 20,000 sequential connections, with response times of less than 1 to 36 seconds, and withstand, without collapsing, 18,000 concurrent connections, with response times between 7 and 62 seconds. The project provides an open source service that raises the groundwork for future projects.


Arousal; VoD; hardware-software system; valence; wearable.

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Altgeld, J. & John, D. (2006). The IPTV/VoD Challenge: Upcoming business models. In: Achieving the triple play: Technologies and business models for success (pp. 3-15). Chicago, IL: IEC.

Bayevsky, R., Ivanov, G., Chireykin, L., Gavrilushkin, A., Dovgalevsky, P., Kukushkin, U., & Fleishmann, A. (2002). HRV analysis under the usage of different electrocardiography systems (Methodical recommendations). Moscow, Russia: Committee of New Medical Techniques of Ministry of Health of Russia. Retrieved from:

Buyya, R. & Dastjerdi, A. [Eds]. (2016). Internet of Things: Principles and paradigms. Cambridge, MA: Morgan Kaufmann.

Evans, D. (2011). The internet of things: How the next evolution of the Internet is changing everything [white paper]. Retrieved from:

Cisco (2016, june 1). Cisco VNI, forecast and methodology, 2015-2020 [white paper]. Retrieved from:

González, G., López, B., & De la Rosa, J. (2004). Managing emotions in smart user models for recommender systems. ICEIS (5), 187-194.

Hall, J. & Guyton, A. (2011). Tratado de fisiología médica. Madrid, España: Elsevier.

Heilman, K. (1997). The neurobiology of emotional experience. Journal of Neuropsychiatry, 9(3), 439-448.

Jones, R., Fay, M., & Popper, A [Eds.] (2010). Music Perception. New York, NY: Springer.

Choi, J. & Gutierrez, R. (2009). Using heart rate monitors to detect mental stress. In: Proceeding 09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (pp. 219-223). New York, NY: ACM.

Meyers, O. (2007). A Mood-Based music classification and exploration system [tesis]. Massachusetts Institute of Technology: Cambridge, MA.

Mohana, S. & Ravish, H. (2015). Remote monitoring of heart rate and music to tune the heart rate. In: Communication Technologies (GCCT). IEEE.

Mohana, S. R., & Aradhya, H. R. (2015, April). Remote monitoring of heart rate and music to tune the heart rate. In 2015 Global Conference on Communication Technologies (GCCT), (pp. 678-681). IEEE.

Moreno, M., Segrera, S., López, V., Muñoz, M., & Sánchez, L. (2016). Web mining based framework for solving usual problems in recommender systems: A case study for movies' recommendation. Neurocomputing, 176(2), 72-80.

Mukhopadhyay, S. (Ed.). (2015). Wearable electronics sensors. Palmerston North, New Zealand: Springer.

Patil, K., Singh, M., Singh, G., Anjali., & Sharma, N. (2015). Mental stress evaluation using heart rate variability analysis: A review. International Journal of Public Mental Health and Neurosciences, 2(1), 10-16.

Posner, J., Russell, J., & Peterson, B. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and psychopathology, 17(03), 715-734.

Pripuzic, K., Zarko, I., Podobnik, V., Lovrek, I., Cavka, M., Petkovic, I., Stulic, P., & Gojceta, M. (2013). Building an IPTV VoD recommender system: An experience report. In: 12th International Conference on Telecommunications (ConTEL), 2013 (pp.155-162). IEEE.

Robayo, F., Neira, J., & Vásquez, M. (2015). Android mobile application for monitoring and recording human nutritional status implemented in a free hardware platform. Sistemas & Telemática, 13(32), 75-88. doi:10.18046/syt.v13i32.2029

Sharma, T. & Kapoor, B. (2014). Intelligent data analysis algorithms on biofeedback signals for estimating emotions. In: 2014 International Conference on Optimization, Reliabilty, and Information Technology (ICROIT), (pp. 335-340). IEEE.

Yang, Y. & Chen, H. (2011). Music emotion recognition . Boca Ratón , FL: CRC .



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