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IoT System for Monitoring and Analysing Physiological Variables in Athletes

Abstract

IoT has had a wide diffusion in monitoring variables of interest in applications such as health, agriculture, environment, and industry, among others. In the context of sport, although wearable devices can monitor physiological variables, they are limited by the fact that they are linked to proprietary applications, have limited storage and perform analyses based on descriptive statistics without including the application of data analytics models. In this paper, we present the construction of an IoT system for monitoring and analysing physiological variables in athletes based on the use of unsupervised learning models. This system is articulated in the IoT four-layer architecture (capture, storage, analysis and visualization). It has the advantage of benefiting from the data provided by commercial devices, storing them in a non-relational database and applying clustering algorithms to the historical data. The proposed system is intended to serve as a reference to be replicated in sports training contexts in order to take advantage of the data provided by commercial wearable devices for decision-making based on the use of machine learning models.

Keywords

IoT, IoT system, monitoring, athletes

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Author Biography

Jesús-Eduardo Consuegra-Fontalvo

Roles: Conceptualization, methodology, writing - review and editing.

Jair Calderón-Velaides

Roles: Conceptualization, methodology, writing - review and editing.

Gabriel-Elías Chanchí-Golondrino

Roles: Supervision, formal analysis, investigation, writing - review and editing.


References

  1. L. Chapin, S. Eldridge, K. Rose, La Internet de las cosas - Una breve reseña, 2015. https://www.internetsociety.org/wp-content/uploads/2017/09/report-InternetOfThings-20160817-es-1.pdf
  2. C. Galera, M. Opazo, “The internet of things (IoT) in sport management : principles, concepts and applications,” Revista Intercontinental de Gestão Desportiva, vol. 5, no. 2, pp. 128–142, 2015.
  3. M. Barrio, Internet de las Cosas. Reus, 2018.
  4. H. A. Peña, G. E. Chanchí, W. Y. Campo, “Sistema IoT para la monitorización de niveles de ruido en zonas aledañas al aeropuerto de Cartagena de Indias,” Revista Ibérica de Sistemas e Tecnologias de Informação, no. E42, pp. 257–272, 2020.
  5. L. Atzori, A. Iera, G. Morabito, “Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm,” Ad Hoc Networks, vol. 56, pp. 122–140, 2017. https://doi.org/10.1016/j.adhoc.2016.12.004 DOI: https://doi.org/10.1016/j.adhoc.2016.12.004
  6. G. Chanchí G., L. M. Marina Sierra, W. M. Yesid Campo, “Propuesta de una plataforma académica portable para la construcción de microservicios en entornos de IoT,” Revista Ibérica de Sistemas e Tecnologias de Informação, no. E27, pp. 1–13, 2019.
  7. M. L. Seuba, Internet de las Cosas. La transformación digital de la sociedad. Grupo Editorial RA-MA, 2019. https://www.ra-ma.es/libro/internet-de-las-cosas_93304/
  8. J. E. Gómez, F. R. Marcillo, F. L. Triana, V. T. Gallo, B. W. Oviedo, V. L. Hernández, “IoT for environmental variables in urban areas,” in Procedia Computer Science, Jan. 2017, vol. 109, pp. 67–74. https://doi.org/10.1016/j.procs.2017.05.296 DOI: https://doi.org/10.1016/j.procs.2017.05.296
  9. P. Kalia, M. A. Ansari, “IoT based air quality and particulate matter concentration monitoring system,” Materials Today Proceedings, vol. 32, pp. 468–475, Jan. 2020. https://doi.org/10.1016/j.matpr.2020.02.179 DOI: https://doi.org/10.1016/j.matpr.2020.02.179
  10. S. Rodríguez, T. Gualotuña, C. Grilo, “A System for the Monitoring and Predicting of Data in Precision Agriculture in a Rose Greenhouse Based on Wireless Sensor Networks,” Procedia Computer Science, vol. 121, pp. 306–313, Jan. 2017. https://doi.org/10.1016/j.procs.2017.11.042 DOI: https://doi.org/10.1016/j.procs.2017.11.042
  11. P. Sanmartín Mendoza, K. Ávila Hernández, C. Vilora Núñez, D. Jabba Molinares, “Internet de las cosas y la salud centrada en el hogar Internet of Things and Home-Centered Health,” Salud Uninorte, vol. 32, no. 2, pp. 337–351, 2016. DOI: https://doi.org/10.14482/sun.32.2.8954
  12. N. Martínez, “IoT de la salud es el Internet de las Cosas aplicado a cuestiones médicas,” Orange, 2019. https://blog.orange.es/innovacion/pastillas-inteligentes-audiometros-digitales-y-plataformas-medicas-asi-es-ya-el-iot-de-la-salud/
  13. L. Fava, D. Vilches, J. Díaz, M. Pagano, R. R. Dapozo, “Tecnología aplicada al deporte de alto rendimiento,” in XX Workshop de Investigadores en Ciencias de la Computación, 2018, pp. 864–868.
  14. P. P. Ray, “Internet of Things for Sports (IoTSport): An architectural framework for sports and recreational activity,” in International Conference on Electrical, Electronics, Signals, Communication and Optimization, 2015, pp. 1–4. https://doi.org/10.1109/EESCO.2015.7253963 DOI: https://doi.org/10.1109/EESCO.2015.7253963
  15. L. Solarte, M. Sánchez, G. Chanchí, D. Durán, J. L. Arciniegas, “Video on demand service based on the inference of emotions user,” Sistemas y Telemática, vol. 14, no. 38, pp. 31–47, 2016. https://doi.org/10.18046/syt.v14i38.2286 DOI: https://doi.org/10.18046/syt.v14i38.2286
  16. F. Wu, T. Wu, M. R. Yuce, “Design and implementation of a wearable sensor network system for iot-connected safety and health applications,” in IEEE 5th World Forum on Internet of Things, WF-IoT, Apr. 2019, pp. 87–90. https://doi.org/10.1109/WF-IoT.2019.8767280 DOI: https://doi.org/10.1109/WF-IoT.2019.8767280
  17. K. Braam, T. C. Huang, C. H. Chen, E. Montgomery, S. Vo, R. Beausoleil, “Wristband Vital: A wearable multi-sensor microsystem for real-time assistance via low-power Bluetooth link,” in IEEE World Forum on Internet of Things, WF-IoT, 2015, pp. 87–91. https://doi.org/10.1109/WF-IoT.2015.7389032 DOI: https://doi.org/10.1109/WF-IoT.2015.7389032
  18. Z. Zhou, H. Yu, H. Shi, “Human Activity Recognition Based on Improved Bayesian Convolution Network to Analyze Health Care Data Using Wearable IoT Device,” IEEE Access, vol. 8, pp. 86411–86418, 2020. https://doi.org/10.1109/ACCESS.2020.2992584 DOI: https://doi.org/10.1109/ACCESS.2020.2992584
  19. S. F. Shaikh, M. M. Hussain, “Marine IoT: Non-invasive wearable multisensory platform for oceanic environment monitoring,” in IEEE 5th World Forum on Internet of Things, WF-IoT, Apr. 2019, pp. 309–312. https://doi.org/10.1109/WF-IoT.2019.8767310 DOI: https://doi.org/10.1109/WF-IoT.2019.8767310
  20. E. A. . Quiroga Montoya, S. F. Jaramillo Colorado, W. Y. Campo Muñoz, G. E. Chanchí Golondrino, “Propuesta de una Arquitectura para Agricultura de Precisión Soportada en IoT,” Revista Iberérica de Sistemas e Tecnologias de Informação, no. 24, pp. 39–56, 2017. https://doi.org/10.17013/risti.24.39-56 DOI: https://doi.org/10.17013/risti.24.39-56
  21. C. Palao Cruz, “Desarrollo de un sistema IoT integrado con dispositivos de eHealth para la detección automática de la variabilidad cardiaca,” Grade Thesis, Universidad Politécnica de Valencia, 2017.
  22. R. Gruetzemacher, A. Gupta, G. B. Wilkerson, “Sports Injury Prevention Screen (SIPS): Design and Architecture of an Internet of Things (IoT) Based Analytics Health App.,” Conf-Irm, p. 18, 2016.
  23. M. Bonilla, E. Córdova, “Sistema autónomo de monitoreo de señales fisiológicas con gestión de emergencias para seguridad vial de ciclistas amateur,” Grade Thesis, Universidad Técnica de Ambato, 2019.
  24. J. De los Ríos, W. Gamba, D. Junco, C. Correax, “Dron-Fit: sistema de monitoreo, seguimiento y generación de alertas enfocado en la práctica deportiva, basado en información capturada con sensores presentes en el dispositivo Microsoft Band y registro de video a través de un drone a través de la plataforma OpenMTC,” Uniandes, 2017.
  25. H. Cristancho Chinome, J. E. Otalora Luna, M. Callejas Cuervo, “Sistema experto para determinar la frecuencia cardiaca máxima en deportistas con factores de riesgo,” Revista Ingeniería Biomédica, vol. 10, no. 19, pp. 23–31, 2016. https://doi.org/10.24050/19099762.n19.2016.1028.
  26. D. Delgado, D. Girón, G. Chanchí, K. Márceles, S. Dionizio, “Sistema para la Detección y Seguimiento de Afecciones Cardíacas Soportado en SBC,” Revista Ibérica de Sistemas e Tecnologias de Informação, vol. E17, pp. 717–728, 2019.
  27. R. Velásquez, M. Segura, “Arquitectura de un sistema de telemonitorización para hospitalización domiciliaria de adultos mayores apoyada en tecnologías de Internet de las cosas (IOT),” Grade Thesis, Universidad de Cartagena, 2017.
  28. G. Chanchí, M. Ospina-Alarcón, M. Monroy, “Arquitectura IoT para el desarrollo de sistemas de monitorización y análisis de variables fisiológicas en el área de asistencia médica,” Revista Investigación e Innovación en Ingeniería, vol. 8, no. 3, pp. 1–13, 2020. https://doi.org/10.17081/invinno.8.3. DOI: https://doi.org/10.17081/invinno.8.3.4699
  29. Y. Liu, J. Niu, L. Yang, L. Shu, “EBPlatform: An IoT-based system for NCD patients homecare in China,” in 2014 IEEE Global Communications Conference, GLOBECOM, Feb. 2014, pp. 2448–2453. https://doi.org/10.1109/GLOCOM.2014.7037175. DOI: https://doi.org/10.1109/GLOCOM.2014.7037175
  30. A. Vallejo Martínez, R. Martínez Unanue, Á. Rodrigo Yuste, “Arquitectura lambda aplicada a clustering de documentos en contextos Big Data,” Universidad Nacional de Eduación a Distancia, 2015.
  31. D. M. D. Agudelo, D. F. G. Timaná, G. E. C. Golondrino, K. M. Villalba, “Iot architecture for the identification of people in educational environments,” Revista Ibérica de Sistemas e Tecnologias de Informação, no. E17, pp. 841–853, 2019,
  32. Y. Yamato, H. Kumazaki, Y. Fukumoto, “Proposal of Lambda Architecture Adoption for Real Time Predictive Maintenance,” in Fourth International Symposium on Computing and Networking (CANDAR), Jan. 2016, pp. 713–715. https://doi.org/10.1109/candar.2016.0130. DOI: https://doi.org/10.1109/CANDAR.2016.0130
  33. C. Le Zhong, Z. Zhu, R. G. Huang, “Study on the IOT Architecture and Access Technology,” in 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Jul. 2017, vol. 2018-Septe, pp. 113–116. https://doi.org/10.1109/DCABES.2017.32. DOI: https://doi.org/10.1109/DCABES.2017.32
  34. M. Villari, A. Celesti, M. Fazio, A. Puliafito, “AllJoyn Lambda: An architecture for the management of smart environments in IoT,” in Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops, Feb. 2015, pp. 9–14. https://doi.org/10.1109/SMARTCOMP-W.2014.7046676. DOI: https://doi.org/10.1109/SMARTCOMP-W.2014.7046676
  35. J. García Manso, “Aplicación de la variabilidad de la frecuencia cardíaca al control del entrenamiento,” Revista Archivos Medicina del Deporte, vol. 30, no. 153, pp. 43–51, 2013.
  36. F. Shaffer, J. P. Ginsberg, “An Overview of Heart Rate Variability Metrics and Norms,” Frontiers in Public Health, vol. 5, pp. 1–17, 2017. https://doi.org/10.3389/fpubh.2017.00258. DOI: https://doi.org/10.3389/fpubh.2017.00258
  37. D. Young, “Self-measure of heart rate variability (HRV) and arrhythmia to monitor and to manage atrial arrhythmias: personal experience with high intensity interval exercise (HIIE) for the conversion to sinus rhythm,” Frontiers in Physiology, vol. 5, pp. 1–4, 2014. DOI: https://doi.org/10.3389/fphys.2014.00251
  38. B. De La Cruz Torres, C. L. López, J. N. Orellana, “Analysis of heart rate variability at rest and during aerobic exercise: A study in healthy people and cardiac patients,” British Journal of Sports Medicine, vol. 42, no. 9, pp. 715–720, 2008. https://doi.org/10.1136/bjsm.2007.043646. DOI: https://doi.org/10.1136/bjsm.2007.043646
  39. G. E. Chanchí, L. M. Sierra, W. Y. Campo, “Propuesta de una plataforma académica portable para la construcción de microservicios en entornos de IoT,” Revista Ibérica de Sistemas e Tecnologías de Informação, no. E27, pp. 1–13, 2020.

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