Características hardware de las aplicaciones del AIOT en salud
| datacite.rights | http://purl.org/coar/access_right/c_16ec | eng |
| dc.contributor.advisor | Méndez Torrenegra, Fernando Miguel | |
| dc.contributor.author | Oquendo Ortega, Mario Andrés | |
| dc.contributor.author | Gómez Peña, Nestor Andrés | |
| dc.contributor.author | Miranda Robles, Daniel David | |
| dc.date.accessioned | 2023-12-04T22:21:46Z | |
| dc.date.available | 2023-12-04T22:21:46Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | El Internet de las cosas (IoT) y la inteligencia artificial (IA) han dado lugar a la inteligencia artificial de las cosas (AIoT), una tecnología que combina la interconexión de dispositivos cotidianos a través de internet, permitiendo nuevas oportunidades para mejorar la eficiencia, la productividad y la calidad de vida en diversos sectores. El presente artículo tiene como objetivo explorar las características de hardware que se utilizan para las aplicaciones en el sector de la salud, para ser más precisos aquellos hardware de sensado, de procesamiento y las tecnologías de comunicación utilizadas con mayor frecuencia en el sector AIoT. Se analizaron varias teorías relevantes relacionadas con el IoT y la IA, así como teorías específicas relacionadas con el hardware utilizado en el sector de la salud, se realizó una detallada revisión bibliográfica en bases de datos de investigación (IEEE Xplore) para identificar las características y los avances en el campo de la AIoT en el ámbito de la salud. La metodología empleada se basó en el análisis comparativo de diferentes dispositivos de IoT utilizados en el sector de la salud, además de las características de hardware de sensado, procesamiento y las tecnologías de comunicación más utilizadas en estas aplicaciones, considerando factores como la precisión, la eficiencia energética y la seguridad de los dispositivos, dándonos como resultados dispositivos como electrodos (ECG) y dispositivos de medición de temperatura como los más utilizados en hardware de censado, el uso de microcontroladores con diferentes características como los más utilizados en hardware de procesamiento y para la tecnología de comunicación, se evidencio el uso del BLE (4.0 y 5.0) como el más utilizado para estos dispositivos, cabe mencionar que el uso de Wi-Fi también fue empleado en algunos de estos. Se espera que este estudio contribuya a un mejor entendimiento de las características de hardware utilizadas en las aplicaciones de AIoT en el sector de la salud, aquellas que pueden facilitar la monitorización remota de pacientes, permitiendo a los profesionales de la salud acceder a datos en tiempo real y realizar un seguimiento más preciso del estado de salud de los pacientes, bridando así una eficiente atención y una mejor calidad de vida. | spa |
| dc.description.abstract | The Internet of Things (IoT) and Artificial Intelligence (AI) have given rise to the Artificial Intelligence of Things (AIoT), a technology that combines the interconnection of everyday devices through the internet, enabling new opportunities to improve efficiency, productivity, and quality of life in various sectors. This article aims to explore the hardware characteristics used for applications in the healthcare sector, specifically sensing hardware, processing hardware, and communication technologies most frequently used in this sector. Several relevant theories related to IoT and AI, as well as specific theories related to the hardware used in the healthcare sector, were analyzed. A detailed literature review was conducted in research databases (such as IEEE Xplore) to identify the characteristics and advancements in the field of AIoT in healthcare. The methodology employed was based on a comparative analysis of different IoT devices used in the healthcare sector, including the characteristics of sensing hardware, processing hardware, and the most used communication technologies. Factors such as accuracy, energy efficiency, and device security were considered. The results revealed that devices such as Electrocardiogram (ECG) electrodes and temperature measurement devices are the most commonly used sensing hardware in healthcare applications. Microcontrollers with various features were found to be the most utilized processing hardware. In terms of communication technology, Bluetooth Low Energy (BLE) (versions 4.0 and 5.0) emerged as the most widely used, while Wi-Fi was also employed in some cases. This study contributes to a better understanding of the hardware characteristics used in AIoT applications in the healthcare sector. These characteristics can facilitate remote patient monitoring, enabling healthcare professionals to access real-time data and achieve more accurate tracking of patients' health status. Ultimately, this enhances efficient healthcare delivery and improves the quality of life for patients. | eng |
| dc.format.mimetype | spa | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/13536 | |
| dc.language.iso | spa | spa |
| dc.publisher | Ediciones Universidad Simón Bolívar | spa |
| dc.publisher | Facultad de Ingenierías | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | |
| dc.rights.accessrights | info:eu-repo/semantics/restrictedAccess | eng |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | IoT | eng |
| dc.subject | AIoT | eng |
| dc.subject | IEEE | eng |
| dc.subject | Microcontroladores | spa |
| dc.subject | ECG | eng |
| dc.subject | BLE | eng |
| dc.subject | Microcontrollers | eng |
| dc.title | Características hardware de las aplicaciones del AIOT en salud | spa |
| dc.type.driver | info:eu-repo/semantics/bachelorThesis | eng |
| dc.type.spa | Trabajo de grado - pregrado | spa |
| dcterms.references | L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, Oct. 2010, doi: 10.1016/j.comnet.2010.05.010. | eng |
| dcterms.references | B. Dong, Q. Shi, Y. Yang, F. Wen, Z. Zhang, and C. Lee, “Technology evolution from self- powered sensors to AIoT enabled smart homes,” Nano Energy, vol. 79. Elsevier Ltd, Jan. 01, 2021. doi: 10.1016/j.nanoen.2020.105414. | eng |
| dcterms.references | A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys and Tutorials, vol. 17, no. 4, pp. 2347–2376, Oct. 2015, doi: 10.1109/COMST.2015.2444095. | eng |
| dcterms.references | J. Zhang and D. Tao, “Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things,” IEEE Internet of Things Journal, vol. 8, no. 10. Institute of Electrical and Electronics Engineers Inc., pp. 7789– 7817, May 15, 2021. doi: 10.1109/JIOT.2020.3039359. | eng |
| dcterms.references | C. González García, E. Núñez-Valdez, V. García-Díaz, C. Pelayo G-Bustelo, and J. M. Cueva-Lovelle, “A Review of Artificial Intelligence in the Internet of Things,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 4, p. 9, 2019, doi: 10.9781/ijimai.2018.03.004. | eng |
| dcterms.references | A. Ghosh, D. Chakraborty, and A. Law, “Artificial intelligence in Internet of things,” CAAI Transactions on Intelligence Technology, vol. 3, no. 4. Institution of Engineering and Technology, pp. 208–218, Dec. 01, 2018. doi: 10.1049/trit.2018.1008. | eng |
| dcterms.references | S. Baker and W. Xiang, “Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities,” IEEE Communications Surveys & Tutorials, pp. 1–1, Mar. 2023, doi: 10.1109/comst.2023.3256323. | eng |
| dcterms.references | A. Castillo-Atoche et al., “Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport,” Applied Sciences (Switzerland), vol. 12, no. 5, Mar. 2022, doi: 10.3390/app12052716. | eng |
| dcterms.references | IEEE Circuits and Systems Society, IEEE Engineering in Medicine and Biology Society, IEEE Solid-State Circuits Society, and Institute of Electrical and Electronics Engineers, BioCAS 2019 : IEEE Biomedical Circuits and Systems Conference : Nara, Japan, October 17-19, 2019 : 2019 conference proceedings. | eng |
| dcterms.references | C.-C. Wei, C.-W. Chen, and L.-C. Hung, “Establish a smart healthcare system with AIoT for Chinese Medicine,” Institute of Electrical and Electronics Engineers (IEEE), Jan. 2023, pp. 1–5. doi: 10.1109/icot56925.2022.10008112. | eng |
| dcterms.references | IEEE Circuits and Systems Society, China. Guo jia ke xue ji shu bu, Guo li zhong xing da xue, and Institute of Electrical and Electronics Engineers, Proceedings, 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems : AICAS 2019 : March 18-20, 2019, Hsinchu, Taiwan. | eng |
| dcterms.references | F. Quiñonez-Cuenca et al., “Evaluation of AIoT performance in Cloud and Edge computational models for mask detection,” Ingenius, vol. 2022, no. 27, pp. 32–47, Jan. 2022, doi: 10.17163/ings.n27.2022.04. | eng |
| dcterms.references | J. M. Díaz, “obra está bajo una licencia de Creative Commons Reconocimiento- NoComercial-SinObraDerivada 4.0 Internacional DOSSIER CUESTIONES BIOÉTICAS DE LA PANDEMIA COVID-19 Inteligencia artificial y Big Data como soluciones frente a la COVID-19 Artificial Intelligence and Big Data as solutions to COVID-19 Intel·ligència Artificial i Big Data per fer front a la COVID-19,” 2020, [Online]. Available: www.bioeticayderecho.ub.edu | eng |
| dcterms.references | L. Guillermo and J. Herrera, “Inteligencia artificial como potencia de herramienta en salud.” [Online]. Available: https://orcid.org/0000-0002-8331-0498 | spa |
| dcterms.references | Y. T. Ternera Muñoz, “Sistema de Control de Acceso basado en Hardware y Software Libre para la Detección de potenciales infectados de COVID-19,” Computer and Electronic Sciences: Theory and Applications, vol. 2, no. 2, pp. 9–20, Dec. 2021, doi: 10.17981/cesta.02.02.2021.02. | spa |
| dcterms.references | Institute of Electrical and Electronics Engineers Bangladesh Section, 1st International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) 10-12 January 2019, Dhaka, Bangladesh. | eng |
| dcterms.references | P. J. Chen, T. H. Hu, and M. S. Wang, “Raspberry Pi-Based Sleep Posture Recognition System Using AIoT Technique,” Healthcare (Switzerland), vol. 10, no. 3, Mar. 2022, doi: 10.3390/healthcare10030513. | eng |
| dcterms.references | T. Sangeetha, D. Kumutha, M. D. Bharathi, and R. Surendran, “Smart mattress integrated with pressure sensor and IoT functions for sleep apnea detection,” Measurement: Sensors, vol. 24, Dec. 2022, doi: 10.1016/j.measen.2022.100450. | eng |
| dcterms.references | R. Morello, F. Ruffa, I. Jablonski, L. Fabbiano, and C. De Capua, “An IoT based ECG system to diagnose cardiac pathologies for healthcare applications in smart cities,” Measurement (Lond), vol. 190, Feb. 2022, doi: 10.1016/j.measurement.2021.110685. | eng |
| dcterms.references | Makroum, Mohammed Amine & Adda, Mehdi & Bouzouane, Abdenour & Ibrahim, H.. (2022). Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors. 22. 1843. 10.3390/s22051843. | eng |
| dcterms.references | Abed, N. & Hussein, Ehab. (2021). Design and Implementation of Real Time Health Care Monitoring System Based on IoT. Journal of Physics: Conference Series. 1818. 012044. 10.1088/1742-6596/1818/1/012044. | eng |
| dcterms.references | Suzuki, Takuji & Ouchi, Kazushige & Kameyama, Ken-Ichi & Takahashi, Masaya. (2009). Development of a Sleep Monitoring System with Wearable Vital Sensor for Home Use.. 326-331. | eng |
| oaire.version | info:eu-repo/semantics/acceptedVersion | eng |
| sb.programa | Ingeniería de Sistemas | spa |
| sb.sede | Sede Barranquilla | spa |