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Validación de un sistema IoT con aprendizaje federado y TinyML para el monitoreo postural en un entorno de cuidado simulado

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dc.contributor.advisorSarmiento Rojas, Jefferson Steven
dc.contributor.advisorAya Parra, Pedro Antonio
dc.creatorTorres Lara, Angela María
dc.creator.degreeMagíster en Ingeniería Biomédica
dc.creator.degreeLevelMaestría
dc.date.accessioned2026-02-05T21:27:03Z
dc.date.available2026-02-05T21:27:03Z
dc.date.created2025-11-14
dc.date.embargoEndinfo:eu-repo/date/embargoEnd/2028-02-06
dc.descriptionEste trabajo presenta la validación de un sistema de monitoreo postural basado en Internet de las Cosas (IoT), Aprendizaje Federado (Federated Learning, FL) y Tiny Machine Learning (TinyML), orientado a entornos asistenciales de salud en condiciones simuladas. El sistema fue diseñado con el objetivo de apoyar la vigilancia continua de posturas corporales en escenarios de cuidado, priorizando la privacidad de los datos, la eficiencia computacional y la operación en dispositivos de bajo consumo energético.
dc.description.abstractThis paper presents the validation of a posture monitoring system based on the Internet of Things (IoT), Federated Learning (FL), and Tiny Machine Learning (TinyML), aimed at healthcare environments in simulated conditions. The system was designed to support continuous monitoring of body posture in care settings, prioritizing data privacy, computational efficiency, and operation on low-power devices.
dc.format.extent36 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_47466
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/47466
dc.language.isospa
dc.publisherUniversidad del Rosario
dc.publisherEscuela Colombiana de Ingeniería Julio Garavito
dc.publisher.departmentEscuela de Medicina y Ciencias de la Saludspa
dc.publisher.programMaestría en Ingeniería Biomédicaspa
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dc.source.bibliographicCitationLi, Yangchen; Cui, Ying; Lau, Vincent (2023) GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems. No. arXiv:2306.07497; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2306.07497. Disponible en: 10.48550/arXiv.2306.07497.
dc.source.bibliographicCitationWoisetschläger, Herbert; Erben, Alexander; Mayer, Ruben; Wang, Shiqiang; Jacobsen, Hans-Arno (2024) FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems. pp. 88 - 102; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2306.05172. Disponible en: 10.1145/3652892.3700751.
dc.source.bibliographicCitationWong, Kok-Seng; Nguyen-Duc, Manh; Le-Huy, Khiem; Ho-Tuan, Long; Do-Danh, Cuong; Le-Phuoc, Danh (2023) An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity. No. arXiv:2305.19831; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2305.19831. Disponible en: 10.48550/arXiv.2305.19831.
dc.source.bibliographicCitationHasan, Tasnimul; Hossain, Abrar; Ansari, Mufakir Qamar; Syed, Talha Hussain (2025) Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning. No. arXiv:2501.15266; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2501.15266. Disponible en: 10.48550/arXiv.2501.15266.
dc.source.bibliographicCitationZeng, Liekang; Ye, Shengyuan; Chen, Xu; Zhang, Xiaoxi; Ren, Ju; Tang, Jian; Yang, Yang; Xuemin; Shen (2025) Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence. No. arXiv:2407.15320; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2407.15320. Disponible en: 10.48550/arXiv.2407.15320.
dc.source.bibliographicCitationEjeofobiri, Chigozie K; Victor-Igun, Olayinka Olubola; Okoye, Clifford (2024) AI-Driven Secure Intrusion Detection for Internet of Things (IOT) Networks. Vol. 31; No. 4; pp. 40 - 55; 2395-4213; Consultado en: 2025-09-30. Disponible en: https://ikprress.org/index.php/AJOMCOR/article/view/8971. Disponible en: 10.56557/ajomcor/2024/v31i48971.
dc.source.bibliographicCitationAbubakar, Muhammad; Sattar, Abdul; Manzoor, Hamid; Farooq, Khola; Yousif, Muhammad; IIOT: An Infusion of Embedded Systems, TinyML, and Federated Learning in Industrial IoT. Vol. 08; No. 2;
dc.source.bibliographicCitationZha, Chao; Pan, Haolin; Bai, Bing; Wu, Jiangxing; Zhang, Ruyun (2025) FlowXpert: Context-Aware Flow Embedding for Enhanced Traffic Detection in IoT Network. No. arXiv:2509.20861; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2509.20861. Disponible en: 10.48550/arXiv.2509.20861.
dc.source.bibliographicCitationSucipto, Willy; Zhou, Jianlong; Kwon, Ray Seung Min; Chen, Fang (2025) A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management. No. arXiv:2509.08822; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2509.08822. Disponible en: 10.48550/arXiv.2509.08822.
dc.source.bibliographicCitationKihara, Kosuke; Mori, Junki; Miyagawa, Taiki; Ebihara, Akinori F. (2025) Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models. No. arXiv:2509.08372; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2509.08372. Disponible en: 10.48550/arXiv.2509.08372.
dc.source.bibliographicCitationXu, Kunran; Zhang, Huawei; Li, Yishi; Zhang, Yuhao; Lai, Rui; Liu, Yi (2023) An Ultra-Low Power TinyML System for Real-Time Visual Processing at Edge. Vol. 70; No. 7; pp. 2640 - 2644; 1558-3791; Consultado en: 2025-09-30. Disponible en: https://ieeexplore.ieee.org/document/10024807. Disponible en: 10.1109/TCSII.2023.3239044.
dc.source.bibliographicCitationSánchez-Fernández, Luis Pastor; Sánchez-Pérez, Luis Alejandro; Carbajal-Hernández, José Juan; Hernández-Guerrero, Mario Alberto; Pérez-Echazabal, Lucrecia (2023) Buildings’ Biaxial Tilt Assessment Using Inertial Wireless Sensors and a Parallel Training Model. Vol. 23; No. 11; pp. 5352 1424-8220; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/1424-8220/23/11/5352. Disponible en: 10.3390/s23115352.
dc.source.bibliographicCitationKiarashi, Yashar; Hedge, Chaitra; Madala, Venkata Siva Krishna; Nakum, ArjunSinh; Singh, Ratan; Tweedy, Robert; Clifford, Gari D.; Kwon, Hyeokhyen (2023) Indoor Localization using Bluetooth and Inertial Motion Sensors in Distributed Edge and Cloud Computing Environment. No. arXiv:2305.19342; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2305.19342. Disponible en: 10.48550/arXiv.2305.19342.
dc.source.bibliographicCitationAbouelnaga, Mohamed; Vitay, Julien; Farahani, Aida (2023) Multivariate Time Series Classification: A Deep Learning Approach. No. arXiv:2307.02253; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2307.02253. Disponible en: 10.48550/arXiv.2307.02253.
dc.source.bibliographicCitationHao, Zhihao; Wang, Guancheng; Tian, Chunwei; Zhang, Bob (2023) A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems. No. arXiv:2306.16023; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2306.16023. Disponible en: 10.48550/arXiv.2306.16023.
dc.source.bibliographicCitationHasson, Hilaf; Maddix, Danielle C.; Wang, Yuyang; Gupta, Gaurav; Park, Youngsuk (2023) Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. No. arXiv:2305.15786; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2305.15786. Disponible en: 10.48550/arXiv.2305.15786.
dc.source.bibliographicCitationNour, Boubakr; Cherkaoui, Soumaya (2022) Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks. No. arXiv:2203.04376; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2203.04376. Disponible en: 10.48550/arXiv.2203.04376.
dc.source.bibliographicCitationWu, Jiajun; Drew, Steve; Zhou, Jiayu (2023) FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks. No. arXiv:2302.07305; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2302.07305. Disponible en: 10.48550/arXiv.2302.07305.
dc.source.bibliographicCitationShen, Cong; Yang, Jing; Xu, Jie (2022) On Federated Learning with Energy Harvesting Clients. No. arXiv:2202.06105; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2202.06105. Disponible en: 10.48550/arXiv.2202.06105.
dc.source.bibliographicCitationZhang, Xiaofan; Chen, Yao; Hao, Cong; Huang, Sitao; Li, Yuhong; Chen, Deming (2022) Compilation and Optimizations for Efficient Machine Learning on Embedded Systems. No. arXiv:2206.03326; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2206.03326. Disponible en: 10.48550/arXiv.2206.03326.
dc.source.bibliographicCitationScherer, Moritz; Mauro, Alfio Di; Fischer, Tim; Rutishauser, Georg; Benini, Luca (2022) TCN-CUTIE: A 1036 TOp/s/W, 2.72 uJ/Inference, 12.2 mW All-Digital Ternary Accelerator in 22 nm FDX Technology. No. arXiv:2212.00688; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2212.00688. Disponible en: 10.48550/arXiv.2212.00688.
dc.source.bibliographicCitationSabot, Andrew; Natesh, Vikas; Kung, H. T.; Ting, Wei-Te (2023) MEMA Runtime Framework: Minimizing External Memory Accesses for TinyML on Microcontrollers. No. arXiv:2304.05544; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2304.05544. Disponible en: 10.48550/arXiv.2304.05544.
dc.source.bibliographicCitationLaidig, Daniel; Seel, Thomas (2023) VQF: Highly Accurate IMU Orientation Estimation with Bias Estimation and Magnetic Disturbance Rejection. Vol. 91; pp. 187 - 204; 15662535; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2203.17024. Disponible en: 10.1016/j.inffus.2022.10.014.
dc.source.bibliographicCitationBhandar, Shubham I; Biradar, Aishwarya; Kshirsagar, Aditi; Biradar, Preeti; Kekan, Jayshri (2022) Human Activity Recognition With Smartphone. Vol. 10; No. 5; pp. 3965 - 3970; 23219653; Consultado en: 2025-09-30. Disponible en: https://www.ijraset.com/best-journal/human-activity-recognition-with-smartphone. Disponible en: 10.22214/ijraset.2022.43295.
dc.source.bibliographicCitationNair, Anju M (2019) Human Activity Recognition Using Accelerometer Data With Multiclass SVM. Vol. 4; No. 3;
dc.source.bibliographicCitationZhang, Tuo; He, Chaoyang; Ma, Tianhao; Gao, Lei; Ma, Mark; Avestimehr, Salman (2021) Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection. pp. 413 - 419; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2106.07976. Disponible en: 10.1145/3485730.3493444.
dc.source.bibliographicCitationLlisterri Giménez, Nil; Monfort Grau, Marc; Pueyo Centelles, Roger; Freitag, Felix (2022) On-Device Training of Machine Learning Models on Microcontrollers with Federated Learning. Vol. 11; No. 4; pp. 573 2079-9292; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/2079-9292/11/4/573. Disponible en: 10.3390/electronics11040573.
dc.source.bibliographicCitationFerraz Junior, Norisvaldo (2022) FedSensor: framework de aprendizagem federada voltado para a eficiência energética e segurança de dispositivos IoT ultra-restritos. : Universidade de São Paulo; Consultado en: 2025-09-30. Disponible en: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-04052023-092320/. Disponible en: 10.11606/T.3.2022.tde-04052023-092320.
dc.source.bibliographicCitationAouedi, Ons; Piamrat, Kandaraj; Muller, Guillaume; Singh, Kamal (2022) Intrusion detection for Softwarized Networks with Semi-supervised Federated Learning. pp. 5244 - 5249; IEEE; Consultado en: 2025-09-30. Disponible en: https://hal.science/hal-03544099. Disponible en: 10.1109/ICC45855.2022.9839042.
dc.source.bibliographicCitationVasiljevic, Pavle; Matic, Milica; Popovic, Miroslav (2025) Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems. pp. 30 - 35; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2506.05138. Disponible en: 10.1109/ZINC65316.2025.11103552.
dc.source.bibliographicCitationRen, Haoyu; Anicic, Darko; Runkler, Thomas A. (2023) TinyReptile: TinyML with Federated Meta-Learning. No. arXiv:2304.05201; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2304.05201. Disponible en: 10.48550/arXiv.2304.05201.
dc.source.bibliographicCitationda Silva, Claudio Nascimento; Prazeres, Cássio V. S. (2025) Tiny Federated Learning for Constrained Sensors: A Systematic Literature Review. Vol. 2; pp. 17 - 31; 2995-7478; Consultado en: 2025-09-30. Disponible en: https://ieeexplore.ieee.org/document/10916688/. Disponible en: 10.1109/SR.2025.3548547.
dc.source.bibliographicCitationJaved, Farhana; Zeydan, Engin; Mangues-Bafalluy, Josep; Dev, Kapal; Blanco, Luis (2025) Blockchain for Federated Learning in the Internet of Things: Trustworthy Adaptation, Standards, and the Road Ahead. No. arXiv:2503.23823; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2503.23823. Disponible en: 10.48550/arXiv.2503.23823.
dc.source.bibliographicCitationNguyen, Dinh C.; Ding, Ming; Pathirana, Pubudu N.; Seneviratne, Aruna; Li, Jun; Poor, H. Vincent (2021) Federated Learning for Internet of Things: A Comprehensive Survey. Vol. 23; No. 3; pp. 1622 - 1658; 1553-877X, 2373-745X; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2104.07914. Disponible en: 10.1109/COMST.2021.3075439.
dc.source.bibliographicCitationJahani-Nezhad, Tayyebeh; Maddah-Ali, Mohammad Ali; Li, Songze; Caire, Giuseppe (2022) SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees. pp. 103 - 108; Consultado en: 2025-09-30. Disponible en: https://ieeexplore.ieee.org/document/9834750. Disponible en: 10.1109/ISIT50566.2022.9834750.
dc.source.bibliographicCitationJahani-Nezhad, Tayyebeh; Maddah-Ali, Mohammad Ali; Li, Songze; Caire, Giuseppe (2022) SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning. No. arXiv:2203.13060; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2203.13060. Disponible en: 10.48550/arXiv.2203.13060.
dc.source.bibliographicCitationBehnia, Rouzbeh; Riasi, Arman; Ebrahimi, Reza; Chow, Sherman S. M.; Padmanabhan, Balaji; Hoang, Thang (2024) Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning. No. arXiv:2304.03841; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2304.03841. Disponible en: 10.48550/arXiv.2304.03841.
dc.source.bibliographicCitationEltarasTamer; SabryFarida; LabdaWadha; AlzoubiKhawla; AHMEDELTARASQutaibah (2023) Efficient Verifiable Protocol for Privacy-Preserving Aggregation in Federated Learning. Consultado en: 2025-09-30. Disponible en: https://dl.acm.org/doi/10.1109/TIFS.2023.3273914. Disponible en: 10.1109/TIFS.2023.3273914.
dc.source.bibliographicCitationSabater, César; Bellet, Aurélien; Ramon, Jan (2022) An accurate, scalable and verifiable protocol for federated differentially private averaging. Vol. 111; No. 11; pp. 4249 - 4293; 1573-0565; Consultado en: 2025-09-30. Disponible en: https://doi.org/10.1007/s10994-022-06267-9. Disponible en: 10.1007/s10994-022-06267-9.
dc.source.bibliographicCitationJeon, Beomyeol; Ferdous, S. M.; Rahman, Muntasir Raihan; Walid, Anwar (2020) Privacy-preserving Decentralized Aggregation for Federated Learning. No. arXiv:2012.07183; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2012.07183. Disponible en: 10.48550/arXiv.2012.07183.
dc.source.bibliographicCitationPrasad, Karthik; Ghosh, Sayan; Cormode, Graham; Mironov, Ilya; Yousefpour, Ashkan; Stock, Pierre (2022) Reconciling Security and Communication Efficiency in Federated Learning. No. arXiv:2207.12779; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2207.12779. Disponible en: 10.48550/arXiv.2207.12779.
dc.source.bibliographicCitationAl-Saedi, Ahmed A.; Boeva, Veselka; Casalicchio, Emiliano (2022) FedCO: Communication-Efficient Federated Learning via Clustering Optimization. Vol. 14; No. 12; pp. 377 1999-5903; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/1999-5903/14/12/377. Disponible en: 10.3390/fi14120377.
dc.source.bibliographicCitationAlajlan, Norah N.; Ibrahim, Dina M. (2022) TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications. Vol. 13; No. 6; pp. 851 2072-666X; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/2072-666X/13/6/851. Disponible en: 10.3390/mi13060851.
dc.source.bibliographicCitationRajapakse, Visal; Karunanayake, Ishan; Ahmed, Nadeem (2023) Intelligence at the Extreme Edge: A Survey on Reformable TinyML. Vol. 55; No. 13; pp. 1 - 30; 0360-0300, 1557-7341; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2204.00827. Disponible en: 10.1145/3583683.
dc.source.bibliographicCitationYelchuri, Harsha; R, Rashmi (2022) A review of TinyML. No. arXiv:2211.04448; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2211.04448. Disponible en: 10.48550/arXiv.2211.04448.
dc.source.bibliographicCitationRen, Haoyu; Anicic, Darko; Runkler, Thomas (2022) How to Manage Tiny Machine Learning at Scale: An Industrial Perspective. No. arXiv:2202.09113; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2202.09113. Disponible en: 10.48550/arXiv.2202.09113.
dc.source.bibliographicCitationZaidi, Syed Ali Raza; Hayajneh, Ali M.; Hafeez, Maryam; Ahmed, Q. Z. (2022) Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML). Vol. 10; pp. 100867 - 100877; 2169-3536; Consultado en: 2025-09-30. Disponible en: https://ieeexplore.ieee.org/document/9893787. Disponible en: 10.1109/ACCESS.2022.3207200.
dc.source.bibliographicCitationSpringer, Tom; Eiroa-Lledo, Elia; Stevens, Elizabeth; Linstead, Erik (2021) On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures. Vol. 10; No. 6; pp. 689 2079-9292; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/2079-9292/10/6/689. Disponible en: 10.3390/electronics10060689.
dc.source.bibliographicCitationBaharaniMohammadreza; TabkhiHamed (2022) ATCN: Resource-efficient Processing of Time Series on Edge. Consultado en: 2025-09-30. Disponible en: https://dl.acm.org/doi/10.1145/3524070. Disponible en: 10.1145/3524070.
dc.source.bibliographicCitation Power Efficient Machine Learning Models Deployment on Edge IoT Devices. Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/1424-8220/23/3/1595.
dc.source.bibliographicCitationZhang, Jian; Soangra, Rahul; Lockhart, Thurmon E; Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines.
dc.source.bibliographicCitationSalem, Ziad; Weiss, Andreas Peter (2023) Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment. Vol. 23; No. 1; pp. 132 1424-8220; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/1424-8220/23/1/132. Disponible en: 10.3390/s23010132.
dc.source.bibliographicCitationLiang, Wenqi; Wang, Fanjie; Fan, Ao; Zhao, Wenrui; Yao, Wei; Yang, Pengfei (2023) Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation. Vol. 23; No. 9; pp. 4229 1424-8220; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/1424-8220/23/9/4229. Disponible en: 10.3390/s23094229.
dc.source.bibliographicCitationThottempudi, Pardhu; Acharya, Biswaranjan; Moreira, Fernando (2024) High-Performance Real-Time Human Activity Recognition Using Machine Learning. Vol. 12; No. 22; pp. 3622 2227-7390; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/2227-7390/12/22/3622. Disponible en: 10.3390/math12223622.
dc.source.bibliographicCitationLobo, Rodolfo Anibal; Valle, Marcos Eduardo (2020) Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories. No. arXiv:2009.08578; arXiv; Consultado en: 2025-09-30. Disponible en: http://arxiv.org/abs/2009.08578. Disponible en: 10.48550/arXiv.2009.08578.
dc.source.bibliographicCitationOkuboyejo, Damilola A.; Olugbara, Oludayo O. (2022) Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning. Vol. 15; No. 12; pp. 443 1999-4893; Consultado en: 2025-09-30. Disponible en: https://www.mdpi.com/1999-4893/15/12/443. Disponible en: 10.3390/a15120443.
dc.source.bibliographicCitationLongoEdoardo; E.C, RedondiAlessandro (2023) Design and implementation of an advanced MQTT broker for distributed pub/sub scenarios. Consultado en: 2025-09-30. Disponible en: https://dl.acm.org/doi/10.1016/j.comnet.2023.109601. Disponible en: 10.1016/j.comnet.2023.109601.
dc.source.bibliographicCitationSpohn, Marco Aurelio (2022) On MQTT Scalability in the Internet of Things: Issues, Solutions, and Future Directions. pp. 4 - 4; 2972-3280; Consultado en: 2025-09-30. Disponible en: https://ojs.wiserpub.com/index.php/JEEE/article/view/1687. Disponible en: 10.37256/jeee.1120221687.
dc.source.bibliographicCitationSahmi, Imane; Abdellaoui, Abderrahim; Mazri, Tomader; Hmina, Nabil (2021) MQTT-PRESENT: Approach to secure internet of things applications using MQTT protocol. Vol. 11; No. 5; pp. 4577 2722-2578, 2088-8708; Consultado en: 2025-09-30. Disponible en: http://ijece.iaescore.com/index.php/IJECE/article/view/25403. Disponible en: 10.11591/ijece.v11i5.pp4577-4586.
dc.source.bibliographicCitationVyas, Abhishek; Lin, Po-Ching; Hwang, Ren-Hung; Tripathi, Meenakshi (2024) Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Survey. Vol. 12; pp. 127018 - 127050; 2169-3536; Consultado en: 2025-09-30. Disponible en: https://ieeexplore.ieee.org/document/10664537. Disponible en: 10.1109/ACCESS.2024.3454211.
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectMonitoreo postural
dc.subjectEntornos asistenciales de salud
dc.subjectInternet de las Cosas (IoT)
dc.subjectAprendizaje federado
dc.subjectTiny Machine Learning
dc.subjectSensores inerciales
dc.subjectSistemas embebidos
dc.subject.keywordPostural monitoring
dc.subject.keywordHealthcare environments
dc.subject.keywordInternet of Things (IoT)
dc.subject.keywordFederated learning
dc.subject.keywordTiny Machine Learning (TinyML)
dc.subject.keywordInertial sensors
dc.subject.keywordEmbedded systems
dc.titleValidación de un sistema IoT con aprendizaje federado y TinyML para el monitoreo postural en un entorno de cuidado simulado
dc.title.TranslatedTitleValidation of an IoT System with Federated Learning and TinyML for Postural Monitoring in a Simulated Care Environment
dc.typemasterThesis
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTesis de maestría
local.department.reportEscuela de Ciencias e Ingeniería
local.regionesBogotá
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Maestría en Ingeniería Biomédica