Conjuntos de Datos para el Reconocimiento de Actividades Humanas: Un Análisis Comparativo

Autores/as

DOI:

https://doi.org/10.47756/aihc.y10i1.198

Palabras clave:

Reconocimiento de Actividades Humanas, Interacción Humano–Computadora, Conjuntos de Datos Públicos, Desbalance de Datos

Resumen

El reconocimiento de actividades humanas es un área clave en la interacción humano–computadora, ya que permite que los sistemas comprendan las acciones de las personas y respondan mediante forma implícita, sin necesidad de comandos directos. Este artículo presenta un análisis comparativo de seis conjuntos de datos públicos ampliamente utilizados, considerando factores como la diversidad de participantes, la variedad de actividades, los tipos de sensores y las condiciones de captura. Los resultados muestran que, aunque estos conjuntos de datos han impulsado el desarrollo de modelos más precisos, aún presentan limitaciones relacionadas con la falta de diversidad y el desbalance de clases, lo que afecta la capacidad de generalización de los sistemas en contextos del mundo real. Se destaca la necesidad de diseñar conjuntos de datos más inclusivos y representativos que reflejen la complejidad de la interacción humana y apoyen el desarrollo de sistemas de reconocimiento de actividades más justos, robustos y adaptativos.

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Biografía del autor/a

Luis Felipe Beltrán Mercado, Instituto Tecnológico de Sonora (ITSON)

He holds a Master's degree in Engineering Sciences, specializing in Control and Systems, and a degree in Mechatronics Engineering from the Technological Institute of Sonora (ITSON). He is currently pursuing a PhD in Engineering Sciences, with a specialization in Computer Science, at ITSON, where he focuses on human activity recognition through deep learning models and the analysis of large volumes of data. His areas of interest include signal processing, simulations, and the development of advanced tools in Artificial Intelligence.

Jessica Beltr´án M´árquez, Centro de Investigación en Matemáticas Aplicadas de la Universidad Autonoma de Coahuila

I have a PhD degree in Computer Science from CICESE, México. As part of my doctoral dissertation, I worked with auditory information to infer the context of older adults. My overall research goal is to advance research in activity and behavior estimation to support the development of pervasive applications, particularly in the domain of healthcare. I am interested in Machine Learning, Signal Processing, Feature Extraction, Context-Aware Computing and Information Retrieval. I received M.Sc. in Computer Science from CICESE, México. As part of my master’s thesis, I worked with image processing and classification for identifying tattoos as part of a soft biometric application. I received my Bachelor’s degree in Electronic Engineering at Sonora Institute of Technology, México. Currently, I am working as Researcher Professor at the Research Center of Applied Math (CIMA) from the Autonomus University of Coahuila (UAdeC).

Luis Adrian Castro Quiroa, Instituto Tecnológico de Sonora (ITSON)

Luis Castro works as a full professor at the Dept. of Computing and Design at the Sonora Institute of Technology (ITSON), Mexico. He holds a Ph.D. in Informatics from the University of Manchester, UK. Castro's main research interests are Community Informatics; Intelligent Systems; Human-Computer Interaction; Interaction Design; and Ubiquitous and Mobile Computing. Dr. Castro has participated in several projects aimed at designing technology for vulnerable populations. Dr. Castro is a professional member of the ACM and a member of the National System of Researchers from the National Council for Science and Technology in Mexico (SNI-CONACYT). He is the former president of the Mexican Association on Human-Computer Interaction (AMexIHC), the former president of the Society of Mexican Students in the United Kingdom, and a former member of the board of the Mexican Association on Computer Science.

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Publicado

2025-11-30

Cómo citar

[1]
Beltrán Mercado, L.F. et al. 2025. Conjuntos de Datos para el Reconocimiento de Actividades Humanas: Un Análisis Comparativo. Avances en Interacción Humano-Computadora. 10, 1 (nov. 2025), 23–32. DOI:https://doi.org/10.47756/aihc.y10i1.198.

Número

Vol. 10 Núm. 1 (2025): Número regular, 2025

Sección

Artículos de Investigación

Licencia

Derechos de autor 2025 Luis Felipe Beltrán Mercado, Jessica Beltrán Márquez, Luis A. Castro

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

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