Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review

Reconocimiento de actividades humanas por medio de extracción de características y técnicas de inteligencia artificial: una revisión

Autores/as

Palabras clave:

reconocimiento de la actividad humana, detección de caídas, tipos de actividades, extracción de características, redes neuronales convolucionales (es).

Palabras clave:

human activity recognition, fall detection, type of activities, feature extraction, convolutional neural networks (en).

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Resumen (en)

Context: In recent years, the recognition of human activities has become an area of constant exploration in different fields. This article presents a literature review focused on the different types of human activities and information acquisition devices for the recognition of activities. It also delves into elderly fall detection via computer vision using feature extraction methods and artificial intelligence techniques.

Methodology: This manuscript was elaborated following the criteria of the document review and analysis methodology (RAD), dividing the research process into the heuristics and hermeneutics of the information sources. Finally, 102 research works were referenced, which made it possible to provide information on current state of the recognition of human activities.

Results: The analysis of the proposed techniques for the recognition of human activities shows the importance of efficient fall detection. Although it is true that, at present, positive results are obtained with the techniques described in this article, their study environments are controlled, which does not contribute to the real advancement of research.

Conclusions: It would be of great impact to present the results of studies in environments similar to reality, which is why it is essential to focus research on the development of databases with real falls of adults or in uncontrolled environments.

Resumen (es)

Contexto: En los últimos años, el reconocimiento de actividades humanas se ha convertido en un área de constante exploración en diferentes campos. Este artículo presenta una revisión de la literatura enfocada en diferentes tipos de actividades humanas y dispositivos de adquisición de información para el reconocimiento de actividades, y profundiza en la detección de caídas de personas de tercera edad por medio de visión computacional, utilizando métodos de extracción de características y técnicas de inteligencia artificial.

Metodología: Este manuscrito se elaboró con criterios de la metodología de revisión y análisis documental (RAD), dividiendo el proceso de investigación en heurística y hermenéutica de las fuentes de información. Finalmente, se referenciaron 102 investigaciones que permitieron dar a conocer la actualidad del reconocimiento de actividades humanas.

Resultados: El análisis de las técnicas propuestas para el reconocimiento de actividades humanas muestra la importancia de la detección eficiente de caídas. Si bien es cierto en la actualidad se obtienen resultados positivos con las técnicas descritas en este artículo, sus entornos de estudio son controlados, lo cual no contribuye al verdadero avance de las investigaciones.

Conclusiones: Sería de gran impacto presentar resultados de estudios en entornos semejantes a la realidad, por lo que es primordial centrar el trabajo de investigación en la elaboración de bases de datos con caídas reales de personas adultas o en entornos no controlados.

Biografía del autor/a

José Camilo Eraso Guerrero, Universidad del Cauca

Ingeniero electrónico, candidato a Magíster en Automática. Universidad del Cauca

Elena Muñoz España, Universidad del Cauca

Ingeniero en Electrónica y Telecomunicaciones, especialista en Informática Industrial, especialista en Redes y Servicios Telemáticos, Magíster en Electrónica y Telecomunicaciones. Profesor de la Universidad del Cauca

Mariela Muñoz Añasco, Universidad del Cauca

Ingeniero Industrial, Magíster en Administración de Empresas, Magíster en Automática, Doctorado en Automática, Robótica e Informática Industrial. Profesor de la Universidad del Cauca.

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Cómo citar

APA

Eraso Guerrero, J. C., Muñoz España, E. ., & Muñoz Añasco, M. (2022). Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura, 26(74), 213–236. https://doi.org/10.14483/22487638.17413

ACM

[1]
Eraso Guerrero, J.C., Muñoz España, E. y Muñoz Añasco, M. 2022. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura. 26, 74 (sep. 2022), 213–236. DOI:https://doi.org/10.14483/22487638.17413.

ACS

(1)
Eraso Guerrero, J. C.; Muñoz España, E. .; Muñoz Añasco, M. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura 2022, 26, 213-236.

ABNT

ERASO GUERRERO, J. C.; MUÑOZ ESPAÑA, E. .; MUÑOZ AÑASCO, M. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura, [S. l.], v. 26, n. 74, p. 213–236, 2022. DOI: 10.14483/22487638.17413. Disponível em: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/17413. Acesso em: 26 sep. 2022.

Chicago

Eraso Guerrero, José Camilo, Elena Muñoz España, y Mariela Muñoz Añasco. 2022. «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review». Tecnura 26 (74):213-36. https://doi.org/10.14483/22487638.17413.

Harvard

Eraso Guerrero, J. C., Muñoz España, E. . y Muñoz Añasco, M. (2022) «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review», Tecnura, 26(74), pp. 213–236. doi: 10.14483/22487638.17413.

IEEE

[1]
J. C. Eraso Guerrero, E. . Muñoz España, y M. Muñoz Añasco, «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review», Tecnura, vol. 26, n.º 74, pp. 213–236, sep. 2022.

MLA

Eraso Guerrero, J. C., E. . Muñoz España, y M. Muñoz Añasco. «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review». Tecnura, vol. 26, n.º 74, septiembre de 2022, pp. 213-36, doi:10.14483/22487638.17413.

Turabian

Eraso Guerrero, José Camilo, Elena Muñoz España, y Mariela Muñoz Añasco. «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review». Tecnura 26, no. 74 (septiembre 25, 2022): 213–236. Accedido septiembre 26, 2022. https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/17413.

Vancouver

1.
Eraso Guerrero JC, Muñoz España E, Muñoz Añasco M. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura [Internet]. 25 de septiembre de 2022 [citado 26 de septiembre de 2022];26(74):213-36. Disponible en: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/17413

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