Artificial Intelligence of Behavior for Human Emotion Recognition in Closed Environments
| dc.contributor.author | Alvarez Garcia, Gonzalo Alberto | |
| dc.contributor.author | Zuniga Canon, Claudia | |
| dc.contributor.author | Garcia Sanchez, Antonio Javier | |
| dc.contributor.author | Garcia Haro, Joan | |
| dc.contributor.author | Sarria Paja, Milton | |
| dc.contributor.author | Asorey Cacheda, Rafael | |
| dc.date.accessioned | 2025-07-08T20:08:22Z | |
| dc.date.available | 2025-07-08T20:08:22Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Understanding human emotions and behavior in closed environments is essential for creating more empathetic and humane spaces. Environmental factors, such as temperature, noise, and light, play a crucial role in influencing behavior, but individuals' emotional states are equally important and often go unnoticed. Artificial Intelligence of Behavior (AIoB) offers a novel approach that integrates environmental measurements with human emotions to create spatially adaptive processes that can influence behavior. In this article, we present a new human emotion sensor developed using video cameras and implemented on a System on Chip (SoC) development board. Our approach uses Convolutional Neural Networks (CNNs) to recognize the presence of emotions in enclosed spaces and generate parameters that can influence emotional states and behavior within an AIoB system. The research successfully integrates advanced CNN technology into a System on Chip (SoC) platform, allowing for real-time processing of video data. The versatility of utilizing an energy-efficient SoC extends its application to smart environments aimed at improving mental health. By employing algorithms capable of detecting emotional states across various individuals, the study enhances its effectiveness. Additionally, it identifies the best CNN operations tailored to the technical specifications of the devices involved. Thus, The development involves a three-step process: (i) collecting enough data to build a robust model, (ii) training the model and evaluating its performance using test values, and (iii) applying the model on the development board. Our study demonstrates the feasibility of using AIoB to recognize and respond to human emotions in closed areas. By integrating emotional cues with environmental measurements, our system can create more personalized and empathetic spaces that cater to the needs of individuals. Our approach could have significant implications for designing public spaces to promote well-being and emotional satisfaction. | |
| dc.identifier.citation | G. -A. Alvarez-Garcia, C. Zúñiga-Cañón, A. -J. Garcia-Sanchez, J. Garcia-Haro, M. Sarria-Paja and R. Asorey-Cacheda, "Artificial Intelligence of Behavior for Human Emotion Recognition in Closed Environments," in IEEE Open Journal of the Computer Society, vol. 5, pp. 578-588, 2024, https://doi.org/10.1109/OJCS.2024.3463173 | |
| dc.identifier.issn | 26441268 | |
| dc.identifier.uri | https://repositorio.usc.edu.co/handle/20.500.12421/7272 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | compressive sensing | |
| dc.subject | Convolutional neural networks | |
| dc.subject | emotions | |
| dc.subject | image classification | |
| dc.subject | instrumentation | |
| dc.subject | system on chip | |
| dc.title | Artificial Intelligence of Behavior for Human Emotion Recognition in Closed Environments | |
| dc.type | Article |
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