Workplace stress assessment using emotional recognition and heart rate techniques
This study aims to develop a mechanism to evaluate employee health through emotion recognition (ER) analysis and heart rate measurement, with the goal of establishing correlations with stress levels. It is the second phase of a previous study, where current results will be compared to those previou...
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| Formato: | Articulo |
| Lenguaje: | Español |
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2025
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/178019 |
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I19-R120-10915-1780192025-05-06T17:17:49Z http://sedici.unlp.edu.ar/handle/10915/178019 Workplace stress assessment using emotional recognition and heart rate techniques Evaluación de estrés laboral mediante técnicas de reconocimiento emocional y ritmo cardíaco Vega, Alejandro Bilbao, Martín Falappa, Marcelo Alejandro 2025-04 2025-04-08T14:09:13Z es Ciencias Informáticas Inteligencia artificial Deep Face Reconocimiento de emociones Salud Artificial intelligence Emotion recognition Health This study aims to develop a mechanism to evaluate employee health through emotion recognition (ER) analysis and heart rate measurement, with the goal of establishing correlations with stress levels. It is the second phase of a previous study, where current results will be compared to those previously obtained. In both studies, biometric devices such as cameras were used to capture facial images analyzed with artificial intelligence, and sensors in mobile phones or smartwatches to record heart rate. Given the challenge of emotion detection, we propase using the DeepFace algorithm for facial emotion recognition, which has demonstrated a 94% accuracy. Additionally, employees will complete a self-administered questionnaire about their emotional and mental state (neutral, tired, energized), allowing for a comparison between detected emotions and subjective reports. This will provide a better understanding of the accuracy of emotion recognition and contribute to improving health status evaluation. Este estudio tiene como objetivo desarrollar un mecanismo para evaluar la salud de los empleados mediante el análisis de reconocimiento de emociones (RE) y la medición del ritmo cardíaco, con el fin de establecer correlaciones con los niveles de estrés. Se trata de la segunda fase de un estudio previo, en la que se compararán los resultados actuales con los obtenidos anteriormente. En ambos estudios se utilizaron dispositivos biométricos, como cámaras para capturar imágenes faciales analizadas con inteligencia artificial y sensores en teléfonos móviles o relojes inteligentes para registrar el ritmo cardíaco. Dado el desafío que representa la detección de emociones, proponemos utilizar el algoritmo Deep- Face para el reconocimiento facial de emociones, el cual ha demostrado una precisión del 94%. Además, los empleados completarán un cuestionario autoadministrado sobre su estado emocional y anímico ( neutral, cansado, con energía), lo que permitirá comparar las emociones detectadas con los informes subjetivos. Esto proporcionará una mayor comprensión sobre la precisión del reconocimiento emocional y contribuirá a mejorar la evaluación del estado de salud. Sociedad Argentina de Informática e Investigación Operativa Articulo Articulo http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Español |
| topic |
Ciencias Informáticas Inteligencia artificial Deep Face Reconocimiento de emociones Salud Artificial intelligence Emotion recognition Health |
| spellingShingle |
Ciencias Informáticas Inteligencia artificial Deep Face Reconocimiento de emociones Salud Artificial intelligence Emotion recognition Health Vega, Alejandro Bilbao, Martín Falappa, Marcelo Alejandro Workplace stress assessment using emotional recognition and heart rate techniques |
| topic_facet |
Ciencias Informáticas Inteligencia artificial Deep Face Reconocimiento de emociones Salud Artificial intelligence Emotion recognition Health |
| description |
This study aims to develop a mechanism to evaluate employee health through emotion recognition (ER) analysis and heart rate measurement, with the goal of establishing correlations with stress levels.
It is the second phase of a previous study, where current results will be compared to those previously obtained. In both studies, biometric devices such as cameras were used to capture facial images analyzed with artificial intelligence, and sensors in mobile phones or smartwatches to record heart rate. Given the challenge of emotion detection, we propase using the DeepFace algorithm for facial emotion recognition, which has demonstrated a 94% accuracy. Additionally, employees will complete a self-administered questionnaire about their emotional and mental state (neutral, tired, energized), allowing for a comparison between detected emotions and subjective reports. This will provide a better understanding of the accuracy of emotion recognition and contribute to improving health status evaluation. |
| format |
Articulo Articulo |
| author |
Vega, Alejandro Bilbao, Martín Falappa, Marcelo Alejandro |
| author_facet |
Vega, Alejandro Bilbao, Martín Falappa, Marcelo Alejandro |
| author_sort |
Vega, Alejandro |
| title |
Workplace stress assessment using emotional recognition and heart rate techniques |
| title_short |
Workplace stress assessment using emotional recognition and heart rate techniques |
| title_full |
Workplace stress assessment using emotional recognition and heart rate techniques |
| title_fullStr |
Workplace stress assessment using emotional recognition and heart rate techniques |
| title_full_unstemmed |
Workplace stress assessment using emotional recognition and heart rate techniques |
| title_sort |
workplace stress assessment using emotional recognition and heart rate techniques |
| publishDate |
2025 |
| url |
http://sedici.unlp.edu.ar/handle/10915/178019 |
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