RESUMENLa investigación se enfocó en identificar, evaluar y controlar los riesgos psicosociales en la Seguridad Industrial de las lavanderías textiles, evidenciando el nivel de impacto, lo cual sirvió de aporte al bienestar físico, psicológico y emocional de los trabajadores, mejorando su calidad de vida, incrementando la productividad y el cumplimiento de los objetivos organizacionales. La población estuvo conformada por hombres y mujeres correspondientes a sesenta y un trabajadores, con un rango de edad entre quince y sesenta y cinco años, de diferentes etnias y se utilizó como metodología el Cuestionario Estandarizado Istas 21 del Instituto Sindical de Trabajo, Ambiente y Salud de España. Consta de seis apartados relacionados a los riesgos psicosociales. En los niveles investigativos se destaca el descriptivo y el explicativo, obteniendo niveles y valores de referencia que permiten comparar con otros estudios sobre factores psicosociales. Posteriormente, se diseñó una Guía de Prevención de Factores Psicosociales para el mejoramiento de la Seguridad Industrial en las lavanderías textiles la cual sirvió como herramienta para la capacitación de los trabajadores en relación a esta temática, a continuación se realizó el retest con el Cuestionario Estandarizado Istas 21 el cual arrojó resultados diferentes a los obtenidos inicialmente, sin embargo es posible apreciar que los apartados con mayor impacto sobre el personal son inseguridad sobre el futuro y 1

Abstract. Loitering is a common behaviour of the elderly people. We goal is develop an artificial intelligence system that automatically detects loitering behaviour in video surveillance environments. The first step to identify this behaviour was used a Generalized Sequential Patterns that detects sequential micro-patterns in the input loitering video sequences. The test phase determines the appropriate percentage of inclusion of this set of micro-patterns in a new input sequence, namely those that are considered to form part of the profile, and then be identified as loitering. The system is dynamic; it obtains micro-patterns on a repetitive basis. During the execution time, the system takes into account the human operator and updates the performance values of loitering in shopping mall. The profile obtained is consistent with what has been documented by experts in this field and is sufficient to focus the attention of the human operator on the surveillance monitor.

The study of human emotions has been significantly important in recent years, mainly due to its incidence in human behavior. Additionally, having semantic tools that infer emotions from multisensory sources is a crucial aspect, especially because the feelings or actions of a person might be identified through these semantic tools. In the present research, a methodology that uses semantic structures is proposed in order to identify complex emotions on the basis of simple emotions. For this purpose, the SHEO ontology was used. This ontology is designed to conceptualize simple emotions, combine them, and work with axioms and rules that infer complex emotions. SHEO takes simple emotions as instances. These emotions can be identified using computer algorithms. This is demonstrated in the testing phase in which the authors of this research designed the software called DetectionEmotion, which is used to identify simple emotions in video and text. The result of the authors' proposal proved the easiness to infer complex emotions by using SHEO. SHEO is not a final solution in this research, but rather a contribution to the semantic management of emotions.

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