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This article presents an exploration of the literature through a systematic mapping around the application of neurolinguistic programming techniques (NLP) supported on the Web of Things (WoT) to prevent Burnout syndrome. This syndrome is a type of work stress that causes physical, mental and emotional exhaustion, generating an inability to work, since it is a gradual process in which the worker loses interest in their tasks, lacks a sense of responsibility and can generate deep depression. In the studies found, the use of WoT for the detection of emotions and work stress stands out, for this, the use of sensors capable of measuring Galvanic response of the skin GSR, HR heart rate, PPG photoplethysmography, ECG electrocardiogram, cameras, Low-cost microphones and microprocessors, as well as the use of Artificial Intelligence to process this data, among the most used techniques and algorithms are SVM Support Vector Machines, K-nearest neighbor and Naive Bayes classifier. In jobs in which emotions or work stress are detected, very few attempt to alter the mental or environmental environment of the user to bring them to a positive emotion or reduce stress. The possibility of using NLP techniques in the prevention of Burnout syndrome was evidenced. However, no work was found that related WoT as a support to NLP techniques to prevent Burnout syndrome, which is considered as a research opportunity in these fields.
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