Key result
AI-powered wearable device enables real-time, privacy-preserving early detection of burnout using physiological parameters.
Why the study?
High demands on employee performance frequently lead to burnout, motivating technical solutions for early detection to improve body and mind health condition.
Does a wearable device measuring physiological parameters combined with an artificial neural network accurately estimate burnout state in employees?
Does a wearable device measuring physiological parameters combined with an artificial neural network accurately estimate burnout state in employees?
A wearable device utilizing an artificial neural network to analyze physiological parameters offers a potential real-time, privacy-preserving method for early burnout detection.
May support wearable monitoring for occupational burnout; leaves open prospective validation before adoption.
This world, the way we know it, dramatically changes every day due to unexpected technological developments with high influence on human wellbeing and health. Today, more than ever, the employees experience high demanding regarding their performance at work with direct consequences on personal comfort, leading, in most of cases, to an undesired burnout state. Early detection of the burnout state with the support of a wearable device is presented in this work. Based on data acquisition followed by local and remote optical wireless communication (OWC) technology, the Heart Rate (HR), the peripheral capillary oxygen saturation (SpO2) as well as Galvanic Skin (GS) values are analyzed and a fast estimation of burnout state is obtained due to artificial neuronal network (ANN). Burnout state early detection allows to increase individual, both body and mind health condition. The solution from this paper has two main advantages. The first is related to the correlation between the results of the quantitative research through questionnaires and of the qualitative research through physiological parameters measurement done by an ANN. The second is related to real-time burnout state estimation based only on measurements and without any outside communication of data, the only protocol for device setup is OWC, that ensures safety and privacy of data.
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Riurean et al. (2020) studied Burnout. Wearable device with optical wireless communication (OWC) and artificial neuronal network (ANN) was evaluated on Early detection and real-time estimation of burnout state. A wearable device utilizing optical wireless communication and an artificial neuronal network enables real-time, privacy-preserving early detection of burnout state using physiological parameters.
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