Evaluation of Work-Related Stress Amongst Industrial Workers

Kumari, Anuradha; Singh, Ravindra

doi:10.1007/978-981-15-8542-5_27

Cited by 1 publication

(1 citation statement)

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“…Moreover, our analyses highlighted the feasibility to employ a faster and less power and time-consuming method to compute entropy measures exploiting a linear Gaussian approximation. This could have practical implications and pave the way to the implementation of the linear algorithms for DE and IS estimation within the firmware of mobile or wearable devices or within fitness or medical apps for smartphones or smartwatches, to allow a real-time assessment of the stress level of people during daily-life situations [13], [14]. Another future activity should foresee the investigation on the application of the entropy measures on shorter HRV time series, i.e.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Linear Parametric Estimation of Dynamic Information Measures to assess Physiological Stress from Short-Term Cardiovascular Variability

Pernice

Volpes

Krohová

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Extensive efforts have been recently devoted to implement fast and reliable algorithms capable of assessing the physiological response of the organism to physiological stress. In this study, we propose the comparison between model-free and linear parametric methods as regards their ability to detect alterations in the dynamics and in the complexity of cardiovascular and respiratory variability evoked by postural and mental stress. Dynamic entropy (DE) and information storage (IS) measures were calculated on three physiological time-series, i.e. heart period, respiratory volume and systolic arterial pressure, on 61 healthy subjects monitored in resting conditions as well as during head-up tilt and while performing a mental arithmetic task. The results of the comparison suggest the feasibility of DE and IS measures computed from different physiological signals to discriminate among resting and stress states. If compared to the model-free algorithm, the faster linear method appears to be capable of detecting the same (or even more) statistically significant variations of DE or IS between resting and stress conditions, being thus in perspective more suitable for the integration within wearable devices. The computation of entropy indices extracted from multiple physiological signals acquired through wearables will allow a real-time stress assessment on people during daily-life situations.

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Section: Discussionmentioning

confidence: 99%