Abstract:
Physical exertion undoubtedly influences physiological parameters. The aim of this paper is to propose a Machine Learning classifier able to evaluate the physical state o...Show MoreMetadata
Abstract:
Physical exertion undoubtedly influences physiological parameters. The aim of this paper is to propose a Machine Learning classifier able to evaluate the physical state of subjects monitored through a wearable device, by simply analysing their Blood Volume Pulse signals. Moreover, a Fatigue-Related Index is presented to quantify the physical well-being status. Results show that the Support Vector Machine classifier provides the best performance for detecting fatigue-induced stress, since it shows an accuracy of 97.50%. The obtained results prove that the proposed approach allows to support the assessment of the worker's well-being status, with the aim of improving the workload management in the context of Industry 4.0.
Published in: 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)
Date of Conference: 07-09 June 2021
Date Added to IEEE Xplore: 27 July 2021
ISBN Information: