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Learning classifiers for analysis of Blood Volume Pulse signals in IoT-enabled systems | IEEE Conference Publication | IEEE Xplore

Learning classifiers for analysis of Blood Volume Pulse signals in IoT-enabled systems


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 More

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.
Date of Conference: 07-09 June 2021
Date Added to IEEE Xplore: 27 July 2021
ISBN Information:
Conference Location: Rome, Italy

References

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