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

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.
Index Terms—Machine learning, Internet of Things, Blood
Volume Pulse, Heart Rate Variability, wearable device, stress
detection, biomedical measurement system, IoT-enabled system.