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.