Assessment of Mental Stress Through the Analysis of Physiological Signals Acquired From Wearable Devices

  • Matteo ZanettiEmail author
  • Luca Faes
  • Mariolino De Cecco
  • Alberto Fornaser
  • Martina Valente
  • Giovanni Guandalini
  • Giandomenico Nollo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 544)


Mental stress is a physiological state that directly correlates to the quality of life of individuals. Generally speaking, but especially true for disabled or elderly subjects, the assessment of such condition represents a very strong indicator correlated to the difficulties, and, in some case, to the frustration that derives from the execution of a task that results troublesome to be accomplished. This article describes a novel procedure for the assessment of the mental stress level through the use of low invasive wireless wearable devices. The information contained in electrocardiogram, respiratory signal, blood volume pulse, and electroencephalogram was extracted to set up an estimator for the cognitive workload level. A random forest classifier was implemented to assess the level of mental stress starting from a pool of 3481 features computed from the aforementioned physiological quantities. The proposed system was applied in a scenario in which two different mental states were elicited in the subject under investigation: first, a baseline resting condition was induced by the presentation of a relaxing video; then a stressful cognitive state was provoked by the administration of a mental arithmetic task. The random forest classifier shows an accuracy of 97.5% in discerning between these two mental states.


Stress assessment Network physiology Wearable devices Measurements Classification Machine learning 



This research was developed with the support of the IEEE Smart Cities Initiative—Student Grant Program and AUSILIA project financed by Provincia Autonoma di Trento (2015–2018).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Matteo Zanetti
    • 1
    Email author
  • Luca Faes
    • 2
  • Mariolino De Cecco
    • 1
  • Alberto Fornaser
    • 1
  • Martina Valente
    • 3
  • Giovanni Guandalini
    • 4
  • Giandomenico Nollo
    • 1
  1. 1.Department of Industrial EngineeringUniversity of TrentoPovoItaly
  2. 2.Department of Energy, Information Engineering and Mathematical ModelsUniversity of PalermoPalermoItaly
  3. 3.Center for Neuroscience and Cognitive SystemUniversity of TrentoRoveretoItaly
  4. 4.Villa Rosa hospitalAzienda Provinciale per i Servizi Sanitari (APSS)Pergine ValsuganaItaly

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