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  • 1. Mohino-Herranz, Inma
    et al.
    Gil-Pita, Roberto
    Ferreira, Javier
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. University of Boras, Boras, Sweden.
    Rosa-Zurera, Manuel
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. University of Boras, Boras, Sweden.
    Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones2015In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 10, p. 25607-25627Article in journal (Refereed)
    Abstract [en]

    Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.

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