Cross validation of bi-modal health-related stress assessment

Egon van den Broek, Frans van der Sluis, Ton Dijkstra

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    12 Citations (Scopus)
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    This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care.
    Original languageUndefined
    Pages (from-to)215
    Number of pages13
    JournalPersonal and ubiquitous computing
    Issue number2
    Publication statusPublished - Feb 2013


    • Post-traumatic stress disorder (PTSD)
    • Stress
    • Speech
    • Validity
    • Computer Aided Diagnostics (CAD)
    • HMI-SLT: Speech and Language Technology
    • HMI-HF: Human Factors
    • HMI-CI: Computational Intelligence
    • EWI-20759
    • IR-79495
    • Machine Learning
    • METIS-284917

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