Abstract
<p class="Abstract"><span id="page629R_mcid43" class="markedContent"><span dir="ltr">Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system’s architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system’s knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.</span></span></p>
Highlights
It is well known that long-term exposure to stress can lead to immunodepression and dysregulation of the immune response, significantly enhancing the risk of contracting a disease or altering its course
Experimental phase, we carried out an experiencesampling study to select the Heart-Rate Variability (HRV) features which best correlate with self-reported stress levels (Section 3)
The performance of the classification task was evaluated using the leave one out cross validation process (Section 5). The objectives of this experiment were two-fold: i) to select a subset of HRV features which best correlate with self-reported stress levels collected during everyday activities; ii) to select a subset of self-reported questions about perceived stress levels which can be used as ground truth to train the final system
Summary
It is well known that long-term exposure to stress can lead to immunodepression and dysregulation of the immune response, significantly enhancing the risk of contracting a disease or altering its course. Defining effective techniques to measure daily stressful episodes in ecological conditions has been identified as an important research objective. To address this challenge, several research groups have started investigating the use of wearable sensors solutions to infer stress from continuous biosignal measurements [5] (for a review, see [6]). Several research groups have started investigating the use of wearable sensors solutions to infer stress from continuous biosignal measurements [5] (for a review, see [6]) Such systems integrate sensors together with on-body signal conditioning and preelaboration, as well as the management of the energy consumption and wireless communication systems. The original contribution of the proposed method is that, to our best knowledge, this is the first approach that integrates the detection of HRV features with the groundtruth of subjective perception of stressful events
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