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The presence and level of stress in clinical practice are confirmed by taking and analysing blood or saliva samples to measure the cortisol hormone level. Due to these health concerns, there has been an increased effort to develop for the detection and assessment of stressful events in everyday situations to support people in minimising these harmful effects. Whether physical, emotional or mental strain, the prolonged presence of stress contributes to developing chronic diseases such as diabetes, cardiovascular and respiratory conditions, depression and even some forms of cancers.
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This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.Īs stress became one of the main problems of modern societies, its adverse effects are quite well known even to the general population. The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%.
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Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. The accuracy and F1 score of the trained models were compared and analysed. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions.
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Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Methodsįeatures extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices.
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This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations.