The demand for wearable devices that can detect anxiety and stress when driving is increasing. Recent studies have attempted to use multiple biosignals to detect driving stress. However, collecting multiple biosignals can be complex and is associated with numerous challenges. Determining the optimal biosignal for assessing driving stress can save lives. To the best of our knowledge, no study has investigated both longitudinal and transitional stress assessment using supervised and unsupervised ML techniques. Thus, this study hypothesizes that the optimal signal for assessing driving stress will consistently detect stress using supervised and unsupervised machine learning (ML) techniques. Two different approaches were used to assess driving stress: longitudinal (a combined repeated measurement of the same biosignals over three driving states) and transitional (switching from state to state such as city to highway driving). The longitudinal analysis did not involve a feature extraction phase while the transitional analysis involved a feature extraction phase. The longitudinal analysis consists of a novel interaction ensemble (INTENSE) that aggregates three unsupervised ML approaches: interaction principal component analysis, connectivity-based clustering, and K-means clustering. INTENSE was developed to uncover new knowledge by revealing the strongest correlation between the biosignal and driving stress marker. These three MLs each have their well-known and distinctive geometrical basis. Thus, the aggregation of their result would provide a more robust examination of the simultaneous non-causal associations between six biosignals: electrocardiogram (ECG), electromyogram, hand galvanic skin resistance, foot galvanic skin resistance, heart rate, respiration, and the driving stress marker. INTENSE indicates that ECG is highly correlated with the driving stress marker. The supervised ML algorithms confirmed that ECG is the most informative biosignal for detecting driving stress, with an overall accuracy of 75.02%.


  • The American Psychological Association has reported that people are currently living with extremely high levels of driving stress, and this is expected to increase in the coming years [1]

  • There was a need to automatically segment all biosignals based on the driving stress marker signal to statistically compare all segments

  • The two event-related moving averages (TERMA) method was used to segment the biosignals into seven periods: R1, C1, HW1, C2, HW2, C3, and R2

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The American Psychological Association has reported that people are currently living with extremely high levels of driving stress, and this is expected to increase in the coming years [1]. Researchers have found that stress plays a major role in the development and progression of cardiovascular diseases [2]. Recent advances in wearable devices, biosignal processing, machine learning (ML), and app development could contribute to providing objective feedback on driving stress. Wearable devices have already been used in several health interventions, achieving promising results [3]. The most used and best understood, biosignal in modern medicine is the electrocardiogram (ECG). ECG-based wearable devices have a great potential to succeed. Note that most of the available ECG wearable devices on the market are


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