This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.

The reproducibility of neuroimaging analyses across computational environments has gained significant attention over the last few years. While software containerization solutions such as Docker and Singularity have been deployed to mask the effects of software-induced variability, variations in hardware architectures still impact neuroimaging results in an unclear way. We study the effect of hardware variability on linear registration results produced by the FSL FLIRT application, a widely-used software component in neuroimaging data analyses. Using the Grid’5000 infrastructure, we study the effect of nine different CPU models using two software packaging systems (Docker and Guix), and we compare the resulting hardware variability to numerical variability measured with random rounding. Results show that hardware, software, and numerical variability lead to perturbations of similar magnitudes — albeit uncorrelated — suggesting that these three types of variability act as independent sources of numerical noise with similar magnitude. Therefore, random rounding is a practical solution to measure the effect of numerical noise induced by hardware variability in this application. The effect of hardware perturbations on linear registration remains moderate, with average translation errors of 0.1 mm (maximum: 0.5 mm) and average rotation errors of 0.02 deg (maximum: 0.2 deg). Such variations might impact downstream analyses when linear registration is used as initialization step for other operations.

Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.

Objectives : Physiological sensors appear a promising tool to monitor psychological stress in the daily life. Yet, despite the increasing know-how developed in affective computing, physiology-driven stress detection algorithms still yield unsatisfactory results on the field. This study intends to highlight physiological patterns that are both stress-sensitive and stress-specific, at the multi-subject scale, in several contexts. Approach : A notional framework was developed and empirically validated, which distinguishes psychological stress from two context-related confounding factors: Background and Activity. A physiological database was elaborated from a multi-task experimental protocol in a standardized environment with skin conductance, electrocardiograph and respiration recordings. Stress-induced variations were isolated in standard physiological features, to select the most stress-sensitive ones independently from confounding factors. Main Results : Most standard features were unable to distinguish acute psychological stress from low cognitive activity (e.g. frequency of skin conductance responses, or spectral power in the low and high frequency bands of the interbeat intervals). After sign-rank tests between the stressful and control conditions of two standard tasks involving arithmetic or speech, their effect sizes remained below 0.3. A number of physiological features also showed task-dependency. Yet, promising correlates were highlighted among cardiac features such as mean heart rate and its standard deviation, or the root mean square of its successive differences (effect sizes above 0.3 for both tasks). Significance : The high sensitivity to context observed in standard features is likely to explain the poor validity on the field of physiology-driven models developed in controlled environments. In particular, mental activity should be considered as confounding factor while elaborating these models to increase their accuracy. The small feature set highlighted in this paper and the use of control data should enhance the generalization abilities of any machine learning algorithm; still, further research is needed to evidence new stress-specific physiological responses.