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.

Visual processing of emotional stimuli critically depends on the type of cognitive appraisal involved. The present fMRI pilot study aimed to investigate the cerebral correlates involved in the visual processing of emotional scenes in two tasks, one emotional, based on the appraisal of personal emotional experience, and the other motivational, based on the appraisal of the tendency to action. Given that the use of spatial frequency information is relatively flexible during the visual processing of emotional stimuli depending on the task’s demands, we also explored the effect of the type of spatial frequency in visual stimuli in each task by using emotional scenes filtered in low spatial frequency (LSF) and high spatial frequencies (HSF). Activation was observed in the visual areas of the fusiform gyrus for all emotional scenes in both tasks, and in the amygdala for unpleasant scenes only. The motivational task induced additional activation in frontal motor-related areas (e.g. premotor cortex, SMA) and parietal regions (e.g. superior and inferior parietal lobules). Parietal regions were recruited particularly during the motivational appraisal of approach in response to pleasant scenes. These frontal and parietal activations, respectively, suggest that motor and navigation processes play a specific role in the identification of the tendency to action in the motivational task. Furthermore, activity observed in the motivational task, in response to both pleasant and unpleasant scenes, was significantly greater for HSF than for LSF scenes, suggesting that the tendency to action is driven mainly by the detailed information contained in scenes. Results for the emotional task suggest that spatial frequencies play only a small role in the evaluation of unpleasant and pleasant emotions. Our preliminary study revealed a partial distinction between visual processing of emotional scenes during identification of the tendency to action, and during identification of personal emotional experiences. It also illustrates flexible use of the spatial frequencies contained in scenes depending on their emotional valence and on task demands.

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.

Research on emotion suggests that the attentional preference observed toward the negative stimuli in young adults tends to disappear in normal aging and, sometimes, to shift toward a preference for positive stimuli. The current eye-tracking study investigated visual exploration of paired natural scenes of different valence (Negative-Neutral, Positive-Neutral, and Negative-Positive pairs) in three age groups (young, middle-aged, and older adults). Two arousal levels of stimuli (high and low arousal) were also considered given role of this factor in age-related effects on emotion. Results showed the automatic attentional orienting toward the negative stimuli was relatively preserved in our three age groups although reduced in the elderly, in both arousal conditions. A similar negativity bias was also observed in initial attention focusing but shifted toward a positivity bias over time in the three age groups. Moreover, it appeared the spatial exploration of emotional scenes evolved over time differently for older adults compared with other age groups. No difference between young adults and middle-aged adults in ocular behavior was observed. This study confirms the interest of studying both spatial and temporal characteristics of oculomotor behaviors to better understand the age-related effects on emotion.

Healthy aging is accompanied by subtle difficulties in language production. While behavioral and neuroimaging studies suggest that older adults rely on acute semantic access to maintain language abilities, the underlying neurophysiological mechanisms remain poorly understood. In particular, it is still unclear how large-scale brain dynamics reorganize to support naturalistic sentence generation with age. In this study, we investigated the spatiotemporal brain-state dynamics during covert sentence generation (GE2REC protocol) in younger and older adults using magnetoencephalography (MEG). Source-reconstructed MEG signals were analyzed using a Hidden Markov Model which identified five recurrent brain states, encompassing language-semantic, language-control, sensorimotor, and visual domains. Latent modeling was then used to relate the spectral and temporal properties of these brain states to age and language performance. Spectrally, older adults appear to redistribute oscillatory activity from sensorimotor-related states toward semantic-related states across alpha, beta, and low-gamma frequency bands. Temporally, older adults exhibit a more segmented processing sequence between semantic and sensorimotor processing which interfaces with visuo-posterior processing. These changes robustly covaried with age and better verbal fluency (semantic & lexical). Taken together, these results suggest that the older adult brain undergoes a coordinated time-frequency reorganization to support sentence production. Individuals likely establish an embodied semantic strategy in aging that involves “chunking” the processing stages of sentence production via visuo-posterior information processing. We speculate that this may help shape a resource-efficient, predictive route for complex cognition in older adulthood.