The aim of this study was to test the hypothesis that high levels of testosterone during prenatal life, testified by a low second-to-fourth digit ratio (2D:4D), as well as in adulthood affect the aggressive behavior of professional soccer players. Using 18 male professional players from a first level Italian Soccer Team we calculated: i) the 2D:4D ratio of the right hand, ii) the number of yellow and red cards per game, iii) the mean salivary testosterone concentration (Sal/T) and iv) the handling of aggressive impulses as assessed by the Picture Frustration test (PFT). Soccer players with a lower 2D:4D ratio had a higher number of fouls per game. A significant negative correlation was observed between Sal/T and 2D:4D ratio, as well as between 2D:4D ratio and the aggressiveness of players. By contrast, a significant positive correlation of Sal/T and fouls/game score and PFT was detected. No significant correlation was detected between 2D:4D or Sal/T and the playing position of players. Results of this study revealed that in professional soccer players, aggressive behavior, with the consequent increased risk of fouls during the game, is more likely to occur in individuals with high testosterone levels, not only in adulthood, but also during their intrauterine life.

The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.

Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

The aim of the present study was investigate if there is an association between second-to-fourth digit length (2D:4D) ratio and personality factors capable of serving as predictors of individual choice towards high-risk activities in a group of experts skydivers; Furthermore, their skills in regulating anxiety and emotions were assessed. The 2D:4D ratio of the right hand of 41 expert skydivers was measured and each of them completed four questionnaires: Big Five Questionnaire-2 (BFQ-2), Profile of Mood States (POMS), State-Trait Anxiety Inventory Form Y (STAI-Y) and Risk-Taking Inventory. Lower 2D:4D ratios did not appear associated with a greater propensity for taking risks but rather with a lower aptitude to assume precautions in unsafe conditions. In fact, the only sub-dimensions of personality, analyzed by the BFQ-2, correlated with the 2D:4D ratio were conscientiousness and agreeableness. Furthermore, prior to launch, the skydiver's level of stress, measured by the POMS, or state anxiety, measured by the STAI-Y, was not significantly correlated with 2D:4D ratio; whereas there was significant positive correlation between 2D:4D values and trait anxiety. Data analysis further revealed that social desirability correlated negatively with state anxiety and total mood disturbance index, and positively with emotion control. The present results suggest that lower 2D:4D ratio may represent a significant predictor of less attentive precautionary behavior when risk-taking.