We describe a multimodal dataset acquired in a controlled experiment on a driving simulator. The set includes data for n=68 volunteers that drove the same highway under four different conditions: No distraction, cognitive distraction, emotional distraction, and sensorimotor distraction. The experiment closed with a special driving session, where all subjects experienced a startle stimulus in the form of unintended acceleration—half of them under a mixed distraction, and the other half in the absence of a distraction. During the experimental drives key response variables and several explanatory variables were continuously recorded. The response variables included speed, acceleration, brake force, steering, and lane position signals, while the explanatory variables included perinasal electrodermal activity (EDA), palm EDA, heart rate, breathing rate, and facial expression signals; biographical and psychometric covariates as well as eye tracking data were also obtained. This dataset enables research into driving behaviors under neatly abstracted distracting stressors, which account for many car crashes. The set can also be used in physiological channel benchmarking and multispectral face recognition.

The negative impact of strong sympathetic arousal on dexterous performance during formal surgical training is well-known. This study investigates how this relationship might change if surgical training takes place as a hobby in an informal environment. Fifteen medical students volunteered in a 5-week training regimen and weekly performed two standardized microsurgical tasks: circular cutting and simple interrupted suturing. Time was taken and two independent reviewers evaluated the surgical proficiency. The State Trait Anxiety Inventory (STAI) and the NASA Task Load Index (NASA-TLX) questionnaires measured subjective anxiety and workload, respectively. A high-resolution thermal imaging camera recorded facial imagery, from which a computational algorithm extracted the perinasal perspiration signal as indicator of sympathetic arousal. Anxiety scores on STAI questionnaires were indifferent for all five sessions. The continuously measured arousal signal from the thermal facial imagery was moderate and did not correlate with surgical proficiency or speed. Progressive experience was the strongest contributor to improved skill and speed, which were attained in record time. It appears that dexterous skill acquisition is facilitated by the absence of strong arousals, which can be naturally eliminated in the context of informal education. Given the low cost and availability of surgical simulators, this result opens the way for re-thinking the current practices in surgical training and beyond.

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Walking is a fundamental human activity and its diminution a potential morbidity factor. Recent developments in mobile computing have enabled ubiquitous monitoring of walking activity via the smartphone accelerometers. Typically, walking apps map accelerometer values to caloric values through calibration algorithms. However, these calibration algorithms assume a flat surface, which is not always true and can introduce significant errors. In this paper, we outline a novel calibration method that estimates surface inclination for uphill walking, thus, improving the caloric estimation in walking apps.

Physical activity is an area of life in which social influence plays a major role. Observing the activity of a sedentary person may cause the observer to exercise less; observing a persistently active person can serve as a motivating factor. The goal of this research is to determine how to optimally pair individuals in order to facilitate motivational relationships with respect to physical activity. This research performs an observational study of data collected from a mobile health and fitness application, iBurnCalorie, which allows users to follow each other in addition to tracking physical activity. Through this social feature, this study examines the influence of users on each other's activity patterns. Our preliminary results indicate that some users have chosen effective role models without any intervention. If this natural effect can be replicated, such a novel interventional networking feature could have a significant impact within iBurnCalorie and all similar applications.