Individuals with intellectual and developmental disabilities (IDD) are at a high risk of experiencing pain. Pain management requires assessment, a challenging mission considering the impaired communication skills in IDD. We analyzed subjective and objective responses following calibrated experimental stimuli to determine whether they can differentiate between painful and non-painful states, and adequately quantify pain among individuals with IDD. Eighteen adults with IDD and 21 healthy controls (HC) received experimental pressure stimuli (innocuous, mildly noxious, and moderately noxious). Facial expressions (analyzed with the Facial Action Coding System (FACS)) and autonomic function (heart rate, heart rate variability (HRV), pulse, and galvanic skin response (GSR)) were continuously monitored, and self-reports using a pyramid and a numeric scale were obtained. Significant stimulus-response relationships were observed for the FACS and pyramid scores (but not for the numeric scores), and specific action units could differentiate between the noxious levels among the IDD group. FACS scores of the IDD group were higher and steeper than those of HC. HRV was overall lower among the IDD group, and GSR increased during noxious stimulation in both groups. In conclusion, the facial expressions and self-reports seem to reliably detect and quantify pain among individuals with mild-moderate IDD; their enhanced responses may indicate increased pain sensitivity that requires careful clinical consideration.

There is increasing scientific interest in elucidating the biological mechanisms underlying cooperative behaviors. Humans have developed a high degree of complexity in their cooperativity, which has been defined as hyper-cooperativity. An interesting biological marker to study how two individuals are emotionally linked when they cooperate is their psychophysiological synchronization (the overlapping of signals as indicators of Autonomous Nervous System activation). Hence, the main aim of this study was to explore participants' psychophysiological synchronization, based on electrocardiograms (ECG) and galvanic skin response (GSR) signals in a sample of strangers who were set up to cooperate (n = 29 pairs of same sex strangers; mean age = 20.52 ± 1.72), compared to participants who were forced to compete (n = 22 pairs of same sex strangers; mean age = 20.45 ± 1.53) in a laboratory setting. Moreover, the roles of the participants' gender and the outcomes (positive or negative) obtained in the cooperation were examined as potential moderators of this psychophysiological synchronization. Results showed a progressive increase in ECG and GSR signal synchronization in participants who cooperated, reaching the highest levels of synchronization during the recovery period. Moreover, cooperation induced higher GSR synchronization in comparison with competition. Finally, although gender played an important role in the psychophysiological synchronization during cooperation (women presented the highest overlapping of GSR signals), feedback about the participants' performance was not significantly associated with their psychophysiological synchronization. Therefore, research in this field would help us to understand more about the body's physiological responses to different types of social interactions, such as cooperation and competition, providing an opportunity to establish interaction strategies that would be physiologically desirable.

Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver's awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.