This paper focuses on the automatic classification of self-assessed personality traits from the HEXACO inventory during crowded mingle scenarios. These scenarios provide rich study cases for social behavior analysis but are also challenging to analyze …
Infants receiving care in the Neonatal Intensive Care Unit (NICU) experience several painful procedures during their hospitalization. Assessing neonatal pain is difficult because the current standard for assessment is subjective, inconsistent, and disc…
Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Partic…
Patient pain can be detected highly reliably from facial expressions using a set of facial muscle-based action units (AUs) defined by the Facial Action Coding System (FACS). A key characteristic of facial expression of pain is the simultaneous occurren…
A growing body of literature has emerged that demonstrates the potential of neurogaming platforms for inter- facing with well-known video games. With the recent convergence of advances in consumer electronics, ubiquitous computing, and wearable sensor …
Location-based social networks (LBSNs) add geographical information into traditional social networks and link people’s virtual and physical lives. As an important application of LBSNs, point-of-interest (POI) suggestion has become an important m…
Apparent personality and emotion analysis are both central to affective computing. Existing works solve them individually. In this paper we investigate if such high-level affect traits and their relationship can be jointly learned from face images in t…
Horse-rider interaction analysis by wearable sensors is a promising tool for monitoring equestrian training. In this paper, a body sensor network (BSN) based equestrian motion analysis system is developed, which combines bespoke inertial measurement un…
Pain is a complex phenomenon, involving sensory and emotional experience, that is often poorly understood, especially in infants, anesthetized patients, and others who cannot speak. Technology supporting pain assessment has the potential to help reduce…
Anxiety is a complex emotional state that has a great impact on people’s physical and mental health. Effectively identifying different anxiety states is very important. By inducing various anxiety states of 12 healthy college students with electroencephalograph (EEG) recording, comprehensive EEG features, including not only commonly used frequency domain features but also the time domain, statistical and nonlinear features were extracted from different EEG bands and brain locations. Next, correlation analysis was performed between various features and anxiety level changes that were predetermined at each stage of the experiment using a 5-point Likert scale, and the most relevant features were collected. Then, different classifiers were applied to classify four anxiety levels using different features alone or together to explore their anxiety recognition ability. Based on our dataset, the highest accuracy of identifying four anxiety states reached approximately 62.56 percent using the Support Vector Machine (SVM), which improved the classification accuracy compared with previous studies. The results also revealed the importance of EEG linear features (especially for features including total power, mean square and variance) in anxiety recognition. Furthermore, it suggested that EEG features in the beta band and the frontal lobe contributed to anxiety recognition more than the features in the other bands or other brain locations. In short, this study improves the accuracy of multi-level anxiety recognition and helps in choosing better features for anxiety recognition, which lay the foundation for the detection of continuous anxiety changes.