3 research outputs found
A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing
Emotions are an essential part of human behavior that can impact thinking,
decision-making, and communication skills. Thus, the ability to accurately
monitor and identify emotions can be useful in many human-centered applications
such as behavioral training, tracking emotional well-being, and development of
human-computer interfaces. The correlation between patterns in physiological
data and affective states has allowed for the utilization of deep learning
techniques which can accurately detect the affective states of a person.
However, the generalisability of existing models is often limited by the
subject-dependent noise in the physiological data due to variations in a
subject's reactions to stimuli. Hence, we propose a novel cost function that
employs Optimal Transport Theory, specifically Wasserstein Distance, to scale
the importance of subject-dependent data such that higher importance is
assigned to patterns in data that are common across all participants while
decreasing the importance of patterns that result from subject-dependent noise.
The performance of the proposed cost function is demonstrated through an
autoencoder with a multi-class classifier attached to the latent space and
trained simultaneously to detect different affective states. An autoencoder
with a state-of-the-art loss function i.e., Mean Squared Error, is used as a
baseline for comparison with our model across four different commonly used
datasets. Centroid and minimum distance between different classes are used as a
metrics to indicate the separation between different classes in the latent
space. An average increase of 14.75% and 17.75% (from benchmark to proposed
loss function) was found for minimum and centroid euclidean distance
respectively over all datasets.Comment: 9 page
Semi-Supervised Behavior Labeling Using Multimodal Data during Virtual Teamwork-Based Collaborative Activities
Adaptive human–computer systems require the recognition of human behavior states to provide real-time feedback to scaffold skill learning. These systems are being researched extensively for intervention and training in individuals with autism spectrum disorder (ASD). Autistic individuals are prone to social communication and behavioral differences that contribute to their high rate of unemployment. Teamwork training, which is beneficial for all people, can be a pivotal step in securing employment for these individuals. To broaden the reach of the training, virtual reality is a good option. However, adaptive virtual reality systems require real-time detection of behavior. Manual labeling of data is time-consuming and resource-intensive, making automated data annotation essential. In this paper, we propose a semi-supervised machine learning method to supplement manual data labeling of multimodal data in a collaborative virtual environment (CVE) used to train teamwork skills. With as little as 2.5% of the data manually labeled, the proposed semi-supervised learning model predicted labels for the remaining unlabeled data with an average accuracy of 81.3%, validating the use of semi-supervised learning to predict human behavior