Ritam Ghosh

Nibraas Khan, Mahrukh Tauseef, Ritam Ghosh et al.

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.

Alexandra Watkins, Ritam Ghosh, Evan Chow et al.

We present a proof-of-concept palm-mounted thermal feedback prototype addressing thermal rendering challenges specific to augmented reality (AR), where users must interact with both real and virtual objects in their physical workspace. In contrast to thermal feedback systems developed for virtual reality, AR thermal feedback must preserve manual dexterity, maintain access to real-world thermal cues, and provide coherent virtual temperature sensations without obstructing natural object interaction. We propose three AR-specific design considerations, which our prototype implements: indirect feedback to preserve fingertip dexterity, active thermal passthrough to sense and render the temperature of contacted physical surfaces, and spatially and temporally varying thermal rendering across the palm. Human-subject experiments evaluated perceptual sensitivity, indirect feedback, active thermal passthrough, spatial pattern recognition, and moving thermal rendering during AR interaction. Results showed that although indirect feedback reduced perceived realism during visual contact at the fingertips, it did not reduce immersion or comfort; active thermal passthrough supported temperature discrimination between real and rendered surfaces; and spatiotemporal rendering significantly improved immersion and realism compared with static thermal stimulation. These findings suggest that our design considerations are viable design strategies for AR thermal haptics, while also clarifying tradeoffs for applications that require precise realism versus broader immersive thermal experience.

Ritam Ghosh

Affective states regulate our day to day to function and has a tremendous effect on mental and physical health. Detection of affective states is of utmost importance for mental health monitoring, smart entertainment selection and dynamic workload management. In this paper, we discussed relevant literature on affective state detection using physiology data, the benefits and limitations of different sensors and methods used for collecting physiology data, and our rationale for selecting functional near-infrared spectroscopy. We present the design of an experiment involving nine subjects to evoke the affective states of meditation, amusement and cognitive load and the results of the attempt to classify using machine learning. A mean accuracy of 83.04% was achieved in three class classification with an individual model; 84.39% accuracy was achieved for a group model and 60.57% accuracy was achieved for subject independent model using leave one out cross validation. It was found that prediction accuracy for cognitive load was higher (evoked using a pen and paper task) than the other two classes (evoked using computer bases tasks). To verify that this discrepancy was not due to motor skills involved in the pen and paper task, a second experiment was conducted using four participants and the results of that experiment has also been presented in the paper.