With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. The present study evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. Data were collected from 15 participants and the spatio-spectral EEG features were analyzed with respect to task performance. The results indicate that scalp measurements of electrical activity can effectively discriminate three workload levels, even after suppression of a co-varying high-frequency activity.

Surgical planners are used to achieve the optimal outcome for surgery. They are especially desired in procedures where a positive aesthetic outcome is the primary goal, such as the Nuss procedure which is a minimally invasive surgery for correcting pectus excavatum (PE) -a congenital chest wall deformity which is characterized by a deep depression of the sternum. The Nuss procedure consists of placement of a metal bar(s) underneath the sternum, thereby forcibly changing the geometry of the ribcage. Because of the prevalence of PE and the popularity of the Nuss procedure, the demand to perform this surgery is greater than ever. Therefore, a Nuss procedure surgical planner is an invaluable planning tool ensuring an optimal physiological and aesthetic outcome. We propose the development and validation of the Nuss procedure planner. First, a generic model of the ribcage is developed. Then, the computed tomography (CT) data collected from actual patients with PE is used to create a set of patient-specific finite element models (FEM). Based on finite element analyses (FEA) a force-displacement data set is created. This data is used to train an artificial neural network (ANN) to generalize the data set. In order to evaluate the planning process, a methodology which uses an average shape of the chest for comparison with results of the Nuss procedure planner is developed. Haptic feedback and inertial tracking is also implemented. The results show that it is possible to utilize this approximation of the force-displacement model for a Nuss procedure planner and trainer.

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The research describes the extension of the Axiomatic Design model to incorporate the aesthetic design as the customer requirement. It also proposes a computational model to support the formalization of aesthetic design in industrial products. The methodology takes into account the cognition process during the design generation and captures this behavior in a group theoretic structure. This approach leads to application of Axiomatic Design paradigm to the domain of the aesthetics. The proposed framework is implemented and validated by taking a design case of the consumer products.

In this paper we propose a different approach for interacting and analyzing agent-based models. The approach relies on creating empathy and understanding between physical agents in the physical world (people) and artificial agents in the simulated world (simulated agents). We propose a simulated empathy framework (SEF) in which artificial agents communicate directly with physical agents through verbal channels and social media. We argue that artificial agents should focus on the communication aspects between these two worlds, the ability to tell their story in a compelling way, and to read between the lines of physical agents speech. We present an implementation of the SEF and discuss challenges associated with implementing the framework in an artificial society.