Modeling user experience in electronic entertainment using psychophysiological measurements

Analyses of user experience in electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems - mainly the subject bias and interpretation difficulties - and therefore should not be used as a sole metric. To deal with this problem, we propose a possibility of creating a model of consumer experience based on psychophysiological measurements and describe how such model can be trained using machine learning methods. Models trained exclusively on real-time data produced by autonomic nervous system and involuntary physiological responses is not susceptible to subjective bias, misinterpretation and imprecision caused by the delay between the experience and the interview. This paper proposes a potentially promising direction for future research and presents an introductory analysis of available biological data sources, their relevance to user experience modeling and technical prerequisites for their collection. Multiple psychophysiological measurements (such as heart rate, electrodermal activity or respiratory activity) should be used in combination with self-reporting methods to prepare training sets for machine learning models. During our initial experiments, we collected time-series heart rate data for two computer games - Hearthstone and Dota 2. This preliminary analysis suggests the existence of a correlation between psychophysiological measurements and in-game events. Actual ready-to-use user experience models are out of the scope of this paper

Testing demand responsive shared transport services via agent-based simulations

Demand Responsive Shared Transport DRST services take advantage of Information and Communication Technologies ICT, to provide on demand transport services booking in real time a ride on a shared vehicle. In this paper, an agent-based model ABM is presented to test different the feasibility of different service configurations in a real context. First results show the impact of route choice strategy on the system performance

Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers

Large-scale online ridesharing: the effect of assignment optimality on system performance

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57% compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20% reduction in vehicle distance driven and 5% lower average passenger travel delay compared to a system that uses insertion heuristic.</p