Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Abstract

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.

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

“…They estimated the long term excitement of the participant to trigger the dynamic difficulty adjustment and found a correlation between excitement patterns and game events. In the literature, machine learning and evolutionary algorithms are used for clustering various gaming events [102], design new levels [103], difficulty adjustment [101,104], modeling user experience [97,105], and feedback to personalized game elements [106]. Despite these advancements, the investigation in modeling and estimating user experience for the improvement of the HCI system is still in its preliminary stages.…”

Design of Data Collection and Analysis Method for a Pleasant and Safe User Experience of Personal Mobility Device

“…Manually removing non-linear segments of the time series can result in the introduction of bias into the analysis. Therefore, given the stochastic nature and variability of psychophysiological data, many of the methods used in studies are under the umbrella of machine learning [8]. One of the most essential advantages Random Forest has is its versatility, capable of running both regression and classification tasks.…”

“…The stress measurement device is assembled using Seeeduino V4.2 as the core microcontroller, which has been in the context of measuring gaming experience producing reliable results [8]. It consists of 8 components, including a 6x AA battery holder to power, the sensors, the shields (Base Shield V2, SD Card Shield V4.0) and a GPS module that allows time syncing of data with the smartphone (Figure 1).…”

Psychophysiological and Psychosemantic Aspects of Teaching Stress-resistant Behavior Law Enforcement Officers

“…Nebylitsin [2], and foreign authors. For example, S. Baldwin, C. Bennell, J.P. Andersen, T. Semple, and B. Jenkins [6] studied changes in physiological responses under stress; M. Čertický, M. Čertický, P. Sinčák, G. Magyar, J. Vaščák, F. Cavallo [7] have established that psychophysiological reactions can indicate pleasure; K. van Hedger, E.A. Necka, A.K.…”