This paper proposes a protocol for the acquisition and processing of biophysical signals in virtual reality applications, particularly in phobia therapy experiments. This protocol aims to ensure that the measurement and processing phases are performed effectively, to obtain clean data that can be used to estimate the users’ anxiety levels. The protocol has been designed after analyzing the experimental data of seven subjects who have been exposed to heights in a virtual reality environment. The subjects’ level of anxiety has been estimated based on the real-time evaluation of a nonlinear function that has as parameters various features extracted from the biophysical signals. The highest classification accuracy was obtained using a combination of seven heart rate and electrodermal activity features in the time domain and frequency domain.

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants’ ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms—Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks—accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms’ classification scores.

This paper presented the assessment of cognitive load (as an effective real-time index of task difficulty) and the level of brain activation during an experiment in which eight visually impaired subjects performed two types of tasks while using the white cane and the Sound of Vision assistive device with three types of sensory input—audio, haptic, and multimodal (audio and haptic simultaneously). The first task was to identify object properties and the second to navigate and avoid obstacles in both the virtual environment and real-world settings. The results showed that the haptic stimuli were less intuitive than the audio ones and that the navigation with the Sound of Vision device increased cognitive load and working memory. Visual cortex asymmetry was lower in the case of multimodal stimulation than in the case of separate stimulation (audio or haptic). There was no correlation between visual cortical activity and the number of collisions during navigation, regardless of the type of navigation or sensory input. The visual cortex was activated when using the device, but only for the late-blind users. For all the subjects, the navigation with the Sound of Vision device induced a low negative valence, in contrast with the white cane navigation.

Much of the costs and dangers of exposure therapy in phobia treatment can be removed through virtual reality (VR). Exposing people to heights, for instance, might sound easy, but it still involves time and money investments to reach a tall building, mountain or bridge. People suffering from milder forms of acrophobia might not even be treated at all, the cost not being worth it. This paper presents a prototype that allows exposure therapy to be done in a controlled environment, in a more comfortable, quick and cheaper way. By applying acrophobia questionnaires, collecting biophysical data and developing a virtual reality game, we can expose volunteers to heights and analyze if there is any change in their fear and anxiety levels. This way, regardless of the initial anxiety level and phobia severity, we can check if there is any post-therapy improvement and verify if virtual reality is a viable alternative to real-world exposure.

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