Virtual Reality (VR) video games have become popular in recent years with the emergence of a wide variety of enhanced VR hardware systems. Typically, current VR video games incorporate high-quality auditory and video feedback along with vibro-tactile cues to provide an immersive experience. However, the ongoing VR hardware and software do not consider the state of the user to determine whether the video game is generating an enjoyable experience, or if it has become a stressing or boring experience. This work provides an assessment of stress level estimation from features extracted from Electrocardiography (ECG), Electrodermal Activity (EDA), and Electromyography (EMG) signals of users while playing a VR video game with different difficulty levels. Statistical differences were found between the rest and gaming stages for several extracted features. Regardless of the fact that no significant statistical differences between the three levels of difficulty were found by analyzing the EDA and ECG features, an 83.1% accuracy was obtained for the classification between the three levels with a KNN model. For the EMG signal, the obtained accuracy ranged between 99% and 100% for the distinction between a difficult level 1 and rest stage for all models. When all features from the ECG, EDA and EMG signals were used, an accuracy of 99% was obtained for the differentiation between the three difficulty levels and a resting stage. The presented work results may serve as a reference for future work regarding feature extraction and game difficulty adaptation for user experience enhancement.

Virtual reality (VR) is continuing to grow as more affordable technological devices become available. Video games are one of the most profitable applications, while rehabilitation has the most significant social impact. Both applications require a proper user evaluation to provide personalized experiences that avoid boring or stressful situations. Despite the successful applications, there are several opportunities to improve the field of human–machine interactions, one of the most popular ones being the use of affect detection to create personalized experiences. In that sense, this study presents the implementation of two dynamic difficulty adaptation strategies. The person’s affective state is estimated through a machine learning classification model, which later serves to adapt the difficulty of the video game online. The results show that it is possible to maintain the user at a given difficulty level, which is analogous to achieving the well-known flow state. Among the two implemented strategies, no statistical differences were found in the workload induced by the users. However, more physical demands and a higher frustration were induced by one of the strategies, validated with the recorded muscular activity. The results obtained contribute to the state of the art of DDA strategies in virtual reality driven by affective data.

This work is concerned with the modelling and control of the state of presence of a user in a virtual reality system (VRS). The manner how the state of a user interacting with the VRS is measured by some of its physiological signals is discussed. Based on the user-VRS interaction, a novel feedback control schema to manipulate the user state is proposed. In this, the error between the required and the actual user behavior is used by a controller to select the appropriate stimulus that must be executed by the VRS core scenario to reduce the error. The schema includes the modelling of the presence state by a discrete event strategy based on Petri Nets. Based on the obtained model, the control strategy to select the appropriate stimuli is devised. Finally, some real experiments with a VRS prototype are carried out to show the effectiveness of the proposal.

A critical aspect of virtual reality is the extent to which the user forgets their real surroundings and becomes completely engaged within the virtual environment. Diverse factors affect this user perception, which are grouped into two main concepts: immersion and presence. Although the study of presence is extensive, researchers have not reached a consensus on a protocol with specific instruments and stages to evaluate it. This leads to a wide variety of results with different assessment methods, experimental setups, stimuli implemented, and applications. Therefore, this article aims to provide an analysis of the state-of-the-art methods for assessing presence in VR systems during the last few years. This study seeks to determine and improve the understanding of current techniques used for presence assessment, human data collected, data analysis methods, and the technologies and virtual environments implemented. In addition, four opportunities are discussed to provide researchers guidelines that can lead to enhanced presence assessments and personalized VR experiences.

Berry production is increasing worldwide each year; however, high production leads to labor shortages and an increase in wasted fruit during harvest seasons. This problem opened new research opportunities in computer vision as one main challenge to address is the uncontrolled light conditions in greenhouses and open fields. The high light variations between zones can lead to underexposure of the regions of interest, making it difficult to classify between vegetation, ripe, and unripe blackberries due to their black color. Therefore, the aim of this work is to automate the process of classifying the ripeness stages of blackberries in normal and low-light conditions by exploring the use of image fusion methods to improve the quality of the input image before the inference process. The proposed algorithm adds information from three sources: visible, an improved version of the visible, and a sensor that captures images in the near-infrared spectra, obtaining a mean F1 score of $0.909\pm 0.074$ and $0.962\pm 0.028$ in underexposed images, without and with model fine-tuning, respectively, which in some cases is an increase of up to 12% in the classification rates. Furthermore, the analysis of the fusion metrics showed that the method could be used in outdoor images to enhance their quality; the weighted fusion helps to improve only underexposed vegetation, improving the contrast of objects in the image without significant changes in saturation and colorfulness.