Reid SN, et al. Nutr Res Pract. 2018 Jun;12(3):199-207. https://doi.org/10.4162/nrp.2018.12.3.199

Background: The World Health Organization announced the inclusion of gaming disorder (GD) in the International Classification of Diseases, 11th Revision, despite some concerns. However, video gaming has been associated with the enhancement of cognitive function. Moreover, despite comparable extensive video gaming, pro gamers have not shown any of the negative symptoms that individuals with GD have reported. It is important to understand the association between extensive video gaming and alterations in brain regions more objectively. Objective: This study aimed to systematically explore the association between extensive video gaming and changes in cognitive function by focusing on pro gamers and individuals with GD. Methods: Studies about pro gamers and individuals with GD were searched for in the PubMed and Web of Science databases using relevant search terms, for example, “pro-gamers” and “(Internet) gaming disorder.” While studies for pro gamers were searched for without date restrictions, only studies published since 2013 about individuals with GD were included in search results. Article selection was conducted by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: By following the PRISMA guidelines, 1903 records with unique titles were identified. Through the screening process of titles and abstracts, 86 full-text articles were accessed to determine their eligibility. A total of 18 studies were included in this systematic review. Among the included 18 studies, six studies included pro gamers as participants, one study included both pro gamers and individuals with GD, and 11 studies included individuals with GD. Pro gamers showed structural and functional alterations in brain regions (eg, the left cingulate cortex, the insula subregions, and the prefrontal regions). Cognitive function (eg, attention and sensorimotor function) and cognitive control improved in pro gamers. Individuals with GD showed structural and functional alterations in brain regions (eg, the striatum, the orbitofrontal cortex, and the amygdala) that were associated with impaired cognitive control and higher levels of craving video game playing. They also showed increased cortical thickness in the middle temporal cortex, which indicated the acquisition of better skills. Moreover, it was suggested that various factors (eg, gaming expertise, duration or severity of GD, and level of self-control) seemed to modulate the association of extensive video game playing with changes in cognitive function. Conclusions: Although a limited number of studies were identified that included pro gamers and/or individuals who reported showing symptoms of GD for more than 1 year, this review contributed to the objective understanding of the association between extensive video game playing and changes in cognitive function. Conducting studies with a longitudinal design or with various comparison groups in the future would be helpful in deepening the understanding of this association.

Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual information tailored to the current situation. To formulate this as a machine-learning problem, we defined 18 different everyday walking styles. Noting that walking strategies significantly affect the spatiotemporal features of hand motions, e.g., the speed and intensity of the swinging arm, we propose a smartwatch-based wearable system that can recognize these predefined walking styles. We developed a wearable system, suitable for use with a commercial smartwatch, that can capture hand motions in the form of multivariate timeseries (MTS) signals. Then, we employed a set of machine learning algorithms, including feature-based and recent deep learning algorithms, to learn the MTS data in a supervised fashion. Experimental results demonstrated that, with recent deep learning algorithms, the proposed approach successfully recognized a variety of walking patterns, using the smartwatch measurements. We analyzed the results with recent attention-based recurrent neural networks to understand the relative contributions of the MTS signals in the classification process.

People who exercise may benefit or be injured depending on their foot striking (FS) style. In this study, we propose an intelligent system that can recognize subtle differences in FS patterns while walking and running using measurements from a wearable smartwatch device. Although such patterns could be directly measured utilizing pressure distribution of feet while striking on the ground, we instead focused on analyzing hand movements by assuming that striking patterns consequently affect temporal movements of the whole body. The advantage of the proposed approach is that FS patterns can be estimated in a portable and less invasive manner. To this end, first, we developed a wearable system for measuring inertial movements of hands and then conducted an experiment where participants were asked to walk and run while wearing a smartwatch. Second, we trained and tested the captured multivariate time series signals in supervised learning settings. The experimental results obtained demonstrated high and robust classification performances (weighted-average F1 score > 90%) when recent deep neural network models, such as 1D-CNN and GRUs, were employed. We conclude this study with a discussion of potential future work and applications that increase benefits while walking and running properly using the proposed approach.

In this paper, we present an intelligent system that is capable of estimating the status of a player engaging in winter activities based on the sequence analysis of multivariate time-series sensor signals. Among the winter activities, this paper mainly focuses on downhill winter sports such as alpine skiing and snowboarding. Assuming that the mechanical vibrations generated by physical interaction between the ground surface and ski/snowboard in motion can describe the ground conditions and playing contexts, we utilize inertial and vibration signals to categorize the motion context. For example, the proposed system estimates whether the player is sitting on a ski lift or standing on the escalator, or skiing on wet or snowy ground, etc. To measure the movement of a player during a game or on the move, we develop a custom embedded system comprising a motion sensor and piezo transducer. The captured multivariate sequence signals are then trained in a supervised fashion. We adopt artificial neural network approaches (e.g., 1D convolutional neural network, and gated recurrent neural networks, such as long short-term memory and gated recurrent units). The experimental results validate the feasibility of the proposed approach.

To accurately perform a goal-directed movement in continuously changing environments, it is unavoidable for individuals to adapt accordingly. The cerebellum has been known to be responsible for such process, specifically adaptation using sensorimotor information. As shown in previous studies, using HMD-VR technology in an experimental setting has similar advantages as in the real-world environment: researchers can manipulate the experimental environment, precisely control the experiments, and quantitatively analyze errors in real time. Moreover, the HMD-VR environment provides high immersiveness and embodiment which even enhance motor learning and increase engagement and motivation of individuals more than real-world environments do. In our HMD-VR-based task, the subjects were trained to adapt to a condition in which the visual information was artificially 20°clockwise rotated from the actual cursor movement. The subjects used a virtual reality tracker to move the cursor from a starting point to a target that appeared randomly at five locations, 20 cm from the starting point with an interval of 15°. Although no significant side effects were expected from experiencing the HMD-VR environment, we considered the appropriate number of trials for patients with cerebellar disease for future use in clinical settings. To examine the feasibility of our task for analysis of visuomotor adaptation pattern as shown in a real-world-based task, we created and compared two paradigms with a difference in the number of trials. As we expected, the results showed that the heading angle error decreased as the participants of both paradigms continued the task and that there was no significant difference between the two paradigms. Next, we applied our short task paradigm to patients diagnosed with cerebellar ataxia and age-matched controls for further examination of applicability to diagnosis and rehabilitation of the patients. As a result, we observed the distinguishable adaptation pattern of the patient group by using our paradigm. Overall, the results suggest that our paradigm is feasible to analyze the visuomotor adaptation pattern of healthy individuals and patients with cerebellar ataxia so may contribute to the clinical field.