Control Game Research Articles

Football referees issue more yellow cards following VAR interventions – mental, tactical, and performance considerations

VAR (video assistant referee) presents a human-technology interface that was designed to improve referees’ ability to make accurate decisions in key match incidents. VAR interventions are pivotal moments in football matches, challenging referees’ critical decisions and potentially affecting their perceived ability to manage the game. This study aimed to investigate how referees handle VAR interventions in terms of game management and control. Through statistical analyses, we examined 120 real-match VAR interventions in penalty incidents from the Israeli Premier League, focusing on the number of fouls and yellow cards before and after the VAR interventions. Our findings revealed a significant impact ( d = 0.40) on yellow cards but not on fouls. Remarkably, referees issued twice the number of yellow cards (normalized per playing minute) following a VAR intervention, irrespective of the time it occurred during the match (i.e., first or second half). These results are discussed within the framework of current research and theories concerning self-control and game management in football refereeing. The increased issuance of yellow cards following VAR interventions suggests that teams may be at a higher risk of receiving disciplinary actions, which can potentially impact their performance and increase the likelihood of losing matches. Consequently, we provide practical recommendations for referee training and match preparation as well as for teams.

Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models

Generative Adversarial Networks (GANs) are unsupervised models designed to learn and replicate a target distribution. The vanilla versions of these models can be extended to more controllable models. Conditional Generative Adversarial Networks (CGANs) extend vanilla GANs by conditioning both the generator and discriminator on some additional information (labels). Controllable models based on complementary learning, such as Rumi-GAN, have been introduced. Rumi-GANs leverage negative examples to enhance the generator's ability to learn positive examples. We evaluate the performance of two controllable GAN variants, CGAN and Rumi-GAN, in generating game levels targeting specific constraints of interest: playability and controllability. This evaluation is conducted under two scenarios: with and without the inclusion of negative examples. The goal is to determine whether incorporating negative examples helps the GAN models avoid generating undesirable outputs. Our findings highlight the strengths and weaknesses of each method in enforcing the generation of specific conditions when generating outputs based on given positive and negative examples.

Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions

The field of physically interactive electronic games is rapidly evolving, driven by the fact that it combines the benefits of physical activities and the attractiveness of electronic games, as well as advancements in sensor technologies. In this paper, a new game was introduced, which is a special version of Bubble Soccer, which we named Q-eBall. It creates a dynamic and engaging experience by combining simulation and physical interactions. Q-eBall is equipped with a fall detection system, which uses an embedded electronic circuit integrated with an accelerometer, a gyroscopic, and a pressure sensor. An evaluation of the performance of the fall detection system in Q-eBall is presented, exploring its technical details and showing its performance. The system captures the data of players’ movement in real-time and transmits it to the game controller, which can accurately identify when a player falls. The automated fall detection process enables the game to take the required actions, such as transferring possession of the visual ball or applying fouls, without the need for manual intervention. Offline experiments were conducted to assess the performance of four machine learning models, which were K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM), for falls detection. The results showed that the inclusion of pressure sensor data significantly improved the performance of all models, with the SVM and LSTM models reaching 100% on all metrics (accuracy, precision, recall, and F1-score). To validate the offline results, a real-time experiment was performed using the pre-trained SVM model, which successfully recorded all 150 falls without any false positives or false negatives. These findings prove the reliability and effectiveness of the Q-eBall fall detection system in real time.

Equilibrium Control in Uncertain Linear Quadratic Differential Games with V-Jumps and State Delays: A Case Study on Carbon Emission Reduction

Uncertainty, time delays, and jumps often coexist in dynamic game problems due to the complexity of the environment. To address such issues, we can utilize uncertain delay differential equations with jumps to depict the dynamic changes in differential game problems that involve uncertain noise, delays, and jumps. In this paper, we first examine a linear quadratic differential game optimistic value problem within an uncertain environment characterized by jumps and delays. By applying the Z(x,y) transform, we convert the infinite-dimensional problem into a finite-dimensional one. We then demonstrate that the condition for the existence of a Nash equilibrium strategy is equivalent to the existence of solutions to two cross-coupled matrix Riccati equations. Furthermore, we explore the saddle point equilibrium strategy of the linear quadratic differential game optimistic value model and derive the corresponding saddle point equilibrium solution. Finally, we apply our results to solve a carbon emission reduction game problem.

Event-triggered robust hierarchical control for uncertain multiplayer stackelberg games via adaptive dynamic programming

Event-triggered robust hierarchical control for uncertain multiplayer stackelberg games via adaptive dynamic programming

A Pilot Controlled Trial of Relaxation Training Combined with a Video Game Reinforcing Emotional Regulation to Improve Anger Management in Children and Adolescents.

Untreated anger and aggression in youth confer heightened risk for subsequent psychosocial problems. However, engaging youth in treatment for anger can be difficult given barriers to accessing care and high rates of attrition. This study examined whether learning relaxation skills and practicing them using a videogame, whose operation was contingent upon keeping heart rate close to baseline levels, could help children learn to manage anger and aggression. Youth ages 7-17 with elevated levels of anger (N = 39) were randomized to receive the active video game condition or a control game that displayed heart rate but did not stop the game if heart rate became elevated. Youth underwent baseline screening, 6 treatment sessions, and follow-up assessments at 2-weeks and 3-months. Compared to the control condition, children in the active condition demonstrated significantly greater improvements in clinician-rated aggression severity (d = 1.48) and youth-rated emotion dysregulation (d = 3.46) at 2-weeks post-treatment. The active group maintained these improvements at 3-month follow-up, but no longer significantly differed from the control group. Intervention effects were nonsignificant for parent-reported emotion dysregulation and aggression. In addition, the intervention group youth, but not control group, experienced increased time with heart rate below baseline over the course of the 6 sessions. Findings suggest a promising brief intervention for reducing aggression and emotion dysregulation in children while they are waiting for longer-term outpatient therapy. Clinical Trial Registration Number: NCT03270813.

ASIP Performance Enhancement by Hazard Control through Scoreboard

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%.

Consistory and Youth Surveillance in the Republic of Geneva: The Case of Games (16th–18th Centuries)

This article explores the theme of youth gaming in the Calvinist Republic of Geneva. This investigation is largely based on disciplinary cases found in the registers of the Consistory of Geneva. Legislative sources, and the registers of the Pastor’s Company and the Small Council, are also used herein. This study, deliberately placed at the crossroads of the history of games, childhood, and Calvinist-style ecclesiastical discipline, aims to shed light on the Consistory’s policy of controlling play activities, and to understand what was at stake. The author defends the idea that the surveillance and control of games played by children was intended to prevent them from straying from their religious education, but also to protect them from the excesses and dangers of abusive play and immoderate behavior (violence, lost money, insults, etc.).

The active foundations of the illusion of control: an experimental test of the Henslin effect

The mistaken belief that personal control can affect the outcomes of random events is seen as a core part of disordered gamblers’ irrational beliefs via the ‘illusion of control’. The present study tests the extent of action-based manifestation of the illusion of control in a dice-rolling game, which provides a novel controlled test of a claim first made by Henslin that craps gamblers tend to shake their dice harder when aiming for higher numbers. We also tested participants’ recent involvement in gambling games and rates of disordered gambling symptomology as two theoretically-informed potential moderators of any effect. An incentivized dice-rolling experiment was programmed for participants’ mobile devices, where device accelerometer data was used to animate the effects of their shaking and to record the dependent variable of shaking strength. 1,692 US-Based participants (Mean age 37.1; 60.7% male) completed 24 trials each, across which the target number that they would win a $1 bonus if rolled was varied from one to six. Participants rolled the dice 4.1% harder for the highest- (six) compared to lowest-number (one). However, the effect did not vary based on participants’ recent engagement with various gambling games, and also did not correlate with gamblers’ Problem Gambling Severity Index scores. These results uniquely demonstrate a small Henslin effect, but also challenge theoretical accounts that illusion of control effects should be higher in people with greater familiarity with a given situation (i.e. relevant gambling games), or who have higher levels of disordered gambling symptomatology.

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.

Game-Based Assessment of Peripheral Neuropathy Combining Sensor-Equipped Insoles, Video Games, and AI: Proof-of-Concept Study.

Detecting peripheral neuropathy (PNP) is crucial in preventing complications such as foot ulceration. Clinical examinations for PNP are infrequently provided to patients at high risk due to restrictions on facilities, care providers, or time. A gamified health assessment approach combining wearable sensors holds the potential to address these challenges and provide individuals with instantaneous feedback on their health status. We aimed to develop and evaluate an application that assesses PNP through video games controlled by pressure sensor-equipped insoles. In the proof-of-concept exploratory cohort study, a complete game-based framework that allowed the study participant to play 4 video games solely by modulating plantar pressure values was established in an outpatient clinic setting. Foot plantar pressures were measured by the sensor-equipped insole and transferred via Bluetooth to an Android tablet for game control in real time. Game results and sensor data were delivered to the study server for visualization and analysis. Each session lasted about 15 minutes. In total, 299 patients with diabetes mellitus and 30 with metabolic syndrome were tested using the game application. Patients' game performance was initially assessed by hypothesis-driven key capabilities that consisted of reaction time, sensation, skillfulness, balance, endurance, and muscle strength. Subsequently, specific game features were extracted from gaming data sets and compared with nerve conduction study findings, neuropathy symptoms, or disability scores. Multiple machine learning algorithms were applied to 70% (n=122) of acquired data to train predictive models for PNP, while the remaining data were held out for final model evaluation. Overall, clinically evident PNP was present in 247 of 329 (75.1%) participants, with 88 (26.7%) individuals showing asymmetric nerve deficits. In a subcohort (n=37) undergoing nerve conduction study as the gold standard, sensory and motor nerve conduction velocities and nerve amplitudes in lower extremities significantly correlated with 79 game features (|R|>0.4, highest R value +0.65; P<.001; adjusted R2=0.36). Within another subcohort (n=173) with normal cognition and matched covariates (age, sex, BMI, etc), hypothesis-driven key capabilities and specific game features were significantly correlated with the presence of PNP. Predictive models using selected game features achieved 76.1% (left) and 81.7% (right foot) accuracy for PNP detection. Multiclass models yielded an area under the receiver operating characteristic curve of 0.76 (left foot) and 0.72 (right foot) for assessing nerve damage patterns (small, large, or mixed nerve fiber damage). The game-based application presents a promising avenue for PNP screening and classification. Evaluation in expanded cohorts may iteratively optimize artificial intelligence model efficacy. The integration of engaging motivational elements and automated data interpretation will support acceptance as a telemedical application.

Collaborative Constrained Target-Reaching Control in a Multiplayer Reach-Avoid Game

Collaborative Constrained Target-Reaching Control in a Multiplayer Reach-Avoid Game

Psychological and physiological benefits of horticultural therapy for Chinese older adults.

Gardening as a leisure activity has been shown to be potentially beneficial for older adults, this study aimed to assess the physiological and psychological impacts of a specific horticultural activity, watering indoor plants, compared with playing a computer game - the Klondike Solitaire Card Game. This crossover study included 100 older adults recruited from a nursing care facility for older people in Shanghai, China. They were allocated to either engage in watering indoor plants (horticultural group) or playing a computer game (control group). For the inclusion criteria, screening for cognitive function was conducted to exclude those with severe cognitive impairments, thereby focusing on those capable of participating fully in the study activities. Brainwave activities, blood pressure, and psychological states were measured through pre- and post-test questionnaires to evaluate the physiological and psychological effects. The activity of watering indoor plants was associated with significant reductions in both systolic and diastolic blood pressure, in contrast to the computer game activity. Additionally, participants in the horticultural group showed increased brainwave mean values, indicating heightened relaxation and focus, while decreases were observed in the control group. The experimental group reported reduced anxiety scores, along with increased feelings of comfort and relaxation, and also exhibited significantly higher meditation levels, which indicates a profound mental relaxation effect. Our findings suggest that engaging in indoor horticultural activities, such as watering houseplants for 10minutes, can reduce mental and physical stress levels in older adults.

Cooperative control of multi-vehicle formation based on cooperative game

In this paper, an optimization decision-making method of vehicle fleet queue based on game theory is proposed. By adopting the upper and lower controller structure, the longitudinal motion stability problem of the vehicle fleet is solved. The upper controller utilizes a game control algorithm specially developed for this study. The game payoff function is constructed by considering real-time vehicle information and comprehensively evaluating factors such as expected distance, vehicle safety, and stability. The optimal expected acceleration of the vehicle is determined. Subsequently, the lower controller adjusts the engine throttle opening or brake pressure to regulate the vehicle operation according to the desired acceleration. Simulation results show that the designed game strategy can effectively reduce vehicle spacing and speed errors, thus significantly improving the stability as well as safety of fleet operation.

Machine Learning-Based Gesture Recognition Glove: Design and Implementation.

In the evolving field of human-computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor. To classify dynamic gestures, we developed a neural network-based classifier, utilizing a convolutional neural network (CNN) with three two-dimensional convolutional layers and rectified linear unit (ReLU) activation where its accuracy was 90%. The developed glove effectively captures dynamic gestures for game control, achieving high classification accuracy, precision, and recall, as evidenced by the confusion matrix and training metrics. Despite limitations in the number of gestures and participants, the solution offers a cost-effective and accurate approach to gesture recognition, with potential applications in VR/AR environments.

Neural network optimal control for tripartite UAV confrontation systems based on fuzzy differential game

The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation.

Examining effectiveness of a randomized controlled trial to enhance understanding of eye gaze cues in autism: Incorporating an active control game in SAGA

Examining effectiveness of a randomized controlled trial to enhance understanding of eye gaze cues in autism: Incorporating an active control game in SAGA

Exploring the user’s gaze during product evaluation through the semantic differential: a comparison between virtual reality and photorealistic images

Advanced product presentation methods can enhance the product evaluation experience both during the design process and online shopping, as static images often fail to convey essential product details. Virtual Reality (VR) technologies hold great potential in this regard, becoming increasingly accessible to all users. However, the influence of display mediums on emotional responses and product assessment needs further investigation, especially using physiological measures to obtain more objective insights. In this study, we investigate the influence of VR and photorealistic images on assessing and observing virtual prototypes of game controllers. The Semantic Differential technique was employed for product assessment, while built-in eye-tracking was used to measure participants’ viewing time on various areas of interest (AOIs). Our findings show that the medium significantly affects not only product evaluation and confidence in the response but also how the user observes it, with sensory-related features being particularly influenced. These findings hold practical implications for product design and vendors, as understanding the relationship between visualization mediums and product evaluation enhances the design process and improves consumer experiences.

Artificial intelligence enabled biodegradable all-textile sensor for smart monitoring and recognition

Artificial intelligence (AI) enabled electronic textiles inherit the advantages of traditional textiles, such as softness, flexibility, and wearable convenience, and demonstrate significant potential for wearable applications. However, the existence of metallic electrodes and polymer thin films sensitive/encapsulating layers in current textile- or fiber-based pressure sensors significantly reduces the unique advantages of textiles, particularly in terms of renewability and biodegradability. Here, an all-textile biodegradable AI enabled pressure sensor is demonstrated using tunable conductivity cotton as the electrode/sensitive layer, incorporating real-time deep learning-based data analysis. The metal electrode and polymer thin film widely adopted in smart textile-based pressure sensors are avoided, and the all-textile components allow the sensors to be freely cut and reassembled like Lego. Based on these Lego-like smart textiles, intelligence applications such as real-time health monitoring, game control, gait analysis, and authentication systems are demonstrated. After completing the functions, the whole device can be rapidly degraded in the cellulase solution and broken down into reducing sugars, thus effectively reducing the environmental pollution. This work provides a new strategy for creating renewable and sustainable textile pressure sensors with significant application potential in next-generation smart green electronics.

Dynamic event-driven optimal consensus control for state-constrained multiagent zero-sum differential graphical games

In this paper, a dynamic event-driven optimal control scheme is proposed for the zero-sum differential graphical games in nonlinear multiagent systems with full-state constraints. Initially, to address the dual demands of optimality and state constraints, a set of system transformation functions are introduced to satisfy the state constraints of the agents. Then, by applying the principle of differential game theory, the distributed optimal control problem affected by external disturbances is formulated as a zero-sum differential graphical game, and the performance index function related to neighbor informations and disturbances is designed for each follower. Afterwards, to enhance the utilization of communication resource, a novel dynamic event-triggered mechanism characterized by a dynamic threshold parameter and an auxiliary dynamic variable is developed, which not only exhibits greater flexibility but also diminishes the frequency of triggers. Furthermore, the approximate optimal control strategies are obtained by employing an event-driven adaptive dynamic programming algorithm. Ultimately, a simulation example is presented to verify the applicability of the proposed control approach.