Next-Point Prediction for Direct Touch Using Finite-Time Derivative Estimation

International audienceEnd-to-end latency in interactive systems is detrimental to performance and usability, and comes from a combination of hardware and software delays. While these delays are steadily addressed by hardware and software improvements, it is at a decelerating pace. In parallel, short-term input prediction has shown promising results in recent years, in both research and industry, as an addition to these efforts. We describe a new prediction algorithm for direct touch devices based on (i) a state-of-the-art finite-time derivative estimator, (ii) a smoothing mechanism based on input speed, and (iii) a post-filtering of the prediction in two steps. Using both a pre-existing dataset of touch input as benchmark, and subjective data from a new user study, we show that this new predictor outperforms the predictors currently available in the literature and industry, based on metrics that model user-defined negative side-effects caused by input prediction. In particular, we show that our predictor can predict up to 2 or 3 times further than existing techniques with minimal negative side-effects

Effects of Local Latency on Games

Video games are a major type of entertainment for millions of people, and feature a wide variety genres. Many genres of video games require quick reactions, and in these games it is critical for player performance and player experience that the game is responsive. One of the major contributing factors that can make games less responsive is local latency — the total delay between input and a resulting change to the screen. Local latency is produced by a combination of delays from input devices, software processing, and displays. Due to latency, game companies spend considerable time and money play-testing their games to ensure the game is both responsive and that the in-game difficulty is reasonable. Past studies have made it clear that local latency negatively affects both player performance and experience, but there is still little knowledge about local latency’s exact effects on games. In this thesis, we address this problem by providing game designers with more knowledge about local latency’s effects. First, we performed a study to examine latency’s effects on performance and experience for popular pointing input devices used with games. Our results show significant differences between devices based on the task and the amount of latency. We then provide design guidelines based on our findings. Second, we performed a study to understand latency’s effects on ‘atoms’ of interaction in games. The study varied both latency and game speed, and found game speed to affect a task’s sensitivity to latency. Third, we used our findings to build a model to help designers quickly identify latency-sensitive game atoms, thus saving time during play-testing. We built and validated a model that predicts errors rates in a game atom based on latency and game speed. Our work helps game designers by providing new insight into latency’s varied effects and by modelling and predicting those effect

Increasing Player Performance and Game Experience in High Latency Systems

Cloud gaming services and remote play offer a wide range of advantages but can inherent a considerable delay between input and action also known as latency. Previous work indicates that deep learning algorithms such as artificial neural networks (ANN) are able to compensate for latency. As high latency in video games significantly reduces player performance and game experience, this work investigates if latency can be compensated using ANNs within a live first-person action game. We developed a 3D video game and coupled it with the prediction of an ANN. We trained our network on data of 24 participants who played the game in a first study. We evaluated our system in a second user study with 96 participants. To simulate latency in cloud game streaming services, we added 180 ms latency to the game by buffering user inputs. In the study we predicted latency values of 60 ms, 120 ms and 180 ms. Our results show that players achieve significantly higher scores, substantially more hits per shot and associate the game significantly stronger with a positive affect when supported by our ANN. This work illustrates that high latency systems, such as game streaming services, benefit from utilizing a predictive system

Small Latency Variations Do Not Affect Player Performance in First-Person Shooters

In interactive systems high latency affects user performance and experience. This is especially problematic in video games. A large number of studies on this topic investigated the effects of constant, high latency. However, in practice, latency is never constant but varies by up to 100 ms due to variations in processing time and delays added by polling between system components. In a large majority of studies, these variations in latency are neither controlled for nor reported. Thus, it is unclear to which degree small, continuous variations in latency affect user performance. If these unreported variations had a significant impact, this might cast into doubt the findings of some studies. To investigate how latency variation affects player performance and experience in games, we conducted an experiment with 28 participants playing a first-person shooter. Participants played with two levels of base latency (50 ms vs. 150 ms) and variation (0 ms vs. 50 ms). As expected, high base latency significantly reduces player performance and experience. However, we found strong evidence that small variations in latency in the order of 50 ms, do not affect player performance significantly. Thus, our findings mitigate concerns that previous latency studies might have systematically ignored a confounding effect

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens