In Fitts’ law research for pointing and selecting tasks, the multi-directional tapping task from ISO/TS 9241-411 has been widely used as a standard task. However, the ISO standard does not describe several task factors in sufficient detail, leading researchers to interpret and apply them independently in various ways. This study aims to investigate the effects of four task factors for multi-directional tapping task: error highlight, hover highlight, target shape, and number of targets. The experimental results indicated that all investigated task factors had significant effects on user performance and behavior, as reflected in both eye movement and mouse movement patterns. Our research findings can be helpful for researchers who want to evaluate the efficiency and effectiveness of interaction techniques and input devices for graphical user interfaces as a multi-directional tapping task protocol.

Virtual Reality (VR) gaming has surged rapidly in the gaming industry, offering new interactive and immersive experiences beyond traditional platforms. However, in addition to its vast potential, VR may also introduce new risks for players. This study aims to synthesize existing research on immersive VR games for entertainment and their associated ergonomic risks. A comprehensive search of three major databases yielded 2,430 articles, of which 51 were selected for final analysis. Results showed that existing studies primarily assess the ergonomic risks concerning game content and user characteristics. Despite varied methodologies, studies consistently report the presence of ergonomic risks in five main categories: simulator sickness, visual fatigue, postural imbalance, workload, and physical discomfort, fatigue, and exertion. Further examination of differences in findings highlights potential influences of study design, offering valuable insights and recommendation for future research on ergonomic risks in VR gaming.

The multi-directional tapping task has long served as a foundational tool for evaluating pointing performance in human-computer interaction research. However, its transition from 2D interfaces to virtual reality (VR) raises challenges, especially in standardizing target depth. This study explores how target depth influences performance in VR, focusing on two common techniques: Raycasting and Virtual Hand. We conducted two controlled experiments (each with$\mathrm{n}=20$) to isolate depth effects. In Experiment 1, fixed target size led to visual angle (VA) shifts across depths, affecting performance. Both techniques performed best when VA was between$1-4^{\circ}$; Raycasting peaked at 2 m, Virtual Hand at$0.4-0.5 ~\mathrm{m}$. In Experiment 2, we controlled VA to isolate depth itself. Raycasting remained stable beyond 2 m but degraded at close range due to biomechanical limits. Virtual Hand remained sensitive to depth despite fixed VA, but differences were smaller, with throughput unaffected. These results suggest VA should be the primary parameter for standardizing the task in VR. Depth-specific evaluation remains necessary, except for Raycasting beyond 2 m. We provide depth-aware guidelines to improve standardization and comparability while aligning with ISO protocols.

Obstacles are a common cause of slips, trips, and falls, but training for obstacle negotiation can be risky. VR offers a safer alternative, but potential differences in human perception between physical and virtual environments may affect training effectiveness. This study examines how people perceive obstacle size and crossing difficulty in both physical and virtual environments. Thirty young adults evaluated 25 obstacle variations (5 heights × 5 depths). Results showed that participants perceived virtual obstacles as smaller and easier to cross than real ones. Importantly, participants estimated obstacle height and depth on a ratio scale in both environments according to Stevens’ power law (power coefficients: 0.94–1.10). These findings highlight the need to consider perceptual differences when designing VR environments for training purposes. Nevertheless, the overall perception trends and excellent absolute agreement suggest a high similarity between physical and virtual environments, supporting the potential of VR for safe, effective fall prevention training.

High-resolution (HR) satellite imagery over long time series is crucial for monitoring fine-scale land use and land cover changes. However, existing freely available Earth observation data are limited to medium to coarse resolutions or short time spans. Our goal is to reconstruct 10-m resolution Sentinel-2-like imagery from 1985 to 2015 using Landsat imagery. Unlike previous studies that heavily rely on HR reference images from the same period, we utilize the overlapping Landsat and Sentinel-2 observations to reconstruct 10-m Sentinel-2-like imagery from 1985 to 2015. Specifically, we propose the STARS-Net, which can effectively transfer multiple similar textures from Sentinel-2 to Landsat images, ensuring accurate reconstruction even with significant land changes. The innovation of our method lies in its ability to integrate any number of HR images, without limiting these images to similar times or the same locations, leading to more flexible and accurate reconstructions. As a result, a 10-m resolution dataset of five major China cities from 1985 to 2015 is available athttps://figshare.com/s/186e9b25426067e0dcf4with a five-year interval. Extensive comparative experiments demonstrate that STARS-Net outperforms state-of-the-art super-resolution (SR) methods and the results highlight that STARS-Net exhibits strong robustness in various scenarios, including cloud cover, image missing, and land cover changes. More importantly, our method produces the first historical image retrospect at 10-m resolution, which has a finer resolution compared to Landsat imagery and an important time extend compared with the Sentinel-2 data. Therefore, our dataset can help to facilitate a deeper understanding of terrestrial transformations over the past four decades.

Land uses (e.g., commercial, residential, and industrial lands) and functional spaces (e.g., living, productive, and ecological spaces) are two-level landscape patches and totally work as basic units for urban planning. The two-level patches are interrelated and mutually binding, but existing mapping methods extracted them separately, leading to substantial conflicts and errors in their mapping results. Accordingly, this study proposes a synergistic classification of multilevel land patches (SC-MLPs). It considers a multitask learning strategy and proposes a novel correlation loss function to measure the correlations between land uses and functional spaces, which is expected to resolve conflicts and improve the accuracy of the two-level land patch mapping results. Consequently, land-use and functional-space maps of three major Chinese cities are generated, which generally have a high resolution of 2 m and high overall accuracies of 90.1% for land uses and 93.8% for functional spaces. Compared to state-of-the-art land-use and functional-space mapping methods, our results have not only higher accuracies but also a better consistency which is improved by 36%. Accordingly, the proposed SC-MLP can generate not only accurate but also consistent maps of land uses and functional spaces, which plays a fundamental role in land system research and urban planning.

OBJECTIVE: This paper proposes a novel application of data augmentation to address various rotation errors of wearable sensors for robust pre-impact fall detection. In such systems, sensor rotation errors are inevitable because of loose attachment and body movement during long deployment. METHODS: Two augmented models with uniform and normal strategies were compared with a non-augmented model on the original dataset (no rotation error) and a validation dataset (with rotation error). The validation dataset was constructed with three types of rotation errors, namely, pitch, roll, and compound roll and pitch (CRP) at three levels of range (low: 15°, medium: 30°, and high: 45°). RESULTS: Five-fold cross validation showed the two augmented models maintained accuracy (>98.5%) as high as the non-augmented model on the original dataset but showed considerable improvements of 6.11% and 6.50% on the validation dataset, respectively. CRP error negatively affected the model accuracy the most, followed by pitch and then roll errors. In addition, the normal model had advantages over the uniform model in the low-to-medium range of error, which is expected to be the typical error range in practical applications. As for lead time, similarly, the augmented models achieved performance similar to the non-augmented model on the original dataset but showed significant improvements on the validation dataset. CONCLUSION AND SIGNIFICANCE: Data augmentation had notable capacities to address sensor rotation errors for practical applications and augmented models especially the normal model showed good potential to be embedded in a wearable system for robust pre-impact fall detection and injury prevention.

The aim of this study was to investigate the effect of different loading symbols and loading durations on online video viewers’ perception of waiting time. Sixty young adults participated in this study and their subjective ratings on waiting time perception for forty-eight different loading symbols (4 progress functions × 2 shapes × 2 embellishments × 3 durations) were obtained through a 7-point Likert scale. Each participant chose the best loading symbols in terms of waiting time perception at the end of the experiment. Results showed that duration and the progress function practically affect the viewers’ waiting time perception, and shape and embellishment do not. Loading symbols of power and inverse power progress functions are perceived as shorter than those of the repetitive and linear progress functions. It is recommended to show loading progress and use manipulated progress functions, and design factors such as shape and embellishment are considered to be less important for designing a better loading symbol. The findings of this study may serve as a useful input for video service providers and symbol designers in creating shortly perceived loading symbols.