Our newly developed Incremental 3-D Object Recognition Network (InOR-Net) is designed to address the obstacles outlined above by enabling continuous recognition of new 3-D object classes, thereby overcoming catastrophic forgetting in previously learned classes. Category-guided geometric reasoning is proposed to deduce local geometric structures, which are distinctive 3-D characteristics of each class, utilizing inherent category information. We formulate a new geometric attention mechanism, guided by a critic, to isolate and utilize the advantageous 3-D characteristics of each class in 3-D object recognition. This scheme is designed to prevent catastrophic forgetting of old classes while mitigating the negative influence of non-essential 3-D features. By implementing a dual adaptive fairness compensation strategy, the forgetting effect due to class imbalance is managed by compensating for the skewed weights and predictions of the classifier. A comparative analysis of the InOR-Net model against other top-performing models demonstrates superior results on various publicly available point cloud datasets.
The neural connection between upper and lower limbs, and the pivotal role of interlimb coordination in human locomotion, underscore the necessity of including correct arm swing as an integral part of gait rehabilitation for individuals with impaired ambulation. Though arm swing is critical for a complete gait, effective methods for maximizing its rehabilitation potential are lacking. A wireless, lightweight haptic feedback system, delivering highly synchronized vibrotactile cues to the arms, was used to manipulate arm swing and examine its impact on the participants' gait patterns in a study involving 12 subjects (20-44 years of age). The system's impact on subjects' arm swing and stride cycle times was substantial, resulting in reductions of up to 20% and increases of up to 35% respectively, compared to their baseline values during normal, unassisted walking. Particularly, a decrease in the cycle times of arms and legs produced a substantial elevation in walking speed, with an average improvement of up to 193%. The subjects' feedback responses were also quantified during both transient and steady-state gait. A study of settling times from the transient responses found that feedback triggered a fast and comparable adjustment in the arm and leg movements, effectively shortening the cycle time (i.e., increasing speed). Feedback for prolonging cycle times (i.e., decreasing pace) resulted in the observation of longer settling durations and varied reaction times between the arms and legs. The study's results definitively demonstrate the developed system's potential to create varied arm-swing patterns, as well as the proposed method's effectiveness in modulating key gait parameters through leveraging interlimb neural coupling, which has implications for gait training approaches.
Gaze signals of high quality are essential in numerous biomedical applications that leverage them. In spite of the limited research on gaze signal filtering, the concurrent issues of outliers and non-Gaussian noise within gaze data remain a hurdle. The primary objective is to develop a comprehensive filtering framework applicable to a wide range of gaze signals, minimizing noise and removing outliers.
Our study formulates an eye-movement modality-based zonotope set-membership filtering framework (EM-ZSMF) to address the issue of noise and outlier presence in gaze signal data. Within this framework are: the eye-movement modality recognition model (EG-NET), an eye-movement modality-driven gaze movement model (EMGM), and a zonotope set membership filter (ZSMF). VT104 inhibitor The EMGM is contingent upon the eye-movement modality, and the filtering of the gaze signal is achieved by combining the ZSMF with the EMGM. This study further creates an ERGF (eye-movement modality and gaze filtering dataset) allowing for the evaluation of future research that combines eye-movement tracking with gaze filtering.
The eye-movement modality recognition experiments yielded the best Cohen's kappa score for our proposed EG-NET, outperforming previous studies. The EM-ZSMF method, as evaluated via gaze data filtering experiments, proved exceptionally effective in diminishing gaze signal noise and eliminating outliers, achieving the best results (RMSEs and RMS) relative to preceding methods.
Through its identification of eye movement patterns, the EM-ZSMF system effectively reduces the noise in gaze data and eliminates any outlying measurements.
To the best of the authors' knowledge, this is the first endeavor to tackle both non-Gaussian noise and outliers in gaze recordings concurrently. Potential applications for the proposed framework encompass any eye image-based eye tracking system, thereby contributing to the broader advancement of eye tracking technology.
This is, to the best of the authors' knowledge, the initial attempt at jointly addressing the issues of non-Gaussian noise and outliers in gaze data. Eye image-based eye trackers can potentially benefit from the proposed framework, which is instrumental in the advancement of eye-tracking technology.
The recent trend in journalism involves a more data-focused and visually oriented approach. A wide audience can more easily comprehend complex topics when aided by visual resources such as photographs, illustrations, infographics, data visualizations, and general images. Research into how visual elements contribute to opinion formation beyond the textual content is a vital undertaking, though substantial work on this topic remains absent. The persuasive, emotional, and memorable aspects of data visualizations and illustrations in journalistic long-form writing are the subject of this research. We conducted a user study to determine the comparative impact of data visualizations and illustrations on shifts in user attitudes about the presented theme. Although visual representations are frequently analyzed from a single perspective, our experimental investigation examines the impact on reader attitudes across three dimensions: persuasion, emotional response, and information retention. A study of multiple versions of a single article allows us to understand the nuanced variations in reader responses based on the visual content, and how these responses change when combined. Data-driven visualizations, unaccompanied by illustrations, achieved a more powerful emotional impact and noticeably altered initial attitudes toward the issue, as demonstrated by the results. Specialized Imaging Systems This investigation adds to the mounting body of work concerning how visual artifacts can shape and influence public understanding and debate. We suggest extending the study’s scope concerning the water crisis to encompass broader applications of the results.
Virtual reality (VR) applications employ haptic technology to directly enhance the feeling of immersion. Research into haptic feedback technologies often features the application of force, wind, and thermal elements. Still, the prevalent form of haptic device simulation targets dry environments, such as living rooms, prairies, or cityscapes. For this reason, riverine, beach, and swimming pool environments are less studied. GroundFlow, a liquid-based system for haptic feedback on a floor, is presented in this paper for simulating flowing fluids on the ground in VR. Our discussion encompasses design considerations, culminating in a system architecture proposal and interaction design. tumour biomarkers To assist in designing a multifaceted feedback mechanism, two user studies are undertaken, followed by the creation of three applications that explore its implementation. Subsequently, the limitations and obstacles inherent in the mechanism are thoroughly evaluated, aiding virtual reality developers and practitioners of haptic technologies.
Immersive experiences are delivered by 360-degree videos, particularly when viewed through virtual reality headsets. However, the inherent three-dimensionality of the video data is often overlooked in VR interfaces designed for accessing such datasets, which almost invariably use two-dimensional thumbnails shown in a grid formation on a plane, either flat or curved. We argue that spherical and cubic 3D thumbnails can lead to a superior user experience, more effectively highlighting the core topic of a video or making it easier to find specific parts. Evaluating 3D spherical thumbnails against 2D equirectangular representations, the study showed a marked advantage for user experience in the 3D format, with 2D representations remaining the top choice for high-level categorization tasks. Despite their presence, spherical thumbnails demonstrated a higher performance than the others when users needed to locate details inside the video. Hence, our data confirms the possible advantage of using 3D thumbnails for 360-degree VR videos, chiefly in the realm of user experience and detailed content search. A hybrid interface design, providing both choices to the users, is suggested. Detailed supplementary materials on the user study and the data employed in the study are hosted at the online location https//osf.io/5vk49/.
This study introduces a mixed reality head-mounted display with a perspective-corrected video see-through, edge-preserving occlusion, and a low-latency design. In order to create a consistent spatial and temporal representation of a real-world scene augmented with virtual objects, we execute three critical operations: 1) modifying captured imagery to match the user's field of view; 2) ensuring virtual objects are correctly hidden behind nearer real-world objects, thus providing precise depth cues; and 3) dynamically updating the combined virtual and real scenes in synchronicity with the user's head movements. Image reconstruction and the creation of occlusion masks depend crucially on the density and accuracy of depth maps. Unfortunately, the calculation of these maps requires substantial computational resources, leading to longer latencies. To find an acceptable balance between spatial consistency and low latency, we rapidly created depth maps, concentrating on smooth edges and resolving occlusions (instead of a complete map), to accelerate the processing time.