OBJECTIVE: This study evaluates the impact of self-management and support of m-health applications on medication adherence (MA) and the corresponding long-term medical expenditures among patients with Type 2 Diabetes (T2D), using an analytic framework generalizable to other chronic conditions. MATERIALS AND METHODS: A systematic review and meta-analysis of randomized controlled trials were conducted to estimate the synthesized effect of m-health interventions on MA. These results were integrated into a Markov state-transition model to simulate patient transitions among three adherence levels over a 10-year horizon. Medical expenditure data by adherence level were derived from the Medical Expenditure Panel Survey (MEPS). Monte Carlo simulation was applied to assess uncertainty and estimate individual- and population-level cost outcomes under baseline and intervention scenarios. RESULTS: The meta-analysis showed a significant positive effect of m-health on MA (standardized mean difference = 0.21, 95% CI: 0.14-0.28). Patients in the intervention scenario experienced an average cost reduction of $4400 over 10 years. At the population level, a cohort of 10 000 patients using m-health tools would yield projected direct medical cost savings of $44 million. DISCUSSION: This study demonstrates the potential of m-health interventions to improve patient behavior and generate substantial long-term cost savings. By linking behavioral health data to downstream cost outcomes, the study adds to the growing evidence base for informatics-driven population health strategies. CONCLUSION: Our study underscores the importance of integrating digital support tools into chronic disease care and informs policy decisions aimed at integrating health informatics innovations.

Classic object detectors are fundamentally limited by their reliance on a single, static viewpoint, which often suffers from occlusions, challenging scales, and ambiguous perspectives. Video-based object detectors can partially mitigate this problem by aggregating information from multiple views, but they usually work with passively gathered sequences and are in no means guaranteed to provide sufficient information for all objects of interest. On the other hand, active vision methods can seek out better views, yet existing active object detectors typically only try to search for a single optimal perspective and discard valuable information gathered along their path. In this article, we introduce a new paradigm for object detection that unifies active exploration with cumulative spatiotemporal aggregation. We train an embodied agent to intelligently explore its environment, guided by a novel, detection-aware reward function that directly encourages seeking out views that resolve visual ambiguities. To leverage this active exploration, we introduce a robust aggregation pipeline that adeptly harnesses SAM 2’s temporal reasoning capabilities to fuse information from the agent’s entire trajectory into a single, coherent set of detections. Through extensive experiments on AI2-THOR, we demonstrate that our framework provides a consistent and substantial performance uplift when applied to a wide range of state-of-the-art detectors, establishing a strong and versatile new baseline for the next generation of active vision systems.

Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearance. To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding. Specifically, a new task termed Embodied Reference Understanding (ERU) is first designed for this concern. Then a new dataset called ScanERU is constructed to evaluate the effectiveness of this idea. Different from existing datasets, our ScanERU dataset is the first to cover semi-synthetic scene integration with textual, real-world visual, and synthetic gestural information. Additionally, this paper formulates a heuristic framework based on attention mechanisms and human body movements to enlighten the research of ERU. Experimental results demonstrate the superiority of the proposed method, especially in the recognition of multiple identical objects. Our codes and dataset are available in the ScanERU repository.

Recent advances on 3D object detection heavily rely on how the 3D data are represented, i.e., voxel-based or point-based representation. Many existing high performance 3D detectors are point-based because this structure can better retain precise point positions. Nevertheless, point-level features lead to high computation overheads due to unordered storage. In contrast, the voxel-based structure is better suited for feature extraction but often yields lower accuracy because the input data are divided into grids. In this paper, we take a slightly different viewpoint --- we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer sufficient detection accuracy. Bearing this view in mind, we devise a simple but effective voxel-based framework, named Voxel R-CNN. By taking full advantage of voxel features in a two-stage approach, our method achieves comparable detection accuracy with state-of-the-art point-based models, but at a fraction of the computation cost. Voxel R-CNN consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region Proposal Network, and a detect head. A voxel RoI pooling is devised to extract RoI features directly from voxel features for further refinement. Extensive experiments are conducted on the widely used KITTI Dataset and the more recent Waymo Open Dataset. Our results show that compared to existing voxel-based methods, Voxel R-CNN delivers a higher detection accuracy while maintaining a real-time frame processing rate, i.e., at a speed of 25 FPS on an NVIDIA RTX 2080 Ti GPU. The code is available at https://github.com/djiajunustc/Voxel-R-CNN.