Ensuring timely takeover in conditionally autonomous vehicles presents a significant challenge, especially when drivers are distracted by non-driving-related tasks or are in suboptimal emotional states. Existing driver monitoring systems struggle with a trade-off between practicality and reliability. Physiological sensors are intrusive, vision-based methods are sensitive to occlusions and variable lighting, and current multimodal learning approaches often rely on simple fusion strategies that fail to reconcile heterogeneous data. We introduce MUST (Multimodal Unified Smartwatch-based Takeover), a framework that predicts driver state and takeover performance using unobtrusive smartwatch signals. MUST employs an asymmetric causal fusion mechanism to model the interplay between driver behavior and emotion. The performance of the architecture was validated in diverse simulator environments reflecting real-world driving conditions, demonstrating robust driver state estimation and takeover prediction. This work establishes the smartwatch as a practical tool for adaptive takeover support, enabling reliable readiness assessment without intrusive hardware or fragile vision systems.

For stroke survivors, diminished hand functions limit their ability to perform activities of daily living (ADLs). Recently, soft-robotic gloves have assisted stroke survivors in active rehabilitation by facilitating their finger movements based on intentions expressed through biosignals, such as electromyogram and electroencephalogram. In this regard, helping stroke survivors actively train multiple hand postures can improve hand functions required for ADLs. However, detecting intentions regarding multiple hand postures remains challenging, often resulting in low online classification performance. To address this, we propose a hand rehabilitation system comprising a vision-based intention detection framework and 8-degree-of-freedom soft-robotic glove. Our proposed framework, depth enhanced hand posture intention network, analyzes images and depths data observing users' arm behavior and hand-object interactions to predict intentions for multiple hand postures. The 8-degrees-of-freedom soft-robotic glove facilitates flexion and extension of individual fingers to help users perform desired hand postures. To support active rehabilitation, we operate our glove to facilitate user's finger movements when the user exerts effort to generate desired hand postures. We test our system on a real-time pick and place task involving five hand postures most commonly utilized in ADLs. Our vision-based system could predict and facilitate the desired hand postures for five healthy individuals and three stroke survivors with average accuracy of 90.4 ± 3.6% and 80.3 ± 4.6%, respectively, outperforming methods reported in previous studies.

This study addresses a view-path-planning problem during 3-D scanning of a large-scale structure based on multiview stereo (MVS) for unmanned aerial platforms. Recently, most studies have adopted an explore-then-exploit strategy for 3-D scanning. The strategy first generates a coarse model from a simple overhead scanning and then plans an inspection path to cover the entire surface of the coarse model. However, even though the inspection path may be optimal, it is difficult to guarantee a complete and accurate reconstruction result due to defective factors of MVS such as occlusions, textureless surfaces, and insufficient parallaxes. Furthermore, the entire procedure of this strategy is inefficiently slow because of path redundancies and long MVS processing time. Therefore, we propose a novel view-path-planning method for 3-D scanning based on an online MVS reconstruction algorithm. The suggested method incrementally reconstructs the target model online and iteratively plans view paths by analyzing the current partial reconstructions. The method continuously analyzes the quality of the model and detects inaccurately reconstructed surfaces. It then plans an inspection path that provides a complete coverage of the detected surfaces while maximizing the performance of MVS. This method can construct a complete 3-D model in a single scanning trial without the need for rescanning. Extensive experiments show that our method outperforms the other state-of-the-art methods, especially in terms of the model completeness of complex structures.

Multi-view stereo (MVS) algorithms have been commonly used to model large-scale structures. When processing MVS, image acquisition is an important issue because its reconstruction quality depends heavily on the acquired images. Recently, an explore-then-exploit strategy has been used to acquire images for MVS. This method first constructs a coarse model by exploring an entire scene using a pre-allocated camera trajectory. Then, it rescans the unreconstructed regions from the coarse model. However, this strategy is inefficient because of the frequent overlap of the initial and rescanning trajectories. Furthermore, given the complete coverage of images, MVS algorithms do not guarantee an accurate reconstruction result.In this study, we propose a novel view path-planning method based on an online MVS system. This method aims to incrementally construct the target three-dimensional (3D) model in real time. View paths are continually planned based on online feedbacks from the partially constructed model. The obtained paths fully cover low-quality surfaces while maximizing the reconstruction performance of MVS. Experimental results demonstrate that the proposed method can construct high quality 3D models with one exploration trial, without any rescanning trial as in the explore-then-exploit method.