Exoskeletons have been shown to effectively assist humans during steady locomotion. However, their effects on non-steady locomotion, characterized by nonlinear phase progression within a gait cycle, remain insufficiently explored, particularly across diverse activities. This work presents a shank angle-based control system that enables the exoskeleton to maintain real-time coordination with human gait, even under phase perturbations, while dynamically shaping assistance profiles to match the biological ankle moment patterns across walking, running, stair negotiation tasks. The control system consists of an assistance profile online generation method and a model-based feedforward control method. The assistance profile is formulated as a dual-Gaussian model with the shank angle as the independent variable. Leveraging only IMU measurements, the model parameters are updated online each stride to adapt to inter- and intra-individual biomechanical variability. The profile tracking control employs a human-exoskeleton kinematics and stiffness model as a feedforward component, reducing reliance on historical control data due to the lack of clear and consistent periodicity in non-steady locomotion. Three experiments were conducted using a lightweight soft exoskeleton with multiple subjects. The results validated the effectiveness of each individual method, demonstrated the robustness of the control system against gait perturbations across various activities, and revealed positive biomechanical and physiological responses of human users to the exoskeleton's mechanical assistance.

While rehabilitation exoskeletons have been extensively studied, systematic design principles for effectively addressing heterogeneous bilateral locomotion in hemiplegia patients are poorly understood. In this article, a multijoint lower exoskeleton driven by series elastic actuators (SEAs) is developed, and the design philosophy of rehabilitation robots for hemiplegia patients is systematically explored. The exoskeleton has six powered joints for both lower limbs in a hip–knee–ankle configuration, and each joint incorporates a custom, lightweight SEA module. A unified interaction-oriented control framework is designed for exoskeleton-assisted walking, including gait generation, task scheduling, and advanced joint-level control. The closed-loop design provides methodical solutions to address hemiplegia rehabilitation needs and provides walking assistance for bilateral lower limbs. Moreover, a multitemplate gait generation approach is proposed to address the altered kinematics induced by exoskeleton-assisted walking and enhance the exoskeleton's adaptability to patient-specific kinematic variations in an iterative manner. Experiments are conducted with both healthy individuals and hemiplegia patients to verify the effectiveness of the exoskeleton system. The clinical outcomes demonstrate that the exoskeleton can achieve mechanical transparency, facilitate movement, and enable coordinated interjoint locomotion for bilateral gait assistance.

Rehabilitation exoskeletons have been demonstrated to benefit mobility-limited patients; however, accessibility is hindered by several challenges, and interaction evaluation of coupled human–exoskeleton systems remains critically understudied. In this pioneering study“, assistive normality (AN)” is introduced to characterize the cross-stage spatiotemporal features of exoskeleton-assisted gait in hemiplegic patients, and a multidimensional, low-data-cost metric framework is developed to quantify AN. Three subdivided metrics, including gait restoration (GR), phase-deviation weighting (PDW) and multijoint coordination (MJC), are proposed to assess coupled system interaction behavior across different intervention stages during rehabilitation. A homologous difference evaluation paradigm (HDEP) is introduced to capture pathological differences between healthy and hemiplegic subjects on the basis of the reusability of experimental data, providing an approach for the nominal assistance calibration and assessment of specific exoskeleton devices. In a pilot study with eight healthy individuals and nine hemiplegic patients, between-group metric differences were analyzed to determine the calibrated AN of an exoskeleton. The results provide calibration references for quantitative metrics and demonstrate the ability of framework to characterize the temporal dynamics of human–exoskeleton interactions. The proposed AN framework offers a generalizable approach to assistive robotics, potentially enhancing evaluation of human-robot interaction and advancing clinical rehabilitation applications.

3D dense captioning aims to semantically describe each object detected in a 3D scene, which plays a significant role in 3D scene understanding. Previous works lack a complete definition of 3D spatial relationships and the directly integrate visual and language modalities, thus ignoring the discrepancies between the two modalities. To address these issues, we propose a novel complete 3D relationship extraction modality alignment network, which consists of three steps: 3D object detection, complete 3D relationships extraction, and modality alignment caption. To comprehensively capture the 3D spatial relationship features, we define a complete set of 3D spatial relationships, including the local spatial relationship between objects and the global spatial relationship between each object and the entire scene. To this end, we propose a complete 3D relationships extraction module based on message passing and self-attention to mine multi-scale spatial relationship features and inspect the transformation to obtain features in different views. In addition, we propose the modality alignment caption module to fuse multi-scale relationship features and generate descriptions to bridge the semantic gap from the visual space to the language space with the prior information in the word embedding, and help generate improved descriptions for the 3D scene. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art methods on the ScanRefer and Nr3D datasets.