Phase-change materials (PCMs) demonstrate transformative potential for wearable thermal management systems; however, their practical implementation faces challenges due to trade-offs among energy storage density, mechanical robustness, and phase-change stability. Here, we present a nanotechnology-directed strategy that integrates ultralow carbon nanotubes (CNT, 0.1 wt.

Many robotic tasks are temporally extended and demand precise specifications of subgoals, constraints, and their temporal ordering. Yet human operators typically communicate such tasks in natural language, which is inherently ambiguous, underspecified, and context dependent. Translating human instructions into formal task specifications, such as Linear Temporal Logic (LTL), is therefore essential for verifiable and safe robotic execution. Existing LLM-based translators attempt to bridge this gap through open-ended reasoning or post-hoc constraint enforcement, but the former may violate domain constraints, whereas the latter can disrupt the reasoning needed for novel instructions. This paper proposes Self-Constrained Reasoning (SCR), a framework that mitigates this trade-off by internalizing structural knowledge into the model's decision-making process rather than imposing it as an external filter. By combining a structural constraint representation with a hierarchical decision-making formulation, SCR guides reasoning within a formally grounded space while preserving adaptability to unseen instructions. Experiments show that SCR improves both domain-constraint satisfaction and generalization, providing an effective and interpretable approach for translating human intent into verifiable specifications for robotic execution.

Bowden cable actuation has attracted increasing attention in rehabilitation robotics because it can transmit power remotely and detach the heavy motors from the robot frame. However, existing control technologies for rehabilitation robots commonly ignore the cable-configuration-dependent nonlinearities in Bowden-cable-actuated robots. Moreover, the dynamic stability of Bowden-cable-actuated robots has not been theoretically guaranteed. To this end, this paper carries out a systematic investigation on the dynamic modeling and impedance control of Bowden-cable-actuated rehabilitation robots. First, a general dynamic model is developed for a class of Bowden-cable-actuated robots, which comprehensively accounts for the effects of friction between the cable and sheath, as well as cable bending and elongation, on the robot's dynamics. A series of experiments conducted on a test bench validate the model. Then, a novel adaptive impedance controller is proposed for a more natural interaction, with the control objective specified as achieving a desired impedance behavior. The presented method can effectively handle the nonlinearities in Bowden-cable-actuated robots and adapts to changes in model parameters due to variations in cable configurations. The convergence of the impedance error and the stability of the closed-loop system are rigorously proven using the Lyapunov method. Simulation and experimental results on lower-limb rehabilitation robots are presented to demonstrate the effectiveness of the proposed control scheme. (c) 2026 Published by Elsevier Ltd.

Knee pain is prevalent in over 20% of the population, limiting the mobility of those affected. In turn, isokinetic dynamometers and robots have been used to facilitate rehabilitation for those still capable of ambulation. However, there are at most only a few wearable robots capable of delivering isokinetic training for bedridden patients. Here, we developed a wearable robot that provides bedside isokinetic training by utilizing a variable stiffness actuator and dynamic energy regeneration. The efficacy of this device was validated in a study involving six subjects with debilitating knee injuries. During two courses of rehabilitation over a total of three weeks, the average peak torque, average torque, and average work produced by their affected knees increased significantly by 81.0%, 101.4%, and 117.6%, respectively. Furthermore, the device’s energy regeneration features were found capable of extending its operating time to 198 days under normal usage, representing a 57.8% increase over the same device without regeneration. These results suggest potential methodologies for delivering isokinetic joint rehabilitation to bedridden patients in areas with limited infrastructure.

The Bowden cable transmission system is employed in wearable exoskeleton devices because of its ability to transmit power remotely. However, achieving precise motion control in Bowden cable-driven exoskeletons is challenging due to two main factors: 1) Bowden cable transmission brings nonlinearities associated with cable configurations and 2) cable configurations can change at any time due to the wearer’s movements. In this study, we achieve finite-time tracking control for Bowden cable-driven exoskeletons with time-varying cable configurations. First, a general dynamic model is established for a class of multiple-degree-of-freedom (DOF) Bowden cable-driven exoskeletons. Then, based on a suitably defined nonsingular terminal sliding vector and Lyapunov stability theory, a novel robust controller is proposed for Bowden cable-driven exoskeletons. This controller does not rely on the accurate model parameters and can guarantee that the tracking error converges to the origin within a finite time. Notably, unlike existing controllers, which are available only when cable configurations remain invariant or vary slowly, the proposed controller can achieve precise control even under time-varying cable configurations. Finally, we build a Bowden cable-driven hip exoskeleton and validate the effectiveness of the proposed method through simulations and experiments.

Understanding biomedical experiments provides a foundation for downstream tasks, e.g., laboratory automation, and facilitates effective cross-disciplinary communication. Two challenges, High Information Density (HID) and Multi-Step Reasoning (MSR), pose unique difficulties for precise experimental understanding. Extracting structured knowledge, e.g., Knowledge Graphs (KGs), is an effective approach to address the HID and MSR. However, existing biomedical datasets for structured knowledge information extraction are limited to a general or coarse-grained level, hindering fine-grained experimental understanding. To address this gap, we introduce Biomedical Protocol Information Extraction Dataset (BioPIE), a dataset providing procedure-centric KGs that capture entities, actions, and relations at a scale sufficient for reasoning across biomedical protocols. We evaluate information extraction methods on BioPIE and implement a question answering system leveraging the dataset for validation, demonstrating improved understanding performance on test sets as well as on the HID and MSR question sets.

The tendon-sheath mechanism is widely used in rehabilitation, surgical, and rescue robotics for its high flexibility and ability to transmit power over long distances. However, sheath-configuration-dependent nonlinearities make precise control of tendon-sheath actuated robots challenging. In this article, we investigate the tracking control of tendon-sheath actuated robots in the discrete-time domain. First, a discrete mathematical model of a class of multijoint tendon-sheath actuated robots is developed using the Euler approximation approach. This model comprehensively considers the effects of friction between the tendon and sheath, tendon deformation, and distal loads on the robot's dynamics. Then, a discrete-time adaptive neural network (NN) controller is proposed for tendon-sheath actuated robots via the backstepping technique. By employing a radial basis function neural network (RBFNN) to approximate the unknown nonlinear function and two adaptive update laws for dynamic parameter tuning, it can effectively handle system nonlinearities and accommodate variations in sheath configuration. Unlike existing studies, the presented controller is formulated in the discrete-time domain and accounts for the dynamic coupling between joints. As a result, it is better suited for implementation on digital devices and can be applied to multijoint tendon-sheath actuated robots. The stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, simulations and experiments on tendon-sheath actuated rehabilitation robots validate the effectiveness of the proposed control strategy.

Evolutionary pressures have pushed humans to become efficient walkers, but inefficient divers. People consume more energy to travel the same distance underwater than on land. In diverse overground locomotion, emerging exoskeletons have reduced the metabolic cost of humans. Can we also improve the energy economy in underwater locomotion via exoskeletons? Here, we propose an underwater exoskeleton to assist scuba diving using flutter kick, by applying assistive knee extension torque during the strike phase of the diving kick cycle. When divers wore the powered exoskeleton, the average net air cost across six experienced divers was reduced by 22.7±10.0%, and the peak quadriceps activation was decreased by 20.9±7.5%, compared with normal diving without the exoskeleton. The average gastrocnemius activation also decreased by 20.6±5.3%, suggesting that the divers sufficiently utilized the exoskeleton assistance. These results indicate that applying exoskeleton assistance is conducive to improving the endurance of human underwater diving and enhancing our ability to explore the underwater world. Our study extends the application boundary of wearable robots, and provides a reference for the

Knee exoskeletons exhibit substantial variability in mechanical configurations and actuation pathways, leading to distinct biomechanical impacts on wearers. However, systematic cross-configuration comparisons under consistent modeling conditions remain limited. Using a unified predictive musculoskeletal framework, this study evaluates the human biomechanical responses to 3 representative knee exoskeleton configurations (anterior cable-driven, lateral parallel-actuated, posterior linkage-driven) during level-ground and uphill walking. Configuration-specific human–exoskeleton interaction forces were analytically modeled and applied to an identical musculoskeletal model under a standardized knee extension assistance profile. Across both terrains, all configurations reduced the knee extension moment impulse relative to unassisted walking, with the lateral configuration consistently achieving the largest reduction, reaching 33.6

Cable-driven exoskeletons can effectively assist patients with neuromuscular injuries in active rehabilitation training. Off-board actuation can substantially reduce the load borne by the wearer while enabling the use of higher-power actuators, batteries, and controllers. However, existing off-board actuation systems still rely on manual operation or fixed installations, which leads to heavy manpower burden, limited overground walking, uncontrollable relative positioning, and degraded force transmission. In this paper, we propose a novel autonomous mobile off-board actuation platform specifically designed for cable-driven exoskeletons, aiming to simultaneously achieve hands-free overground mobility, high force transmission efficiency, and safety under cable constraints. The platform features a holonomic (omnidirectional) mobile architecture for formation keeping under tethered human-robot interaction, a transmission-oriented human-following protocol that maintains a fixed relative pose (a constant distance behind the wearer with a 0 degrees orientation offset) to minimize Bowden cable bending and mitigate friction-induced efficiency loss, and a hybrid passive-active safety architecture combining a strain-compensated mechanism with an electromagnetic quick-release module to handle sudden tension and enable emergency decoupling. Moreover, a multi-sensor fusion framework together with a fuzzy-PD controller is developed for real-time 3-DOF pose estimation and robust trajectory tracking. A prototype is constructed and extensively evaluated. Experimental results demonstrate accurate following, rapid and reliable safety disengagement, and consistently high force transmission efficiency, validating the effectiveness of the proposed platform.

This study aims to investigate the biomechanics of the flutter kick in diving, including lower limb kinematics, muscle activation patterns, and coordination, as well as how these characteristics change under varying diving speeds. Ten divers performed 100-meter underwater swimming at self-selected normal and fast speeds, and their lower limb kinematic data and surface electromyography signals from eight muscles were collected. Based on the refined phase division of the kicking cycle into two transition phases, a power phase, and a recovery phase, the movement pattern featuring bending waves is characterized by hip-driven sequential activation of the knee and ankle joints. The quadriceps serve as the primary power source, while the shank muscles provide joint stabilization. Muscle activation analysis revealed two synergistic patterns, with the tibialis anterior and gastrocnemius lateralis exhibiting activation in both patterns and showing the highest co-activation index. With increasing speed, the peak angular velocities of all joints increased. The range of motion of the ankle joint increased, while the timing of its peak angular velocity was later. Although the muscle activation patterns remained similar, they exhibited a prolonged duration and increased amplitude of activation. This study enhances the understanding of the flutter kick with fins in diving.

Accurately measuring and assessing human swimming performance remains challenging due to difficulties in capturing full-body motion in horizontal postures and evaluating swimming dynamics based on measured data. This study proposes an integrated framework combining wearable inertial measurement units (IMUs), an onshore swim trainer, and a multi-rigid-body dynamic model to measure swimming kinematics and evaluate swimming performance. Seventeen wireless IMUs are used to capture full-body motion data during onshore breaststroke, freestyle, and butterfly strokes, and comparison with optical motion capture data demonstrates that the IMU-based measurement scheme has good validity (Spearman’s correlation >0.75), reliability (ICC >0.75), and accuracy (NRMSE <25%) for most body segments. However, lower limb and trunk motions deviate from typical in-water patterns due to restricted downward swing on the onshore trainer. To assess swimming performance with the IMU-measured data, a Newton-Euler dynamic model incorporating fluid forces is developed. Simulations reveal that stroke frequency (SF) has a significant effect on swimming speed and propulsion force across the three strokes. Two case studies further demonstrate the framework’s potential for motion optimization: modifying arm movements in freestyle and trunk movements in butterfly can improve swimming performance. Overall, this framework enables reliable and efficient onshore swimming motion measurement, dynamic performance assessment, and individualized technique optimization, which could provide a supplementary tool and preliminary screening method for guiding swimming training and swimming robot development.

The knee joint plays a critical role in locomotion but is susceptible to overuse injuries, motivating the development of assistive exoskeletons. Current designs face a fundamental trade-off between achieving kinematic compatibility with the knee's complex polycentric motion and providing effective variable-stiffness functionality for biomechanical support. This study presents a novel cable-driven multisegment exoskeleton to reconcile these competing requirements through an integrated biomimetic design. The proposed system employs redundant rotational joints and a linear guide rail to passively accommodate natural joint kinematics while enabling wide-range stiffness regulation (0-207 Nm/rad) via active cable length adjustment. This single-actuator approach achieves dynamic stiffness regulation, deterministic torque transmission with an effective moment arm exceeding 70 mm, and seamless state modulation within a low-profile structure (0.63 kg). Benchtop characterization confirmed precise stiffness control across the operational range (rmse <= 0.035 Nm/rad). Human subject experiments revealed significant muscular effort reduction during demanding tasks without compromising natural joint kinematics, including 23.9% decrease in peak vastus lateralis activation during incline walking and 29.2% reduction during squatting compared to unassisted conditions. These results validate the exoskeleton's ability to reconcile anatomical compatibility with physiologically relevant stiffness regulation, representing a significant advance in knee assistive technology with broad applications in clinical rehabilitation and physical performance augmentation. This study bridges a critical gap in knee exoskeleton development, offering a unified solution for comfortable and effective assistance across dynamic tasks.

Lower-limb wearable robots require accurate Locomotion Mode Prediction (LMP) to provide appropriate assistance across diverse terrains. Recent reconstructionbased LMP methods fuse high-dimensional multimodal sensor data to model the relationship between human motion and terrains, improving prediction accuracy and cross-terrain adaptability. However, they typically incur high computational cost, including GPU dependency, and often require initialization procedures involving wearer participation or professional supervision. This paper proposes a self-initialized, GPU-free LMP method to overcome these deployment constraints. Our method adopts a gravity-aligned world coordinate frame as a unified geometric reference: a self-initialization procedure first establishes this reference, upon which a progressive plane representation enables GPU-free terrain reconstruction. Together, these two components form a pipeline that achieves reconstruction-based LMP on an onboard CPU without manual intervention. Comprehensive experiments across various terrains and subjects evaluate the system in terms of LMP accuracy, initialization success rate, computational efficiency, and memory footprint. We also compare the proposed method with a lightweight end-to-end baseline to further examine the role of the GPU-free terrain reconstruction. The results show that the proposed method achieves prediction accuracy comparable to state-of-the-art methods, including GPU-dependent counterparts, while operating entirely on a CPU without manual intervention.

Total Knee Arthroplasty (TKA) is an effective treatment for patients suffering from severe knee osteoarthritis. However, the success of postoperative functional recovery is closely related to early ambulation after surgery, especially in the acute phase. Although lower-limb exoskeletons have been used for gait training, existing devices are not suitable for post-TKA patients in the acute phase. In this study, we propose a portable, rigid-soft hybrid knee exoskeleton for post-TKA patients. The exoskeleton employs a patient-centric design to address postoperative challenges. Considering weak physical mobility, joint instability, and inability to walk on a treadmill, the mechanical design integrates a lightweight, joint-free linear actuator, paired with a portable actuation unit for overground ambulation. To accommodate substantial interpatient kinematic variability and weakened postoperative joint control, the high-level control system implements a personalized trajectory generator and a patient-in-loop modeling pipeline. The results of the clinic test showed that after gait training, the maximum knee angle of patients increased from 16.45(degrees) to 26.43(degrees), and the knee angle range improved from 12.64(degrees) to 24.46(degrees). The preliminary clinic experiment showed that the portable system might be safe and feasible for patients. These findings suggest that our proposed exoskeleton-based intervention is a potential approach for acute-phase gait training of TKA patients.

Understanding the dynamic behavior of the human trunk during locomotion is critical for addressing prevalent disorders like low back pain and scoliosis, yet current biomechanical models often oversimplify the trunk as rigid segments. To bridge this gap, we introduce a novel approach combining body surface topography changes with network analysis to characterize trunk motion as a dynamic continuum. By employing the skin surface as a non-invasive observer, we utilized community detection algorithms to identify synchronous deformation regions (SDRs) during gait. Our results suggest that the human back operates as a modular system of synchronized kinematic regions rather than a single homogeneous tissue. These SDRs exhibit robust spatial boundaries and long-range synergies across varying walking speeds, reflecting underlying musculoskeletal dynamics, including spinal kinematics and myofascial coordination. We identified stable clustering in thoracic and lumbar regions, alongside speed-dependent pelvic-scapular synchronization, validating the synergy between upper and lower limb girdles. This framework establishes a methodological foundation for precision rehabilitation by observing the motion of the outer skin. In the future, after validation in larger cohorts and clinical populations, this approach may support candidate surface-derived indicators for evaluating pathological deviations, such as asymmetry in scoliosis or rigid movement patterns in low back pain.

Plantar flexor is crucial for overcoming gravity and generating forward propulsion during walking. While exoskeletons aim to reduce the load on plantar flexors, current passive and active designs face challenges in delivering both tunable and energy-efficient assistance. In addition, the impact of exoskeleton assistance on plantar flexor muscle force remains unclear. This study introduces a cable-driven ankle exoskeleton that provides tunable assistance across various terrains, including energy-efficient eccentric assistance via electromagnetic damping, without requiring electrical power for motor operation during this phase. A detailed analysis of ankle joint kinematics, kinetics, muscle activation, and muscle force revealed that plantar flexor muscle force decreased by 18.63%, correlated with reductions in ankle moment (15.71%) and muscle activation (16.92%). This study highlights an efficient exoskeleton design and offers new insights into musculoskeletal responses, advancing our understanding of human-exoskeleton interaction.

Human endurance in underwater locomotion is fundamentally restricted by high energetic demands to overcome drag and the finite supply of self-contained breathing gas. While exoskeleton technology can reduce the metabolic cost of humans in terrestrial locomotion, its potential to enhance human endurance during underwater diving remains entirely unexplored. Here, we present DiveMate, a field-deployable, untethered exoskeleton designed to improve human diving endurance via adaptive kick assistance in real-world underwater environments. During naturalistic diving, DiveMate increases the travel distance using a given energy (breathing gas) by 42.9

Patients with mild lower limb dysfunction maintain independent mobility but face elevated fall risks. Traditional walking aids, such as canes, require continuous manual support, which not only restricts the user’s upper-limb mobility but may also foster dependency. This paper introduces a novel intelligent robotic cane that provides support only when necessary. First, an ultra-lightweight (1.5 kg) utilizing a specialized force-transfer mechanism is designed to ensure high load-bearing capacity without compromising maneuverability. Second, a marker-less intention estimation system fusing RGB-D vision with Ultra-Wideband (UWB) is developed to achieve precise position and orientation tracking. Furthermore, a proactive fall prediction algorithm based on upper-body kinematics is developed to trigger pre-impact interventions. Finally, an adaptive fuzzy PD controller is designed to enable the robotic cane to perform stable and precise dynamic following under predefined biomechanical rules, thus freeing the user’s hands during stable locomotion. Experimental results demonstrate that the developed robotic cane successfully achieves high-precision hands-free autonomous following, consistently maintaining its optimal support position during normal walking. Additionally, in simulated instability tests, the system exhibited rapid risk identification and support response capabilities, validating its effectiveness in timely intervention and fall prevention. These findings from healthy subjects not only confirm the technical feasibility of the system but also lay a solid foundation for subsequent clinical trials to evaluate its efficacy in target populations.

The mobile manipulator holds potential for executing multi-scene operational tasks through the collaborative movement of both the mobile base and manipulator. However, the conventional sequential base-manipulator control method for mobile manipulator is restricted in speed and gracefulness by the need for the mobile base to stop moving before the manipulator starts moving. In contrast, humans effortlessly handle such tasks while walking or running, simultaneously managing secondary tasks such as avoiding obstacles, optimizing posture, and monitoring the environment. Regrettably, mobile manipulators lack this agile finesse displayed by humans. To address this shortfall, this paper introduces a coordinated motion planning method that considers the manipulator and mobile base as a whole structure based on the task-priority redundancy resolution and considers the optimal mobile base placement in navigation. The simulation results demonstrate that the suggested method significantly improves the speed, reliably, and task completion efficiency of the mobile manipulator.