Search Results (309)

Stroke is the second leading cause of death globally and a major contributor to lower-limb disability, affecting gait, balance, and functional independence in elderly populations. While robot-assisted rehabilitation has demonstrated effectiveness in motor recovery, access remains limited due to high costs and geographic barriers, particularly in Latin America. This study presents ExoKnee, a low-cost knee exoskeleton designed through biomimetic principles and 3D-printed fabrication as a proof-of-concept device targeting gait rehabilitation in elderly adults. The system integrates a single-degree-of-freedom pneumatic actuator controlled by electromyography (EMG) signals from the quadriceps muscle, enabling knee flexion and extension (90° to 180°). The design was evaluated through finite element analysis and dynamic simulations in MATLAB/Simulink R2024a under constant, stepwise, and sinusoidal reference inputs in a digital-twin environment. Expert validation using the Content Validity Coefficient yielded a mean score of 0.8747, reflecting preliminary expert agreement on the conceptual design’s coherence and relevance. The prototype demonstrated controlled movements through a 6-bar pneumatic system with EMG-triggered relay activation, validated at the proof-of-concept level through simulation and single-subject threshold calibration. ExoKnee addresses critical gaps by offering an anthropometrically informed, biosignal-driven, and locally manufacturable rehabilitation platform for low- and middle-income countries, pending clinical validation. Future work will focus on clinical trials and adaptive EMG control strategies. Full article

The aging population and the high incidence of neurological disorders have driven an increasing demand for lower-limb motor dysfunction rehabilitation. Traditional rehabilitation methods suffer from limitations such as low efficiency and a lack of personalization. Lower-limb rehabilitation exoskeleton robots have emerged as a critical solution, with human–robot intelligent fusion serving as the core theoretical framework and technological pathway for performance enhancement. From the unique perspective of human–robot intelligent fusion, this paper systematically reviews the application and recent advances of artificial intelligence in three key aspects—intention perception, intelligent control, and human–robot integration—based on a layered architecture of “fusion perception, fusion decision-making, and fusion execution”. The definition, connotations, and realization mechanisms of human–robot intelligent fusion are clarified. Furthermore, this review analyzes the fusion mechanisms, applicable scenarios, and technical characteristics of different AI technologies and summarizes the human–robot intelligent fusion modes and clinical application status of representative products such as EksoNR, MyoSuit, and AiLegs. In addition, key challenges are identified from the perspectives of fusion generalization capabilities, the trade-off between real-time performance and robustness, algorithm interpretability, and multimodal deep fusion mechanisms. This paper provides a systematic theoretical reference and technical roadmap for establishing a unified human–robot intelligent fusion framework for lower-limb rehabilitation exoskeletons. Full article

To address the high physical demands faced by personnel engaged in power maintenance operations, this study develops a hip assistive exoskeleton capable of state recognition between level-ground walking and transmission tower climbing. The mechanical structure of the exoskeleton is designed based on motion data analysis of human level-ground walking and tower climbing activities. A dynamic model of the human lower limb is conducted to support state-based torque control of the actuators. To accommodate different locomotion scenarios, a control strategy based on a hierarchical finite state machine (HFSM) is proposed to achieve adaptive state recognition and enable the exoskeleton to provide state-specific torque output. State recognition and transition experiments, alongside laboratory and field transmission tower climbing experiments, are conducted. The results show that the exoskeleton can reliably recognize transitions between walking and climbing, providing effective assistance during transmission tower climbing operations. Furthermore, laboratory and field transmission tower climbing experiments show that exoskeleton assistance reduces integrated EMG (IEMG), root mean square (RMS) and maximum absolute value (MAXABS) values of the biceps femoris (BF), rectus femoris (RF), and vastus medialis (VM), demonstrating the effectiveness of the exoskeleton. Full article

Accurate estimation of human joint moments from multimodal sensor signals is essential for lower-limb exoskeleton control. Recent studies have addressed this problem in an end-to-end manner, but remain limited by insufficient long-range temporal modeling, limited training data, and class imbalance. To address these issues, we propose Diff-MomentFormer, a generative diffusion-augmented Transformer framework for end-to-end joint moment estimation from multimodal wearable sensor signals. The framework integrates a classifier-free conditional diffusion model for activity-conditioned synthetic data generation, together with a Transformer-based regression network for modeling long-range temporal dependencies and cross-modal interactions. Through the combination of controllable data augmentation and global temporal modeling, Diff-MomentFormer can learn more robust multimodal representations for accurate and stable joint moment estimation. Extensive experiments on a public lower-limb biomechanics dataset show that the proposed method consistently improves hip and knee joint moment estimation performance across different activity categories, while ablation studies further confirm the effectiveness of the proposed framework. Full article

Wearable lower-limb exoskeletons can enhance mobility, reduce metabolic cost, and aid rehabilitation. Effective human-exo cooperation requires personalized assistance profiles that match biomechanical principles. Existing methods often rely on fixed curves, involve complex tuning, and lack biomechanical interpretability. To address this, we propose a “Physics-guided perception and physiology-driven optimization” approach. First, a Physics-guided Dynamic Fusion Model (PDFM) is proposed, which integrates Newton–Euler dynamics, LSTM, and NTM to estimate multi-plane hip joint moments without ground reaction forces, employing biomechanical models as complementary fusion factors rather than the embedded hard constraints used in conventional physics-informed neural networks (PINNs). The model achieved correlation coefficients of 0.938, 0.924, and 0.929, and relative root mean square error (rRMSE) values of 5.29%, 9.79%, and 5.61%, in the sagittal, coronal, and transverse planes, respectively. These results outperformed all single-network baselines across all three anatomical planes. Second, an assistance profile derived from estimated moments is individually optimized using Bayesian optimization based on multi-muscle sEMG. Compared to no-exo walking, the optimized system reduced target muscle loading by 49.31% and metabolic cost by 14.75%; relative to the pre-optimized profile, the reductions were 23.64% and 5.74%, respectively. This work provides a laboratory-validated framework for personalized hip exoskeleton assistance in healthy adults, establishing a foundation for future clinical translation. Full article

In this paper, an adaptive-weight model predictive control (AW-MPC) strategy is proposed to address the trajectory tracking problem of a lower-limb rehabilitation exoskeleton robot. First, based on human motion analysis, the dynamics of the lower-limb rehabilitation exoskeleton are established, and the nonlinear dynamic model is transformed into a linear model. Second, a MPC objective function is formulated to minimize the tracking error, yielding the optimal control input. Then, on the basis of conventional MPC, a weight-tuning scheme is developed: a weighting function is constructed according to the evolution of the tracking error to adaptively adjust the MPC weighting coefficients, and the closed-loop stability of the control system is proven via a Lyapunov-based analysis. Finally, the proposed method is validated on a lower-limb rehabilitation exoskeleton experimental platform, with a PID controller designed as a baseline for comparison. The experimental results demonstrate that, compared with the PID controller, the proposed AW-MPC achieves faster convergence of the tracking error, higher tracking accuracy, and enhanced robustness. Full article

This study develops a reduced-order predictive model of the Sit-To-Stand (STS) task to examine whether a simplified biomechanical representation can reproduce key STS patterns reported in the literature and to investigate the role played in movement by a flexible trunk. The model represents the human body as a planar multibody system and formulates STS as an optimization problem within a discrete mechanics framework. This formulation combines reduced model complexity, explicit torso flexibility, and a structure-preserving numerical approach for trajectory generation. Simulations were used to evaluate the effects of movement duration, reduced joint strength, and seat height on joint torques, kinematics, trunk motion, and ground reaction forces (GRFs). The results reproduced several qualitative trends reported in previous experimental studies, including increased peak joint torques and GRFs with shorter movement duration, lower joint strength, and reduced seat height, as well as greater compensatory trunk motion under more demanding conditions. These findings suggest that the proposed framework captures key adaptive features of STS mechanics and may provide useful insights for rehabilitation analysis and the design of assistive technologies such as lower-limb exoskeletons and rehabilitation devices. At the same time, the present work should be regarded as an initial methodological study, since validation is currently qualitative and further experimental calibration, quantitative validation, and sensitivity analysis remain part of ongoing work. Full article

Transmission tower climbing is a critical specialized operation in ultra-high-voltage power maintenance and communication infrastructure servicing. However, existing lower-limb exoskeletons used for tower climbing still suffer from insufficient motion intention recognition accuracy under complex operational environments. To address this issue, this study proposes an inertial measurement unit (IMU)-based bidirectional temporal deep learning method for motion intention recognition. First, a one-dimensional convolutional neural network (1D-CNN) is employed to extract local temporal features from multi-channel IMU signals. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) is introduced to model the forward and backward temporal dependencies of motion sequences. Furthermore, a temporal attention mechanism is incorporated to emphasize discriminative features at critical movement phases, enabling the precise recognition of short-duration and transitional motions. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches and unidirectional temporal models in terms of accuracy, F1-score, and other evaluation metrics. In particular, this method demonstrates significant advantages in identifying the flexion/extension phases and transitional states. This study provides an offline method for analyzing movement intentions in lower-limb exoskeleton control for power transmission tower climbing scenarios and offers a reference for developing assistive control strategies for assisted climbing tasks in this specific context. Full article

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit–Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies—PD control, PD with gravity compensation, and the computed torque method—are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4–1.45 m/s, validating the effectiveness of the proposed model and control strategies. Full article

Lower-limb rehabilitation exoskeletons must balance biomechanical compatibility, structural safety, and low mass to enable practical, repeatable gait assistance. This paper proposes a planar pantograph-derived exoskeleton leg driven by a Chebyshev Lambda linkage and develops an integrated workflow from mechanism synthesis to manufacturable optimization and experimental verification. A mannequin-coupled multibody model was built in MSC ADAMS to evaluate joint kinematics, end-point (foot) trajectories, and joint reaction forces under multiple scenarios (fixed-frame, ramp, stair ascent, and inclined-plane walking). The extracted joint loads were transferred to a parametric finite element model in ANSYS Workbench 2019, where response surface surrogates and a multi-objective genetic algorithm (MOGA) were used to minimize mass under stiffness and strength constraints. For the optimized load-bearing link, the selected minimum-mass design reached a component mass of 0.542 kg while respecting the imposed structural limits, i.e., a maximum total deformation below 0.2 mm and a maximum equivalent (von Mises) stress below 50 MPa (e.g., ~0.188 mm deformation and ~39 MPa stress in the optimal candidate). A rapid prototype was manufactured by 3D printing and experimentally evaluated using CONTEMPLAS high-speed video tracking, providing measured X_M(t) and Y_M(t) trajectories and joint-angle histories for quantitative comparison with simulations via RMSE metrics. Full article

For safe and efficient human–robot interaction, lower-limb exoskeletons used for assistance and rehabilitation need to be precisely and energy-efficiently controlled. By creating an adaptive neuro-fuzzy fractional-order PID (ANFIS-FOPID) controller, this project seeks to improve tracking accuracy, robustness, and energy efficiency in a two-degree-of-freedom hip–knee exoskeleton. The Euler–Lagrange formulation is used to derive a nonlinear dynamic model, and a Lyapunov-based stability analysis is used to show that the closed-loop system remains uniformly ultimately bounded under disturbances and parameter uncertainties. The suggested controller performs noticeably better than traditional PID and fixed-parameter FOPID controllers, according to numerical simulations conducted under both normal and perturbed conditions. The ANFIS FOPID achieves root mean square errors below 0.028 rad and lowers the integral absolute errors at the hip and knee joints to 0.1454 and 0.1480, as opposed to 0.3496–0.3712 for PID controllers. Under ±10% parameter uncertainty, the total control-energy proxy drops from 2870.0 (PID) to 936.25, a 67.4% decrease, and stays at 1587.93. Statistically significant variations in energy consumption are confirmed by one-way ANOVA (p < 10⁻¹⁷⁶). Large effect sizes are found (η² = 0.237–0.314). These results demonstrate the superior tracking performance, robustness, and energy efficiency of the ANFIS-FOPID controller. The results set a quantitative standard for future experimental validation and hardware-in-the-loop implementation, despite being based on high-fidelity simulations. Full article

This study identifies electromyography (EMG) features as an alternative to metabolic cost for distinguishing varying levels of movement effort. Data from two experiments was used to analyze the performance of 50 EMG-based features. The first experiment, the Load experiment, involved participants walking with and without carrying loads of 2, 4, and 8 kg, and the second, the Exo experiment, had participants walking with and without varying levels of hip exoskeleton assistance. In the Load experiment, amplitude-based features generally performed well, with Waveform Length (WL) emerging as the top-performing feature achieving a detection rate of 77% when distinguishing between loaded and unloaded conditions in the most challenging 2 kg condition. In contrast, in the Exo experiment, where both increases and decreases in EMG were observed throughout the stride, it failed and mean-based as well as variance-based features performed best and effectively captured fluctuations in muscle activation with a detection rate of up to 71%. This study underscores the importance of selecting EMG features tailored to specific movement tasks and highlights the potential benefits of noise management strategies to improve detection performance for varying levels of movement effort, providing a foundation for EMG-based human-in-the-loop optimization of lower limb exoskeletons. Full article

Ensuring user safety while enabling independent mobility is crucial to autonomous healthcare and rehabilitation robots, such as active lower-limb orthoses and exoskeletons. A key requirement for these devices is to provide full assistance without supervision; however, existing designs do not simultaneously satisfy autonomous operation and inherent safety. To address this gap, a novel safety principle, Safety by Design, and a corresponding system architecture for a fully assistive active knee orthosis are introduced. The proposed architecture is based on a comprehensive risk analysis for the use of active orthoses and exoskeletons and integrates redundancies for all safety-critical components while minimizing additional weight. This redundancy enables the orthosis to remain operational at reduced power in the event of component failure, improving both user safety and system reliability. The design supports safe, unsupervised operation by ambulatory users, enhancing independent patient mobility and the performance of the gait activities of level walking, stair climbing and sitting down/standing up. The proposed architecture is scalable and adaptable to a wide range of robotic devices. By improving robustness, efficiency, and safety, this work contributes to the advancement of autonomous biomedical robotic systems and wearable assistive devices. Full article

Lower-limb exoskeletons require accurate dynamic models to achieve stable and compliant human–robot interactions. However, least-squares-based identification often relies on demanding experiments and may yield limited accuracy for exoskeletons with non-standard structures and actuator-induced uncertainties. This paper proposes a two-stage dynamic parameter identification method that integrates recursive least squares (RLS) and a genetic algorithm (GA), denoted as RLS-GA. RLS is first executed offline to estimate the variation ranges of the inertial parameter vector and to construct a finite, physically meaningful search space. GA then refines the parameters within these bounds by minimizing the regression residual norm. Experiments on a hip exoskeleton show that RLS-GA achieves higher identification accuracy than LS and unconstrained GA, while converging faster than GA under identical conditions. Full article

Most pediatric exoskeletons for cerebral palsy (CP) focus on lower-limb assistance and neglect trunk control, limiting rehabilitation outcomes. This study presents a self-aligning trunk–limb exoskeleton that integrates trunk stabilization with active lower-limb support. The design includes a hip–waist rapid adjustment mechanism, a bioinspired gear-rolling knee joint, modular thigh–shank structures, a trunk support module, and a body-weight support device. To enable transparent and coordinated assistance under pathological gait conditions, a continuous gait progress-based multi-joint control framework is developed. Joint motion is described as continuous gait progress over the full gait cycle (0–100%), and joint-specific progress estimates are fused into a unified system-level reference using observability-weighted circular statistics. Inter-joint coordination is achieved through phase-consistency-based temporal modulation implemented, enabling smooth synchronization while preserving joint-level autonomy and motion continuity. Technical evaluation—comprising kinematic misalignment analysis, simulation validation, and gait trials—demonstrated a 66.8% reduction in hip misalignment and an 87.4% reduction in knee misalignment. Gait parameters under exoskeleton-assisted walking closely matched baseline walking, confirming natural kinematic preservation without interference. These results indicate that the proposed trunk–limb exoskeleton improves human–robot synergy, enhances postural stability, and provides a promising solution for pediatric gait rehabilitation in CP. Full article