Search Results (628)

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

Exoskeletons are increasingly used in industrial settings, yet most are designed for structured, repetitive tasks, limiting adaptability to dynamic movements. In construction, frequent locomotion tasks demand continuous lower-limb engagement, and ladder climbing places substantial loads on coordination and flexibility. This study aimed to identify key muscles involved in climbing to support the development of adaptive exoskeletons. Ten healthy male participants (33.8 ± 3.4 years; 178.7 ± 5.0 cm; 87.4 ± 16.1 kg) performed vertical and A-frame ladder ascents in a controlled laboratory setting. Surface electromyography was recorded from eight right-leg muscles and processed using band-pass filtering, rectification, and root mean square smoothing. Two normalization strategies were applied: walking normalization, expressing climbing activity relative to level walking, and maximum voluntary contraction normalization, with amplitudes expressed as a percentage of maximum voluntary contraction. Our results showed that all muscles were more active in climbing than walking, with quadriceps (vastus medialis, vastus lateralis, rectus femoris) exhibiting the greatest increases. Gastrocnemius also approached or exceeded 100%MVC, tibialis anterior averaged 70–80%MVC, and hamstrings contributed 20–40%MVC mainly for stabilization. Vertical and A-frame ladders followed similar patterns with subtle posture-related variations. These findings highlight knee extensors as primary targets for adaptive exoskeleton assistance during ladder climbing tasks commonly performed on construction sites. Full article

Hand rehabilitation in neurologic conditions such as stroke and cerebral palsy traditionally emphasizes repetitive task practice with visually observable feedback, despite motor impairment arising largely from abnormal neuromuscular activation. We present a platform that leverages noninvasive measurements of brain and muscle activity for neurofeedback-guided movement training. Trainees first learn to control EEG during movement preparation, followed by reciprocal control of finger muscle EMG during exoskeleton-assisted movement. We describe the platform design and two feasibility studies. Five neurotypical individuals learned to use EEG and EMG to drive an exoskeleton to grasp and release a virtual ball in a single session. They achieved a mean success rate of 65%, demonstrating improved movement latency (9%) and task completion time (6%) across the session. One individual post-stroke trained with the platform across eight sessions and exhibited improvements on the Box and Blocks Test, the Action Research Arm Test, and the Wolf Motor Function Test. These results demonstrate the feasibility of multi-level, neurofeedback training that targets neural activation throughout movement, rather than movement outcome alone. By explicitly engaging both cortical and muscular control signals, this paradigm offers a promising new direction for hand rehabilitation following neurologic injury. Full article

Exoskeleton knee-assistance (EKA) systems are wearable robotic technologies designed to rehabilitate and improve impaired mobility of the lower limbs. Clinical exercises are conducted on disabled patients based on physically demanding tasks which are prescribed by expert physicians. In order to carry out good tracking of the desired tasks, efficient controllers must be designed. In this study, a novel control framework is introduced to improve the robustness characteristics and tracking precision of EKA systems. The control approach integrates a super-twisting sliding mode controller (STSMC) with a nonlinear disturbance observer (NDO) to ensure robust and precise tracking of the knee joint trajectory. An evaluation of the proposed system is conducted through numerical simulations under the influence of external disturbances. The findings reveal considerable enhancements to trajectory tracking accuracy and disturbance rejection when compared to conventional STSMCs and sliding mode perturbation observer (SMPO)-based STSMCs. Full article

Against the backdrop of intensifying global population aging, lower-limb exoskeleton robots serve as core devices for rehabilitation and power assistance. Their control accuracy and motion smoothness rely on precise dynamic models. However, parameter uncertainties caused by variations in human lower limbs, assembly errors, and wear pose a critical bottleneck for accurate modeling. Aiming to achieve high-precision dynamic modeling for a two-degree-of-freedom lower-limb exoskeleton, this paper proposes a parameter identification method named Tent-GA-GWO. A dynamic model incorporating joint friction and link inertia was constructed and linearized. An excitation trajectory based on Fourier series, conforming to human physiological constraints, was designed. To enhance algorithm performance, Tent chaotic mapping was employed to optimize population initialization, a nonlinear control parameter was used to balance search behavior, and genetic algorithm operators were integrated to increase population diversity. Simulation results show that, compared to the traditional GWO algorithm, Tent-GA-GWO improved convergence efficiency by 32.1% and reduced the fitness value by 0.26%, demonstrating superior identification accuracy over algorithms such as GA and LIL-GWO. Validation on a physical prototype indicated a close agreement between the computed torque based on the identified parameters and the actual output torque, confirming the method’s effectiveness and engineering feasibility. This work provides support for precise control of exoskeletons. Full article

The exoskeleton rehabilitation robot is a structural robot that uses the actuator to control, so as to construct a human–robot collaborative rehabilitation training system to realize the perception and decoding of patients and promotes the recovery of limb function and neural remodeling. This review focused on the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. This review systematically retrieved literature related to the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. Publications from 2011 to 2025 were searched for across the EI, IEEE Xplore, PubMed, and Web of Science databases. The included studies mainly covered the period from 2018 to 2025, reflecting recent technological progress. This article summarizes the collaborative progress of physical and informational interaction in exoskeleton rehabilitation robots. The physical and information interaction is manifested in the bionic structure, physiological information detection and information processing technology to identify human movement intention. The bionic structural design is fundamental to realize natural coordination between human and robot to improve the following of movements. The active participation and movement intention recognition accuracy are enhanced based on multimodal physiological signal detection and information processing technology, which provides a clear direction for the development of intelligent rehabilitation technology. Full article

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

With an aging population and the high incidence of neurological diseases, rehabilitative lower limb exoskeleton robots, as a wearable assistance device, present important application prospects in gait training and human function recovery. As the core of human–computer interaction, control strategy directly determines the exoskeleton’s ability to perceive and respond to human movement intentions. This paper focuses on the control strategies of rehabilitative lower limb exoskeleton robots. Based on the typical hierarchical control architecture of “perception–decision–execution,” it systematically reviews recent research progress centered around four typical control tasks: trajectory reproduction, motion following, Assist-As-Needed (AAN), and motion intention prediction. It emphasizes analyzing the core mechanisms, applicable scenarios, and technical characteristics of different control strategies. Furthermore, from the perspectives of drive system and control coupling, multi-source perception, and the universality and individual adaptability of control algorithms, it summarizes the key challenges and common technical constraints currently faced by control strategies. This article innovatively separates the end-effector control strategy from the hardware implementation to provide support for a universal control framework for exoskeletons. Full article

This study presents a small-scale hip exoskeleton incorporating bi-directional artificial muscles constructed with springs of Shape Memory Alloy (SMA). The prototype can effectively support hip motion in both extension and flexion, spanning an angular range of $-{20}^{°}$ to ${200}^{°}$. Experiments for thermo-mechanical characterization were executed to assess the performance of the SMA muscles throughout the entire motion range. The outcomes not only confirmed the suitability of the SMA muscles for the designed exoskeleton but also provided valuable insights into their behavior and capabilities. System identification experiments were carried out to establish an accurate transfer function, guiding the tuning of Proportional-Integral-Derivative (PID) controllers for enhanced motion-control effectiveness. The safety of the SMA system was addressed with a focus on preventing overheating. Challenges in accurately measuring the temperature of a thin spring were overcome by utilizing two thermocouples for each SMA springs group. Additionally, conventional SMA temperature measurement methods, such as infrared and resistance-based techniques, are limited by high cost, nonlinearity, and small range. This study presents a model-based temperature estimation algorithm that integrates a heat transfer model, electrical input data, and thermocouple data to enable accurate and real-time SMA temperature estimation without additional sensors, offering a cost-effective and reliable alternative. To evaluate the small hip prototype and its controller capabilities, control experiments were executed for both stepping and sinusoidal trajectories. The exoskeleton successfully tracked the desired trajectories, showing its precision. Moreover, system performance under adaptive control was further investigated, revealing an RMSE of $0.{94}^{°}$ in sinusoidal trajectory experiments, indicating reliable disturbance rejection in the angle measurements. Full article

Human gait exhibits stable contralateral coupling, making healthy-side motion a viable predictor for affected-limb kinematics. Leveraging this property, this study develops FusionTCN–Attention, a causality-preserving temporal model designed to forecast contralateral hip and knee trajectories from unilateral IMU measurements. The model integrates dilated temporal convolutions with a lightweight attention mechanism to enhance feature representation while maintaining strict real-time causality. Evaluated on twenty-one subjects, the method achieves hip and knee RMSEs of 5.71° and 7.43°, correlation coefficients over 0.9, and a deterministic phase lag of 14.56 ms, consistently outperforming conventional sequence models including Seq2Seq and causal Transformers. These results demonstrate that unilateral IMU sensing supports low-latency, stable prediction, thereby establishing a control-oriented methodological basis for unilateral prediction as a necessary engineering prerequisite for future hemiparetic exoskeleton applications. Full article

This paper presents the design, prototype, and experimental evaluation of the AlmatyExoElbow, a lightweight cable-driven robotic exoskeleton that is intended to support elbow joint rehabilitation. The device provides two active degrees of freedom for flexion/extension and pronation/supination. It also incorporates a sensor-based control system for accurate motion tracking. The mechanical structure is fabricated using 3D-printed PLA plastic, resulting in a compact, modular, and comfortable design suitable for prolonged use. The control architecture is based on an Arduino Nano microcontroller integrated with IMU sensors, enabling the real-time monitoring of elbow motion and the precise reproduction of physiologically relevant movement patterns. The results of experimental testing demonstrate smooth and stable operation, confirming reliable torque transmission through antagonistic cable mechanisms. Overall, the proposed design achieves a balanced combination of functionality, portability, and user comfort, highlighting its potential for upper-limb rehabilitation applications in both clinical and home-based settings. Full article

Lower-limb exoskeletons have become an effective tool for gait rehabilitation by enabling precise and repetitive joint movements for individuals with motor impairments. Nevertheless, the nonlinear and uncertain nature of human–robot interaction dynamics requires effective control strategies that are both robust and smooth. Conventional sliding mode control (SMC) provides robustness against disturbances but, in effect, is prone to chattering, which can adversely cause mechanical vibrations and reduce user comfort. This paper proposes a novel hybrid sliding mode control integrated with integral resonant control (SMC + IRC), strategy addressing a gap in 3-DOF exoskeleton control where structural resonance and chattering mitigation are simultaneously required while maintaining robustness and trajectory accuracy. The IRC component in this work uses a resonant damping mechanism to filter high-frequency switching elements in the SMC signal, resulting in smoother actuator torques without compromising system stability, robustness or responsiveness. The proposed control framework here is implemented on a lower-limb exoskeleton with hip, knee, and ankle joints and compared to classical SMC and Super-Twisting SMC (STSMC) methods. Upon simulation, results showed that the SMC + IRC approach significantly reduces chattering as well as produces smoother torque profiles while maintaining high tracking precision. Quantitative analyses using RMSE and chattering index metrics prove the superior performance of the proposed controller over the previous ones, establishing it as a practical and effective solution for safe and comfortable rehabilitation motion in real-time exoskeleton systems. Full article

Lower limb exoskeleton systems require accurate recognition of the wearer’s movement intentions prior to action execution in order to achieve natural and smooth human–machine interaction. Surface electromyography (sEMG) signals can reflect neural activation of muscles before movement onset, making them a key physiological source for movement intention recognition. To improve sEMG-based recognition performance, this study proposes an innovative deep learning framework, IFIANet. First, a CNN–TCN-based spatiotemporal feature learning network is constructed, which efficiently models and represents multi-scale temporal–frequency features while effectively reducing model parameter complexity. Second, an IFIA (Frequency-Informed Integration Attention) module is designed to incorporate global frequency information, compensating for frequency components potentially lost during time–frequency transformations, thereby enhancing the discriminability and robustness of temporal–frequency features. Extensive ablation and comparative experiments on the publicly available MyPredict1 dataset demonstrate that the proposed framework maintains stable performance across different prediction times and achieves over 82% average recognition accuracy in within-experiments involving nine participants. The results indicate that IFIANet effectively fuses local temporal–frequency features with global frequency priors, providing an efficient and reliable approach for sEMG-based movement intention recognition and intelligent control of exoskeleton systems. Full article

Background: Recovery following incomplete spinal cord injury (iSCI) remains challenging, with conventional rehabilitation often emphasizing compensation over functional restoration. As most new spinal cord injury cases preserve some motor or sensory pathways, there is increasing interest in therapies that harness neuroplasticity. Robotic exoskeletons provide a promising means to deliver task-specific, repetitive gait training that may promote adaptive neural reorganization. This feasibility study investigates the feasibility, safety, and short-term effects of exoskeleton-assisted walking in individuals with degenerative iSCI. Methods: Two cooperative male patients (patients A and B) with degenerative iSCI (AIS C, neurological level L1) participated in a four-week intervention consisting of one hour of neuromotor physiotherapy followed by one hour of exoskeleton-assisted gait training, three times per week. Functional performance was assessed using the 10-Meter Walk Test, while gait quality was examined through spatiotemporal gait analysis. Vendor-generated surface electromyography (sEMG) plots were available only for qualitative description. Results: Patient A demonstrated a clinically meaningful increase in walking speed (+0.15 m/s). Spatiotemporal parameters showed mixed and non-uniform changes, including longer cycle, stance, and swing times, which reflect a slower stepping pattern rather than improved efficiency or coordination. Patient B showed a stable walking speed (+0.03 m/s) and persistent gait asymmetries. Qualitative sEMG plots are presented descriptively but cannot support interpretations of muscle recruitment patterns or neuromuscular changes. Conclusions: In this exploratory study, exoskeleton-assisted gait training was feasible and well tolerated when combined with conventional physiotherapy. However, observed changes were heterogeneous and do not allow causal or mechanistic interpretation related to neuromuscular control, muscle recruitment, or device-specific effects. These findings highlight substantial inter-individual variability and underscore the need for larger controlled studies to identify predictors of response and optimize rehabilitation protocols. Full article