Search Results (627)

Conventional gel electrodes are the gold standard for surface electromyography (sEMG), yet their bulkiness, stiffness, and limited gel lifetime prevents seamless day-long integration with wearable robots. We integrated ultrathin skin-conformal temporary tattoo electrodes with a powered unilateral hip exoskeleton and compared signal quality during treadmill walking against gel. In this pilot study, five healthy participants completed three consecutive walking blocks at fixed speed: (1) using gel electrodes; (2) using tattoo electrodes to compare signal quality; and (3) using the same tattoo electrodes (not repositioned) after eight hours of wear to simulate a full day of typical device use and to evaluate potential degradation in signal quality over time. Electrodes were positioned on muscles not covered by the exoskeleton interface (tibialis anterior and gastrocnemius medialis), as well as on muscles located beneath the exoskeleton cuff, which were potentially subject to motion artifacts due to the application of external forces by the exoskeleton (rectus femoris and biceps femoris, BF). Across all muscles, for both gel and tattoo electrodes, the root mean square error (RMSE) between normalized sEMG envelopes and biological activation profile was 0.069 ± 0.048, and Pearson’s correlation coefficient (ρ) was 0.844 ± 0.091. Re-testing the same tattoo electrode pair after eight hours confirmed day-long stability without the need for recalibration. Statistical analysis revealed no significant differences in signal quality, also when applying assistive forces, between the two electrode types and across all muscles (RMSE, all p ≥ 0.3125; ρ, all p ≥ 0.1250), as well as no degradation after eight hours (RMSE and ρ: all p ≥ 0.0626, uncorrected). Finally, in a proof-of-concept session, BF activity measured with tattoo electrodes was found reliable to drive hip-extension assistance in real time. Collectively, these results show that tattoo electrodes deliver signal quality comparable to gel electrodes while offering a low-profile skin-conformal interface and day-long usability, making them a promising option for enhancing EMG-based control in wearable robots. Full article

With the rapid advancement of exoskeletons and rehabilitation robotics, modern healthcare increasingly demands high dynamic accuracy and reliability from medical devices. However, the dynamic response and durability of mechanical systems are greatly influenced by the inevitable existence of clearances in kinematic joints. Existing studies predominantly focus on simplified planar or spatial mechanisms, offering limited guidance for complex mechanical structures in medical applications. To address this issue, a unified modeling framework is proposed in this study to explore the nonlinear dynamic behavior and wear properties of bionic humanoid rigid mechanisms incorporating revolute joint clearances. A dynamic model that accounts for revolute joint clearances is established, employing the Lankarani–Nikravesh contact model alongside a refined Coulomb friction approach to characterize contact behavior. To characterize the wear progression between the shaft and the bushing, the Archard wear model is employed, while the system’s dynamic equations are formulated using the Lagrange multiplier approach. Systematic simulations are conducted to examine the effects of clearance size, location, and multi-clearance coupling on dynamic response and wear behavior. The results reveal that clearances at the hip joint have the most pronounced impact on system performance, tibiofemoral joint clearances exacerbate precision disturbances, and foot-end clearances considerably undermine system robustness. Increased clearance sizes and the coexistence of multiple clearances aggravate wear and induce more severe nonlinear dynamic phenomena. Phase portraits and Poincaré maps further illustrate that the system may exhibit complex or chaotic behavior under certain conditions. This study offers theoretical insights into performance degradation mechanisms in humanoid robots with joint clearances and introduces a modular “driving–mid–terminal” structure that enhances model generality, enabling its application to exoskeletons and rehabilitation devices for design optimization, service life prediction, and health monitoring. 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

Rehabilitation robotics faces the challenges of aligning engineering design with patient-specific needs. Most existing controllers in rehabilitation robots often constrain motion to fixed paths or provide only passive guidance, limiting user engagement and adaptability. This study proposes a novel active-assistive mode controller that integrates a virtual tunnel-based force generation mechanism with a dynamic moving-window technique for tracking activities of daily living (ADL) trajectories. Unlike conventional impedance controllers, the proposed method dynamically adjusts the virtual tunnel in real time, permitting voluntary upper-limb movement within a safe operational range while preventing excessive deviation. The system was implemented on a wearable two-degree-of-freedom (DOF) upper-limb exoskeleton equipped with drive and integrated sensor units. Experimental results demonstrated that decreasing the guidance force ($F_{gf}$) increased tracking errors, from $1°$ at 100% $F_{gf}$ to $5°$ at 30% $F_{gf}$, indicating greater voluntary participant motion. Peak actuator torques correspondingly decreased from 14.75 to 13.43 Nm (elbow) and from 4.14 to 2.48 Nm (wrist), confirming the controller’s capability to modulate robotic assistance according to user effort. Tests with 30 healthy participants demonstrated the effectiveness of guidance along predefined ADL trajectories, validating the controller’s potential for patient-centered rehabilitation. 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

Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management. Full article

The flexible upper-limb exoskeleton robot (exosuit) is composed of fabrics, soft actuators and compliant force-transmitting structures, which provides assistance or rehabilitation training for the shoulders, elbows, wrists and hands. By realizing human–robot collaboration, this kind of system has the advantages of comfort, light weight and portability, thus promoting motor function recovery and neural plasticity. This review establishes a classification and comparison framework for flexible upper-limb exoskeletons based on the actuation modalities and systematically summarizes the research progress under different actuation modalities. The relevant literature published from 2015 to 2025 was retrieved from the EI, IEEE Xplore, PubMed and Web of Science databases. After screening according to the preset inclusion and exclusion criteria, a total of 64 original research papers meeting the criteria were finally included for analysis. According to the actuation modalities, the flexible upper-limb exoskeleton robot is classified, and all kinds of systems are summarized and compared. Motor–cable/tendon actuation and pneumatic/hydraulic actuation have advanced substantially and are approaching technical maturity for flexible upper-limb exoskeletons. Meanwhile, designs based on passive/hybrid mechanisms (e.g., elastic energy storage elements and clutches) and new intelligent material actuations are showing a diversified development trend. In the future, the development is expected to further focus on lightweight and compliance, and by integrating multimodal sensing and feedback control, motion intention recognition and human–robot interaction theories, actuation systems will be developed towards modularization, intelligence and high-power density, in order to achieve more comfortable, lighter and more effective flexible upper-limb exoskeleton systems. Full article

Contemporary physiotherapy requires technological tools to provide effective therapy to the increasing group of patients with neurological conditions, among others. This can be achieved with rehabilitation robots, which can also be exoskeletons—wearable devices that mobilize multiple joints with complex motions representing activities of daily living. To perform kinesiotherapy conveniently in home-like environments, the exoskeletons need to be relatively lightweight. The paper presents the methodology for decreasing the mass of the exoskeleton design with real-life data-driven simulations of motions, followed by multibody dynamics simulations, and finite element method (FEM) multistep optimization. The process includes sequential initial parametric optimization, topology optimization, and final parametric optimization. The steps are used to set initial dimensional and material parameters, extract new geometrical features, and adjust the final geometry dimensions of a new design. The presented case of the SmartEx-Home exoskeleton resulted in a total mass reduction of almost 50% for the main construction elements while meeting the criteria of the minimum safety factor and maximum internal stress and strain for all components. The final design was manufactured and tested with humans, reflecting an almost fully automatic passive and active therapy. Full article

Upper limb motor dysfunction resulting from stroke requires effective rehabilitation solutions; however, current exoskeletons are limited by single-input control, inadequate adaptation to various rehabilitation stages, and restriction to one limb. This study presents the development of a three-degree-of-freedom upper limb rehabilitation exoskeleton with three core innovations: (1) a neuro-fuzzy adaptive admittance control architecture that integrates human–robot interaction force and joint angular velocity as dual inputs for real-time damping adjustment, enabling accurate capture of dynamic movement intentions; (2) a Brunnstrom stage-specific fuzzy rule base that directly links clinical rehabilitation needs to adaptive control parameters; (3) a bilateral adaptable mechanical structure, allowing dual-upper limb training to enhance practical application. By combining radial basis function (RBF) neural network-based adaptive proportional–integral–derivative (PID) control with fuzzy variable-parameter admittance control, the system achieves a maximum trajectory tracking error of less than 1.2° and a root mean square (RMS) error of ≤0.13°. Trajectory tracing experiments confirm an RMS error of 2.99 mm for a circular trajectory at Bd = 2. The proposed strategy, validated through position tracking, admittance interaction, and trajectory tracing experiments, effectively balances tracking accuracy and human–machine compliance, providing valuable technical support for robot-assisted upper limb rehabilitation. 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

Background: Post-stroke pain (PSP), particularly shoulder pain, is frequent and often underdiagnosed, limiting rehabilitation adherence and functional recovery. Current pharmacological and physical treatments offer only partial relief. Robotic-assisted therapy (RAT), such as the gravity-supporting Armeo^® Spring exoskeleton, delivers intensive, task-specific training with visual 2D feedback that may also alleviate PSP while enhancing motor outcomes. This study investigates whether RAT performed with the Armeo^® Spring reduces upper-limb PSP in chronic stroke patients versus conventional therapy and evaluates its effects on motor function and functional independence. Methods: In this retrospective parallel group study, 32 chronic post-stroke patients (8 females and 24 males with a mean age of 57 ± 11.74) were allocated to two groups: 16 received upper-limb RAT with the Armeo^® Spring, a gravity-supporting exoskeleton, (RAT group) and 16 underwent conventional rehabilitation (CR). The RAT group completed one-hour sessions 6 days/week for 8 weeks, performing 2D/3D gamified tasks targeting shoulder, elbow and forearm movements. The CR group received an equivalent amount of standard therapy, including passive/active-assisted mobilization, Bobath-based neuromuscular facilitation and reaching exercises. Results: Both the Armeo^® Spring and conventional therapy groups showed significant reductions in post-stroke pain (RAT p < 0.001 and conventional rehabilitation p = 0.004) and improvements in upper-limb motor function and functional independence (both p ≤ 0.002). Spasticity in the impaired limb decreased modestly in the RAT group (p = 0.031), with no significant between-group differences in pain or spasticity change (p = 0.437; p > 0.05, respectively). Conclusions: Gravity-support exoskeleton training reduced upper-limb spasticity, and no statistically significant between-group differences were observed compared with conventional physiotherapy for pain, mobility, and functional independence. Although clinical outcomes improved, health-related quality-of-life domains showed heterogeneous trajectories, underscoring the complexity of perceived health changes during chronic stroke rehabilitation. Larger randomized controlled trials incorporating neurophysiological and kinematic endpoints and longer follow-up are warranted to confirm effectiveness, particularly in chronic stroke and durability. Full article

The translation of knowledge and methodologies across disciplines represents a valuable source of innovation, particularly in user-centered design approaches that have become essential in medical device development. This study explores the use of the PICO (Population, Intervention, Comparison, and Outcome) framework, a cornerstone of evidence-based medicine for formulating clinical questions, as a conceptual structure to support the alignment between clinical needs and engineering design consideration in robotic rehabilitation devices, with a focus on hand exoskeletons. Through a conceptual reinterpretation and application-oriented exploration supported by illustrative case studies involving both rigid and soft robotic glove prototypes, this study shows how each PICO component can inform engineering parameters, from defining user impairments and intervention strategies to benchmarking and outcome measurements. The analysis highlights the potential of PICO in fostering a user-centered design perspective and bridging clinical and engineering domains while also identifying its structural limitations when applied to device design contexts. This study concludes that while the PICO framework offers a valuable foundational structure, it requires customization to fully address the multifactorial requirements of effective, patient-specific robotic rehabilitation device design. Full article

Background and Objectives: This study was conducted to investigate the effects of robot-assisted gait rehabilitation approaches using commonly used end-effector, fixed exoskeleton, and wearable exoskeleton on gait and balance abilities in patients with early post-stroke (≤3 months). Materials and Methods: Sixty patients admitted to a rehabilitation center with confirmed stroke by a medicine specialist were assigned to three groups such as the end-effector group (EG 1), the fixed exoskeleton group (EG 2), and the wearable exoskeleton group (EG 3). The primary endpoint was pre-specified as the change in timed up-and-go gait test (TUG) from baseline to week 6, and all other outcomes were treated as secondary. The functional gait category (FAC), 10-m walk test (10MWT), six-minute walk test (6MWT), timed up-and-go gait test (TUG), dynamic gait index (DGI), and Berg Balance Scale (BBS) were measured at four time points (baseline, 2 weeks, 4 weeks, and 6 weeks). Results: A significant main effect of time was observed for all outcome variables, but neither the main effect of group nor the interaction between group and time was significant for any outcome variable. Within-group analyses revealed that FAC, 6MWT, DGI, and BBS increased over time in all groups, whereas 10MWT and TUG decreased. Conclusions: All three robot-assisted gait rehabilitation approaches in patients with early post-stroke were associated with significant improvements in gait and balance abilities over 6 weeks. However, statistically significant differential trajectories were not detected across robot types in this sample. Full article