A key issue in wearable robotics is the design of an exoskeleton robot with human-like motor capabilities to match the wearer's natural locomotion in daily life. It poses a challenge for an exoskeleton to replicate the sophisticated motor intelligence that enables humans to master a variety of agile motor skills. We herein propose a new design principle for lower limb exoskeletons that can transfer human motor intelligence to the robotic mechanical system and thereby endow the designed exoskeleton with natural locomotion capabilities. We first captured the synergistic characteristics among lower limb joints in human natural locomotion, and identified basic motor primitives (i.e., kinematic synergies). Then we established the mechanical design principle for exoskeletons capable of replicating the locomotor synergistic characteristics. Finally, we proposed the implementation of the kinematic synergies to ensure the compactness and lightweight of the exoskeleton. Experimental tests were conducted on a prototype exoskeleton to validate the effectiveness of the proposed design principle. The results confirmed that the proposed exoskeleton could assist users in completing a variety of locomotor tasks while exhibiting inherent characteristics of human locomotion. These findings demonstrate the potential of the design principle to advance the development of wearable exoskeletons for applications such as daily mobility assistance, post-stroke rehabilitation, and industrial load-carrying.

Investigating a novel 3D-printed electrical impedance tomography sensor for monitoring the interaction pressure on a customized physical interface in wearable robots

Electromyography (EMG) electrodes are critical for detecting and interpreting muscle activity, which is essential for operating prosthetic devices and wearable robots. Traditional EMG electrodes, however, often face limitations such as being uncomfortable, lacking stretchability, and wearing out quickly. To overcome these challenges, we developed an innovative EMG wristband with mesh electrodes created using charge-reverse electro writing (CREW). The wristband is tailored to fit the unique muscle distribution of the user, featuring a special fiber with a core/shell structure. The core, enriched with liquid metal nanoparticles (LM-NPs), ensures excellent electrical conductivity, while the thermoplastic polyurethane (TPU) shell enhances flexibility, durability, and washability. The wristband is also designed for long-term comfort, with a breathable 3D scaffold structure that allows natural skin ventilation. Even under maximum strain, it maintains high signal clarity, achieving a signal-to-noise ratio (SNR) of over 30 decibels. The signals are processed through a machine learning algorithm, the multilayer perceptron (MLP), with minimal delay time, enabling smooth and human-like motor movements in prosthetic devices. This breakthrough addresses key challenges in traditional electrodes, providing a reliable, high-performance solution for wearable robotics and assistive technologies, with a focus on comfort, durability, and seamless integration into everyday life.

Multifunctional nanomaterials, systems, and algorithms for neuromorphic computing applications: Autonomous systems and wearable robotics

The increasing aging population and the rise in sports injuries have led to greater demand for elbow function rehabilitation and daily assistance. To address the limitations of traditional rigid rehabilitation aids and existing flexible assistive systems, this paper designs a wearable elbow-assist robot that arranges pneumatic muscles based on the distribution of human elbow muscles. By integrating bionic design, experimental research, and mathematical modeling, the proposed approach determines the optimal scheme through comparative experiments on material structures and provides supporting data, while the mathematical model describes the force characteristics of the pneumatic muscles. Final experiments verify that the system can effectively assist elbow movement and significantly enhance flexion torque.

Abstract The lower limb exoskeleton is a typical wearable robot designed to assist human motion. However, its system stability and performance are often compromised due to unknown model parameters and inadequate control strategies. Therefore, it is crucial to explore the parametric identification of the exoskeleton and the design of corresponding control strategies for human-exoskeleton cooperative motion. First, an exoskeleton platform is developed to provide experimental validation. Simultaneously, a two-degree-of-freedom (2-DOF) exoskeleton model is constructed using the Lagrange method. The neighborhood field optimization (NFO) technique is then applied to identify the unknown model parameters of the exoskeleton. Additionally, the excitation trajectories for the exoskeleton are developed by the NFO method, incorporating several motion constraints to enhance the accuracy of model identification. An admittance controller is implemented to enable active control of the exoskeleton, allowing it to align with human intention and thereby improving the wearability and comfort of the device. Finally, both simulation and experimental results are compared and verified on the platform. These results demonstrate that the NFO method achieves superior identification accuracy compared to particle swarm optimization (PSO) and genetic algorithm (GA).

The motion of the human whole-body center of mass (CoM) supplies information that is useful for controlling wearable robots and studying important aspects of human movement, such as balance. However, most datasets do not use a whole-body markerset or provide the full 3D ground reactions forces (GRFs), both of which can be crucial for accurately estimating the CoM, or mainly focus on cyclic activities such as walking. This work contains the whole-body markerset and 3D GRF measurements of 10 participants performing 14 activities, 11 of which were derived from the Berg Balance Scale test. Additionally, we provide the results of inverse kinematics and dynamics analysis, as well as estimates of the CoM position and velocity. The information in this dataset enables study of human movement in novel scenarios, with possible applications such as quantifying balance and designing controllers for powered prosthetic legs.

Abstract Autonomous donning/doffing of physical attachments is critical for independent, efficient usage and acceptance of wearable robots. However, existing physical attachments consist of passive sleeves and straps that require user dexterity for donning, which limits independent usage by impaired populations. Thus, we introduce the first automatic limb attachment system consisting of soft actuated straps that wrap around the limb and lock using a magnet-and-hook latch. Our novel system achieves an average donning time of 27.7 ± 1.0 s reliably over ten consecutive cycles, accommodates different range of arm circumferences (220–294 mm), maintains closure for clinical-level mechanical loads (up to 80 N normal and 32 N shear), and precisely controls strapping pressure within the 15 kPa human comfort threshold over time. The system secures the limb while consistently regulating attachment. This work provides proof-of-concept for a practical, versatile solution towards universally applicable self-securing attachments limb for rehabilitation, occupational, and home-based robotic exoskeletons.

Tracking forearm movement via measured physiological signals is crucial for understanding human motor control mechanism. Current methods mainly use muscle-derived signals to predict arm movements while often overlooking the potential role of gaze attention, which is important for hand-eye coordination and instant and continuous motion planning and execution. In this study, we explored the impact of gaze on motion tracking. A hierarchical transformer-based structure was developed to integrate gaze into muscle activity signals for recovering the joint trajectory. To collect the dataset, six subjects were recruited to perform arm motions broadly involved in daily activities; the measured signals from the muscle activity and gaze attention were used to train and evaluate the proposed method. A performance comparison was conducted between the models using solely muscle activity signals and both muscle and gaze information. The experimental results showed the important role of gaze information involved in motion prediction and the motor control mechanism. This research also gained insights on how to integrate gaze information into the muscle signals, which offers an alternative to bringing artificial intelligence to be engaged in the framework of motion tracking. Consequently, it is important for future designs of biomechanical sensors and wearable robotics systems.

Multi-sensor fusion has emerged as a foundational enabling technology for modern electronic information acquisition systems, facilitating accurate, robust, and real-time environmental perception in complex operational scenarios. By combining complementary modalitiessuch as inertial, optical, radio-frequency and physiological sensorsfusion mitigates the limitations of single sensors with respect to noise, occlusion, drift and range. This paper surveys the conceptual levels of fusion (data-, feature- and decision-level) and typical hybrid designs, then reviews representative computational methods including Kalman filtering and its nonlinear variants, Bayesian inference and particle filtering[1], and deep learningbased multimodal models. Application domains are selected from four key fields: autonomous driving, healthcare and wearable monitoring, robotics and SLAM, and industrial condition monitoring, illustrating how fusion improves detection accuracy, continuity, and reliability. A concise comparative analysis is provided for different fusion strategies and algorithms, focusing on their performance in terms of information fidelity, latency, computational cost, and robustnessc. Open challengessensor synchronization, domain shift, explainability, privacy and securityare discussed together with emerging trends such as edge intelligence and collaborative sensing among distributed devices. The review aims to provide a practice-oriented map of methods and trade-offs to guide the design of electronic information acquisition systems that are safe, efficient, and scalable.

This study presents a wearable robot equipped with a novel anchoring structure designed to improve both force transmission efficiency and user comfort. While soft wearable robots are inherently more comfortable than rigid exoskeletons, they often suffer from significant force loss and increased anchoring pressure when higher assistive forces are applied. To address this trade-off, a chain-linking anchoring structure with anisotropic stiffness was developed and integrated into the wearable system. The structure is designed to exhibit high stiffness in regions critical for force transmission, minimizing deformation, while maintaining low stiffness in areas that require gentle deformation for wearer comfort. Experimental validation of the wearable robot demonstrated a 34.2% improvement in force transmission efficiency compared to conventional anchoring methods, without compromising comfort. These results highlight the effectiveness of the proposed robot in delivering strong assistance while remaining comfortable, offering a practical solution for the advancement of wearable assistive technologies.

Data-driven methods have transformed our ability to assess and respond to human movement with wearable robots, promising real-world rehabilitation and augmentation benefits. However, the proliferation of data-driven methods, with the associated demand for increased personalization and performance, requires vast quantities of high-quality, device-specific data. Procuring these data is often intractable because of resource and personnel costs. We propose a framework that overcomes data scarcity by leveraging simulated sensors from biomechanical models to form a stepping-stone domain through which easily accessible data can be translated into data-limited domains. We developed and optimized a deep domain adaptation network that replaces costly, device-specific, labeled data with open-source datasets and unlabeled exoskeleton data. Using our network, we trained a hip and knee joint moment estimator with performance comparable to a best-case model trained with a complete, device-specific dataset [incurring only an 11 to 20%, 0.019 to 0.028 newton-meters per kilogram (Nm/kg) increase in error for a semisupervised model and 20 to 44%, 0.033 to 0.062 Nm/kg for an unsupervised model]. Our network significantly outperformed counterpart networks without domain adaptation (which incurred errors of 36 to 45% semisupervised and 50 to 60% unsupervised). Deploying our models in the real-time control loop of a hip/knee exoskeleton (N=8) demonstrated estimator performance similar to offline results while augmenting user performance based on those estimated moments (9.5 to 14.6% metabolic cost reductions compared with no exoskeleton). Our framework enables researchers to train real-time deployable deep learning, task-agnostic models with limited or no access to labeled, device-specific data.

In this paper, we present the design, development, and initial testing of a prototype exoskeleton model created for augmenting human strength and mobility, specifically for upper-body weightlifting applications. This model, crafted to fit a small articulated doll (approximately 11.9% the size of an average human), using a PLA-constructed frame, an Arduino control system, and a rope-pulley mechanism for movement, this design represents a practical approach to wearable assistive robotics. While primarily a proof-of-concept, the project aims to explore foundational principles of wearable robotics and mechanical augmentation in a simplified manner. The proposed exoskeleton can be served as a robotic tool in demonstrating the interaction between mechanical design and control systems. The exoskeleton design also contributes to the field of wearable robotics by addressing the specific needs of weightlifters, offering a practical and functional solution to enhance strength, reduce fatigue, and minimize the risk of musculoskeletal injuries during weightlifting exercises. Preliminary results discuss feasibility and working efficiency of this model while providing insights into potential applications and improvements in larger-scale wearable robotics. The exoskeleton’s performance was assessed for vertical holding and lifting tasks, identifying challenges in torque, friction, and material choice, which inform future improvements in exoskeleton technology.

BackgroundParkinson’s disease (PD) is a neurodegenerative disorder characterized by progressive motor deficits and gait disturbances. While medication offers symptomatic relief, long-term complications and gradual functional decline remain significant challenges. Robot-assisted training provides intensive, task-specific motor rehabilitation and has shown promise in improving gait for PD patients. Soft wearable robot suits, designed with lightweight, flexible materials, offer enhanced comfort, adaptability, and biomechanical support compared to traditional robots. However, there is limited evidence regarding the effectiveness of hip extensor assistance with soft wearable robots for gait improvement in PD.MethodsThis is a prospective, single-center, single-blind, parallel-group study, and will recruit 34 PD patients. The participants will be assigned to either a robot or control group. Both groups will receive identical rehabilitation interventions, each session comprising 20-min of strength training, 5-min rest, and 20-min of treadmill walking. The rehabilitation program will be applied identically to all participants. The key difference between the groups will be whether participants wear the soft wearable robot suit during treadmill walking session. The intervention will be conducted 2 times per week, a total of 12 sessions for 6 weeks. The H-Medi (HUROTICS, Inc.), a cable-driven soft wearable robot suit will be utilized for the intervention and hip extensor assistance will be applied. For outcome measures, the following assessments will be performed at baseline (T0) and post-intervention (T1): Gait speed, Timed-Up and Go test, Short Physical Performance Battery, Berg Balance Scale, Movement Disorder Society-Unified Parkinson’s Disease Rating Scale, Freezing of Gait Questionnaire, gait parameters, muscle strength and endurance, quadriceps muscle thickness, body composition, cognition, and depression. The primary outcome will be the difference of gait speed from T0 to T1. The secondary outcomes will be the differences of other measures.DiscussionThis study will be the first to assess hip extensor assistance provided by a soft wearable robot suit as a targeted therapy for gait impairment in PD. Results are expected to clarify device usability, safety, and impact on gait. By focusing on hip extension, the findings may help advance personalized gait rehabilitation and inform the design and clinical adoption of future wearable robotic devices for PD.Clinical trial registrationKCT0010793.

A wearable exoskeletal lumbar spinal rehabilitation robot based on sliding mode control scheme

Soft wearable robots have gained widespread interest across various disciplines; however, they remain insufficient in overcoming the physical limitations of the human body. In particular, enhancing vertical jump height, a commonly used indicator of physical capability, requires improved actuator power density, stroke length, and soft structure efficiency. To address these challenges, the Jump‐Enhancing Textile Suit is proposed, which integrates the Pneumatic Energy‐Storing Propulsion Actuator (PESPA) and the Triarticular Kinetic‐Chained Structure (TKiCS) to assist jump performance. PESPA stores elastic energy under pneumatic pressure and releases it during the propulsive phase to augment human movement. TKiCS uses the kinetic chain mechanism to reduce anchoring points and fully harness the high stiffness region, thereby improving force transmission efficiency. Controlled vertical jump experiments with healthy adult participants are conducted. The suit increases jump height by 3.74 cm on average and up to 9.04 cm maximum, while also enhancing hip, knee, and ankle torques. Under isotonic testing, PESPA achieves a power density of 2298.69 W kg −1 and outperforms conventional pneumatic actuators. A dynamic model enables accurate force prediction and precise timing for effective assistance. These findings establish a practical foundation for pneumatic wearable robotics and suggest applications in jump augmentation, rehabilitation, and athletic performance.

Waist assistive exoskeleton is a wearable robot used to alleviate muscle fatigue and prevent lower back injury for workers during repetitive load lifting. Multi-joint human-robot coupling, the effect of human control and the model uncertainties like load variations make the robust controller design of waist assistive exoskeleton become challenged. Most existed control methods are based on a simplified dynamic model and neglect model uncertainties, which leads to a limited control performance. This paper focuses on the dynamic modeling and high-performance force control of waist assistive exoskeleton. In order to obtain a dynamic model which is accurate as well as suitable for controller design, a 5-DOF human-robot rigid body dynamics is established first. Then holonomic constraints are proposed to describe the control effect of the wearer, which helps convert the 5-DOF dynamics into a 1-DOF dynamics. Based on the established 1-DOF dynamics, adaptive robust force control strategy is proposed to effectively address various model uncertainties and disturbances. Comparative simulations and experiments indicate that the proposed control method can realize accurate and robust force control performance under different loads.

Soft robotics requires compliant actuators for safe human interaction. While McKibben artificial muscles are popular for their high force output, their reliance on bulky, noisy pumps limits their use in wearable devices. Electrohydrodynamic (EHD) pumps offer a compact and silent alternative, but existing designs struggle with dielectric discharge and fabrication issues, which compromise reliability and power density. This study introduces a novel EHD pump featuring 0.1 mm copper wire electrodes in a diagonal arrangement within a laser‐cut acrylic frame. This design improves dielectric resilience, minimizes deformation, and allows for compact integration. A new simplified fabrication process results in sample variation under 5%. The pump demonstrates remarkable performance, achieving 107 kPa pressure and an 88 mL min −1 flowrate, doubling the power density of the previous model while retaining 88% of its flowrate after 50 discharge events. An automated self‐recovery mechanism is also implemented, enabling the pump to instantly restore function after a discharge. When paired with a McKibben muscle, the system achieves a 2 s contraction time, a tenfold improvement over the prior EHD‐driven system. This work presents a significant advancement in fast, resilient, and scalable actuation, paving the way for next‐generation wearable robotics and assistive technologies.

The aim of this study is to assess the usability of the Myosuit within a chronic stroke survivors' rehabilitation program and to explore its therapeutic and assistive role on gait, stair negotiation, sit-to-stand transfers, and balance. Ten chronic stroke survivors with gait impairments were enrolled. The System Usability Scale (SUS) was the primary outcome of the study; secondary outcomes were the Stroke Self-efficacy Questionnaire (SSEQ), the Short Physical Performance Battery, the 10-meter Walking Test (10mWT), the 2-minute Walking Test (2minWT), and the Stair Climbing Test. Tests were carried out before (T0) and after (T1) the training sessions, with and without the exoskeleton. The SUS rated poor-to-ok in 30% of the participants, good in 40%, and excellent to best imaginable in 30%. Comparing T1 vs T0, all the functional tests, except stair descending, showed statistically significant improvements without the exoskeleton, and SSEQ did not change significantly. T1 vs T0 comparisons with the exoskeleton showed improvements in all functional tasks, statistically significant for all, except for 2minWT and 10mWT. This study confirmed the feasibility of a Myosuit-mediated treatment in a sample of chronic stroke survivors. Despite the usability of the wearable robot being generally positively perceived, it varied among users. Furthermore, the Myosuit exhibited both therapeutic and assistive potential in the sample.

In this review, we discuss electrical and optogenetic technologies for stimulating the spinal cord to improve movement after spinal cord injury (SCI). Paralysis or paresis following SCI severely impairs control and movement of the extremities. Restoring movement in the upper and lower extremities is a top priority for this population. Invasive and noninvasive electrical stimulation of the spinal cord can modulate the activity of spinal circuits, resulting in improvements in motor and sensory function. More recently, optogenetic stimulation has emerged as another technique capable of modulating spinal circuity to facilitate movement recovery in animal models. Recent studies are offering new insights into the effects of parameter selection, multisite stimulation, and the combined effects of stimulation and wearable robotic exoskeletons, all with the goal of restoring movement after SCI. Modulating the activity of the spinal cord via electrical and optogenetic stimulation is a promising intervention for improving movement after SCI. Future studies should determine optimal stimulation parameters, synergistic effects when combined with wearable robotics, and the safety of optogenetics in the human spinal cord. Such work will best position these emerging technologies for clinical translation.