Exoskeletons have been shown to effectively assist humans during steady locomotion. However, their effects on non-steady locomotion, characterized by nonlinear phase progression within a gait cycle, remain insufficiently explored, particularly across diverse activities. This work presents a shank angle-based control system that enables the exoskeleton to maintain real-time coordination with human gait, even under phase perturbations, while dynamically shaping assistance profiles to match the biological ankle moment patterns across walking, running, stair negotiation tasks. The control system consists of an assistance profile online generation method and a model-based feedforward control method. The assistance profile is formulated as a dual-Gaussian model with the shank angle as the independent variable. Leveraging only IMU measurements, the model parameters are updated online each stride to adapt to inter- and intra-individual biomechanical variability. The profile tracking control employs a human-exoskeleton kinematics and stiffness model as a feedforward component, reducing reliance on historical control data due to the lack of clear and consistent periodicity in non-steady locomotion. Three experiments were conducted using a lightweight soft exoskeleton with multiple subjects. The results validated the effectiveness of each individual method, demonstrated the robustness of the control system against gait perturbations across various activities, and revealed positive biomechanical and physiological responses of human users to the exoskeleton's mechanical assistance.

Wheel-legged robots integrate the adaptability of legged locomotion with the efficiency of wheeled movement, enabling agile traversal across diverse terrains. However, abrupt terrain transitions introduce substantial state variations, including velocity fluctuations, posture shifts, and slippage, which pose significant challenges to locomotion stability. To address these issues, we propose a state error compensation framework that integrates a residual network with a trust-region mechanism. The residual network implicitly captures nonlinear contact dynamics, enabling real-time correction of slippage-induced state deviations, while the trust-region mechanism regulates compensation amplitude to maintain stable locomotion. Furthermore, we introduce a dual-source contrastive learning strategy, which explicitly differentiates terrain-induced transitions from external perturbations, facilitating context-aware error recovery. The proposed framework is integrated into a model-free reinforcement learning pipeline, ensuring adaptability to previously unseen environments. To further enhance robustness, an uncertainty-aware calibration module is introduced. This module dynamically adjusts the trust region boundary in real time, leveraging sensory feedback to adaptively constrain residual corrections and prevent over-adjustment, thereby maintaining stability during diverse terrain transitions. Experimental results demonstrate that the proposed framework achieves a 96.7% terrain traversal success rate and 92% velocity tracking accuracy under dynamic disturbances. On unstructured and mixed terrains, it maintains a mean velocity tracking error of 0.15 m/s and stable posture, with pitch and roll angles constrained to ±0.04 rad and ±0.02 rad, respectively.

While rehabilitation exoskeletons have been extensively studied, systematic design principles for effectively addressing heterogeneous bilateral locomotion in hemiplegia patients are poorly understood. In this article, a multijoint lower exoskeleton driven by series elastic actuators (SEAs) is developed, and the design philosophy of rehabilitation robots for hemiplegia patients is systematically explored. The exoskeleton has six powered joints for both lower limbs in a hip–knee–ankle configuration, and each joint incorporates a custom, lightweight SEA module. A unified interaction-oriented control framework is designed for exoskeleton-assisted walking, including gait generation, task scheduling, and advanced joint-level control. The closed-loop design provides methodical solutions to address hemiplegia rehabilitation needs and provides walking assistance for bilateral lower limbs. Moreover, a multitemplate gait generation approach is proposed to address the altered kinematics induced by exoskeleton-assisted walking and enhance the exoskeleton's adaptability to patient-specific kinematic variations in an iterative manner. Experiments are conducted with both healthy individuals and hemiplegia patients to verify the effectiveness of the exoskeleton system. The clinical outcomes demonstrate that the exoskeleton can achieve mechanical transparency, facilitate movement, and enable coordinated interjoint locomotion for bilateral gait assistance.

Rehabilitation exoskeletons have been demonstrated to benefit mobility-limited patients; however, accessibility is hindered by several challenges, and interaction evaluation of coupled human–exoskeleton systems remains critically understudied. In this pioneering study“, assistive normality (AN)” is introduced to characterize the cross-stage spatiotemporal features of exoskeleton-assisted gait in hemiplegic patients, and a multidimensional, low-data-cost metric framework is developed to quantify AN. Three subdivided metrics, including gait restoration (GR), phase-deviation weighting (PDW) and multijoint coordination (MJC), are proposed to assess coupled system interaction behavior across different intervention stages during rehabilitation. A homologous difference evaluation paradigm (HDEP) is introduced to capture pathological differences between healthy and hemiplegic subjects on the basis of the reusability of experimental data, providing an approach for the nominal assistance calibration and assessment of specific exoskeleton devices. In a pilot study with eight healthy individuals and nine hemiplegic patients, between-group metric differences were analyzed to determine the calibrated AN of an exoskeleton. The results provide calibration references for quantitative metrics and demonstrate the ability of framework to characterize the temporal dynamics of human–exoskeleton interactions. The proposed AN framework offers a generalizable approach to assistive robotics, potentially enhancing evaluation of human-robot interaction and advancing clinical rehabilitation applications.

When applying exoskeletons for walking assistance, one important consideration is to ensure that the users retain full control over the exoskeleton-provided assistance, which is quite limited in existing exoskeletons due to the absence of a suitable control system. In this article, a time-independent exoskeleton control system is developed based on a novel assistance profile generation method and an iterative force control method to enable continuous assistance adjustment. The assistance profile is formulated as a Gaussian function with a human state variable and can be updated online to adapt to different users. The proposed profile continuously self-adjusts along the movement of the user's leg, especially when users change their walking patterns. The proposed control system iteratively compensates for the force control lag and amplitude attenuation to enable precise tracking of the assistance profile during natural human walking. Experiments have been conducted using a soft exoskeleton on subjects with and without prior experience using an exoskeleton. The experimental results have shown the effectiveness of the proposed control system compared with a common time-dependent control system.

Soft exoskeletons have demonstrated the potential to save energy, but their efficiency is sensitive to variations in human gait cadence. This work aims to develop adaptive gait state detection and iterative force control methods for a soft exoskeleton to reduce human walking metabolic cost consistently, while the user may change walking cadence. The proposed approach is motivated by the rhythmicity of gait and applies an iterative learning concept to enhance the exoskeleton’s adaptability to varying walking conditions. The gait state detection method proposed for the designed exoskeleton combines two feature extraction algorithms, which can learn from the present and past body kinematic data, to provide accurate user gait state detection. Based on the state, the proposed force control method iteratively adjusts the commands to keep track of the desired profile. Experiments have been conducted on healthy subjects walking with varying cadence using the soft exoskeleton. Promising results were presented in separate validation tests. Moreover, metabolic costs of subjects walking under one unpowered and two powered conditions, where the assistance profiles were produced by classical methods and the proposed methods, showed that the proposed methods can effectively improve the exoskeleton’s ability to save human energy of walking with varying cadence.Note to Practitioners—Lower limb exoskeletons have demonstrated the potential to save human energy in medical and industrial applications. The main purpose of this work is to solve the exoskeleton assistance efficiency loss problem for users walking with changing cadence. Constant cadence is unlikely maintained during natural human walking. Few existing exoskeletons could retain high efficiency under user cadence changes, limited by their control system capability. This work presents a new cable-driven cadence-insensitive soft exoskeleton, which is purposely designed with two adaptive methods to enable the device to offer consistent benefit to users walking with varying cadence. The proposed methods are inspired by the rhythmicity of human gait and can be iteratively reconfigured to perform accurate human gait state detection and assistive force tracking. The proposed methods have the potential to be integrated into other human-oriented robots to improve their adaptability. This work can greatly enhance the possibility of using the walking assist robotic devices in more practical applications.

Human locomotor activity (LA) recognition is important in the control of exoskeletons and prostheses and in patient monitoring. This article presents a practical recognition approach that can classify level walking, stair ascent, and stair descent activities across different subjects and diverse gait patterns. The thigh angle is measured and utilized in this method to construct a phase curve in an activity-specific coordinate frame during a stride. The LA is recognized by matching the curvature of its phase curve to the expected one. The factors affecting the adaptability of the proposed method to gait variations are analyzed and compensated for. The proposed method is evaluated with eight subjects who are asked to perform the three types of activity at two different cadences: 70 steps/min and 110 steps/min. Experimental results show that the proposed classifier outperforms an existing phase variable based classifier in all validation experiments and a${\boldsymbol{k}}$-nearest neighbor classifier when using nonsubject-specific training data, indicating that the proposed method has superior adaptability to changes in human and in strides. Moreover, the feature used in the proposed method has demonstrated the potential in quantitatively indicating the extent of neuromotor impairments of patients.

In sEMG-based recognition systems, accuracy is severely worsened by disturbances, such as electrode shifts by doffing/donning. Traditional recognition models are fixed or static, with limited abilities to work in the presence of the disturbances. In this paper, a transfer learning method is proposed to reduce the impact of electrode shifts. In the proposed method, a novel activation angle is introduced to locate electrodes within a polar coordinate system. An adaptive transformation is utilized to correct electrode-shifted sEMG samples. The transformation is based on estimated shifts relative to the initial position. The experiments acquisition data from ten subjects consist of sEMG signals under eight gestures in seven or nine arbitrary positions, and recorded shifts from a 3D-printed annular ruler. In our extensive experiments, the errors between recorded shifts (as the reference) and estimated shifts is about -0.017±0.13 radians. Eight gestures recognition results have shown an average accuracy around 79.32%, which represents a significant improvement over the 35.72% ( ) average accuracy of results obtained using nonadaptive models, and 60.99% ( ) results of the other method iGLCM (an improved gray-level co-occurrence matrix). More importantly, by only using one-label samples, the proposed method updates the pre-trained model in an initial position. As a result, the pre-trained model can be adaptively corrected to recognize eight-label gestures in arbitrarily rotary positions. It is proven a highly efficient way to relieve subjects' re-training burden of sEMG-based rehabilitation systems.

Summary Scientific and objective evaluations of atmospheric quality have become a primary task for researchers with the continuous development of modern industrial processes. At present, various approaches are used in monitoring air quality. The core factors of these approaches are the selection and establishment of an intelligent evaluation model. In this study, we designed a fuzzy genetic neural network model that fuses data based on the characteristics of autonomic learning and self‐organization and optimizes the fuzzy system by using the neural network model. A simulation was conducted to verify the feasibility of the algorithm. Results indicate that the proposed algorithm is not only a highly objective, scientific, and accurate method for detecting atmospheric environmental quality but also a practical solution.