Existing research suggests that lower limb exoskeleton robots, when used for rehabilitation training based on the pre-stroke gait trajectories of stroke patients, may be more beneficial for gait rehabilitation. However, it’s challenging to obtain such personalized trajectories for specific patients. Therefore, this hypothesis is difficult to be verified. This paper introduces an Individualized Gait Trajectory Generation (IGTG) method based on Fast Fourier Transform (FFT) to approximate and regress pre-stroke gaits, along with conducting clinical rehabilitation validation trials. Initially, human gait trajectories are described using Fourier coefficients to construct gait features. Subsequently, a probabilistic mapping between these gait features and physical body parameters is established. Then, personalized gait trajectories are obtained by applying the inverse Fourier transform to the predicted gait features. The application of fast Fourier transform can reduce the number of the regression data points needed, decrease dependency on large datasets, and enhance the systematic robustness. This algorithm is trained using body parameters and gait trajectories collected from 128 healthy subjects. The algorithm is further applied to generate specific personalized trajectories for the 9 stroke patients. Clinical trial results indicate that rehabilitation training using these individualized gait trajectories reduces blood oxygen saturation (SpO2) and heart rate (HR) by up to 66.67% and 69.23% respectively compared to training with fixed trajectories. Note to Practitioners —The main purpose of this paper is to solve gait trajectories mismatch problem when different stroke patients use lower limb exoskeleton robot for rehabilitation training. Variations in body factors among individuals lead to different gait trajectories including walking speed, gender, age, and other anthropometric parameters. Therefore, this paper introduces a novel Individualized Gait Trajectory Generation (IGTG) method to generate suitable gait trajectories for stroke patients with different body characteristic parameters when taking gait rehabilitation training with a lower limb exoskeleton robot. The detailed methodology introduction and a full analysis of experimental results are also given. Finally, clinical experiments involving stroke patients were conducted to demonstrate the feasibility and effectiveness of the presented method.

To address the limitations of traditional mirror therapy in stroke rehabilitation, such as rigid movement mapping and insufficient personalization, this study proposes a robot-assisted mirror rehabilitation framework integrating multimodal biofeedback. By synchronously capturing kinematic features of the unaffected upper limb and surface electromyography (sEMG) signals from the affected limb, a dual-modal feature fusion network based on a cross-attention mechanism is developed. This network dynamically generates a time-varying mirror ratio coefficient $\lambda$, which is incorporated into the exoskeleton’s admittance control loop. Combining a trajectory generation algorithm based on dynamic movement primitives (DMPs) with a compliant control strategy incorporating dynamic constraints, the system achieves personalized rehabilitation trajectory planning and safe interaction. Experimental results demonstrate that, compared to traditional mirror therapy, the proposed system exhibits superior performance in bilateral trajectory covariance metrics, the mirror symmetry index, and muscle activation levels.

The knees, as important parts in daily locomotion, carry the body weight and provide stability and flexibility. With the degradation of muscle and endurance, the elders get weak knee strength, which heavily affects their life quality. To address this issue, a single-source dual-drive flexible knee assistive exoskeleton is explored to assist the elders in their daily locomotion. Firstly, a single-source dual-drive architecture is designed. The left and right knee are controlled by single electric motor with clutch. It reduces the weight of exoskeleton and has the potential to enhance interaction safety and comfort with cable-driven and elastic elements. Secondly, a cyclic locomotion pattern recognition (LPR) methodology for daily walking situation is provided, adopted with a dual detection strategy fused with finite state machine and fuzzy control. Daily walking situations, e.g., level walking (LW), ramp ascending (RA), ramp descending (RD), stair ascending (SA), and stair descending (SD), could be recognized accurately and precisely. Finally, a time-shared assistive torque control strategy with finite state is conducted to effectively provide suitable auxiliary torque in various walking environments based on locomotion patterns. Three young participants and 3 elder participants participate in outdoor LPR experiments in real-world environment and indoor energy loss experiments. The initial experiment results indicate that the average precision of LPR reaches 98.89%, and the average metabolic cost and surface electromyography signals reduce as much as 28.20% and 67.33%, respectively, with the assistance of exoskeleton.