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.

A typical application of upper-limb exoskeleton robots is deployment in rehabilitation training, helping patients to regain manipulative abilities. However, as the patient is not always capable of following the robot, safety issues may arise during the training. Due to the bias in different patients, an individualized scheme is also important to ensure that the robot suits the specific conditions (e.g., movement habits) of a patient, hence guaranteeing effectiveness. To fulfill this requirement, this paper proposes a new motion planning scheme for upper-limb exoskeleton robots, which drives the robot to provide customized, safe, and individualized assistance using both human demonstration and interactive learning. Specifically, the robot first learns from a group of healthy subjects to generate a reference motion trajectory via probabilistic movement primitives (ProMP). It then learns from the patient during the training process to further shape the trajectory inside a moving safe region. The interactive data is fed back into the ProMP iteratively to enhance the individualized features for as long as the training process continues. The robot tracks the individualized trajectory under a variable impedance model to realize the assistance. Finally, the experimental results are presented in this paper to validate the proposed control scheme.

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this article proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.

Exoskeleton robots provide assistive forces to suit the human subject via physical human-robot interaction. During the closely-coupled interaction, a mismatch between the wearer and the robot may result in physical conflict, which could affect assistance efficiency or even compromise safety. Therefore, such conflicts should be accurately detected and then properly relaxed by adjusting the robot's action. This paper proposes a new learning scheme to detect physical conflicts between humans and robots. The constructed learning network receives multi-modal information from proprioceptive sensors and then outputs the anomaly score to specify the physical conflict, which score is further used to continuously adjust the robot impedance to ensure a safe and efficient interaction. Such a formulation allows the robot to explore the semantic information during the interaction (e.g., gait phases, imbalance, human fatigue) and hence react properly to the physical conflict. Experimental results and comparative studies on a lower-limb exoskeleton robot are presented to illustrate that the proposed learning scheme can deal with physical conflicts in a faster and more accurate manner.

The advantages of cable-driven exoskeleton robots with series elastic actuators can be summarized in twofold: 1) the inertia of the robot joint is relatively low, which is more friendly for human-robot interaction; 2) the elastic element is tolerant to impacts and hence provides structural safety. As trade-offs, the overall dynamic model of such a system is of high order and subject to both unmodelled disturbances (due to the cable-driven mechanism) and external torques (due to the human-robot interaction), opening up challenges for the controller development. This paper proposes a new trajectory-tracking control scheme for cable-driven upper-limb exoskeleton robots with series elastic actuators. The control objectives are achieved in two stages: Stage I is to approximate then compensate for unmodelled disturbances with iterative learning techniques; Stage II is to employ a suboptimal model predictive controller to drive the robot to track the desired trajectory. While controlling such a robot is not trivial, the proposed control scheme exhibits the advantages of force-sensorlessness, high accuracy, and low complexity compared with other methods in the real-world experiments.

Recently, a large amount of stroke survivors are suffering from motor impairment. However, existed therapy interventions have limited effects to restore normal motor function. Thus, we proposed a novel control strategy for gait rehabilitation of hemiplegic patients. The whole system consists of a Functional Electrical Stimulation (FES) device and Treadmill-Walk system. FES contributes to improve the quality of the gait based on real-time adjustment of gait pattern. During gait, the electrical stimuli from separate output channels of an FES device are launched to stimulate two lower extremity muscles (Tibialis Anterior (TA) and Hamstrings). Stimulus launching procedure is based on identifying subject's gait state (stance and swing phases). According to the current variation of treadmill motor, gait phase and muscle activation of lower limbs can be determined during walking on Treadmill-Walk. Three able-bodied subjects simulated hemiplegic patients in the experiment. The results indicated that the proposed method is a safe, feasible and promising intervention.