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.

Emerging applications of robots with direct physical interactions with humans have led to the development of a variety of series elastic actuators (SEAs) which are compliant, force controllable, and back drivable. The performance of current SEAs is mainly dependent on the specific stiffness of the spring. In our previous work, a compliant actuator with two-stage stiffness has been designed to overcome the performance limitations in current SEAs. The key novelty is that a low-stiffness spring and a high-stiffness spring are employed instead of a single spring in current SEAs, which has the advantages of high fidelity, low output impedance, and also large force range and bandwidth. In this paper, a tracking control scheme is proposed for the compliant actuator with two-stage stiffness. Although the overall stiffness is discontinuous, the proposed controller is continuous by integrating different control modes for two springs into a single one. The transition between control modes is smooth and embedded inside the controller, and it is also automatically realized by monitoring the output force of the actuator. The stability and convergence of the closed-loop system are analyzed, and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.Note to Practitioners—An SEA is developed by placing an elastic element into the actuator; this elasticity gives SEAs several unique properties including low mechanical output impedance, tolerance to impact loads, and passive mechanical energy storage, which makes it suitable for human–robot interaction. The performance of existing SEAs is highly dependent on the stiffness of a single spring. To overcome the limitations, a novel SEA with two-stage stiffness was proposed in our previous work. This paper suggests a continuous tracking control method for the proposed compliant actuator. Although the overall stiffness is discontinuous, the transition between different control modes for two springs is smooth and automatically realized. Experimental results show that the output force of the actuator is bounded. In future research, uncertainties in actuator dynamics will be considered, such that system identification or calibration is not required.

Series elastic actuators (SEAs) are known to offer a range of advantages over stiff actuators for human–robot interaction, such as high force/torque fidelity, low impedance, and tolerance to shocks. While a variety of SEAs have been developed and implemented in initiatives that involve physical interactions with humans, relatively few control schemes were proposed to deal with the dynamic stability and uncertainties of robotic systems driven by SEAs, and the open issue of safety that resolves the conflicts of motion between the human and the robot has not been systematically addressed. In this paper, a novel continuous adaptive control method is proposed for SEA-driven robots used in human–robot interaction. The proposed method provides a unified formulation for both therobot-in-chargemode, where the robot plays a dominant role to follow a desired trajectory, and thehuman-in-chargemode, in which the human plays a dominant role to guide the movement of robot. Instead of designing multiple controllers and switching between them, both typical modes are integrated into a single controller, and the transition between two modes is smooth and stable. Therefore, the proposed controller is able to detect the human motion intention and guarantee the safe human–robot interaction. The dynamic stability of the closed-loop system is theoretically proven by using the Lyapunov method, with the consideration of uncertainties in both the robot dynamics and the actuator dynamics. Both simulation and experimental results are presented to illustrate the performance of the proposed controller.

Series elastic actuators (SEAs) are known to offer a number of advantages such as high force/torque fidelity, low impedance, and tolerance to shocks, which make it suitable for the applications involving human-robot interaction. In existing SEA-driven robot systems, the control objective is usually specified as a predefined trajectory or an impedance model that describes the relationship between the desired motion of robot and the external force, and controllers are always activated to regulate the desired motion or the desired impedance model. In this paper, a region control scheme is proposed for robots driven by SEAs, where the control objective is specified as a region, instead of trajectory or desired impedance. The region control has the advantage of flexibility, in the sense that the robot is able to move freely inside the desired region and thus compliant with the environment or physical interactions with humans. Though the overall dynamics that includes both actuator and robot dynamics is a fourth-order system, the proposed control method does not require the high-order derivatives or the construction of any observer. Experimental results are presented to demonstrate the effectiveness of the proposed control method.

Series Elastic Actuators (SEAs) have several superiorities over conventional stiff and non-back-drivable actuators in force control, e.g., lower reflected inertia, low cost force measurement, high force fidelity, safety, and so on. However, their position control applications significantly suffer from low performance and disturbances due to insufficient controller designs. In this paper, a new Active Disturbance Rejection (ADR) controller is proposed for the robust position control problem of SEAs by combining Differential Flatness (DF) and Disturbance Observer (DOb) in state space. The trajectory of the actuator is generated by using DF and is tracked by using a conventional state feed-back controller. The state and control input references of a DF-based trajectory tracking controller are modified by using estimated disturbances so that the robustness is achieved. The proposed controller provides high performance position control for SEAs when they suffer from plant uncertainties and external disturbances such as inertia variation, backlash, friction and external load. Experimental results are given to validate the proposal.

This paper proposes a new robust position control method for a novel series elastic actuator (SEA). It is a wellknown fact that SEAs provide many benefits in force control, e.g., lower reflected inertia and impedance, greater shock tolerance, safety, and so on. However, current SEA designs have a common performance limitation due to the compromise on the selection of spring stiffness. The performance of an SEA can be significantly improved by changing the stiffness of the spring; however, designing a variable-stiffness SEA is a quite challenging task. In this paper, a novel variable-stiffness SEA, which can relax the fundamental performance limitation of conventional SEAs, is proposed. It consists of torsional and linear springs, which have different compliances, in series. The soft and hard springs improve the performance when low and high force control applications are performed, respectively. Although SEAs have several advantages in force control, their position control problem is more complicated than the force control one. Moreover, using extra springs increases the number of vibration mode, which may significantly deteriorate the performance, in the position control problem of the SEA. In this paper, a new position control system, which improves the performance by increasing the robustness and suppressing the vibration, is proposed for a novel SEA. Experimental results are given to validate the proposal.

Rehabilitation robots, by necessity, have direct physical interaction with humans. Physical interaction affects the controlled variables and may even cause system instability. Thus, human-robot interaction control design is critical in rehabilitation robotics research. This paper presents an interaction control strategy for a gait rehabilitation robot. The robot is driven by a novel compact series elastic actuator, which provides intrinsic compliance and backdrivablility for safe human-robot interaction. The control design is based on the actuator model with consideration of interaction dynamics. It consists mainly of human interaction compensation, friction compensation, and is enhanced with a disturbance observer. Such a control scheme enables the robot to achieve low output impedance when operating in human-in-charge mode and achieve accurate force tracking when operating in force control mode. Due to the direct physical interaction with humans, the controller design must also meet the stability requirement. A theoretical proof is provided to show the guaranteed stability of the closed-loop system under the proposed controller. The proposed design is verified with an ankle robot in walking experiments. The results can be readily extended to other rehabilitation and assistive robots driven with compliant actuators without much difficulty.

We are developing an intelligent compact and modular knee-ankle-foot robot gait rehabilitation at outpatient and home settings. The robot is designed with a novel compact compliant force controllable actuator. We adopt a modular design for the knee and ankle joint so that the robot can assist patients with different conditions of gait impairments. The light-weight anthropomorphic structure designed based on biomechanical studies is built with advanced composite materials to achieve portability. A prototype of the robot has been built for testing. In this paper, we present the mechanical design of the robot with focus on the actuator and mechanism design and analysis, with testing results to demonstrate the performance of the actuator.