Lower extremity paralysis has become common in recent years, and robots have been developed to help patients recover from it. This paper presents such a robotic system that allows for two working modes, the robot-active mode and human-active mode. The robot is designed to be equipped with magnetorheological (MR) actuators that have the advantages of high torque, fast response, flexible controllability, low power consumption and safety guarantee. The design and characteristics of the MR actuator are introduced. In the robot-active mode, the MR actuator works as a clutch to transfer the torque to the robotic joint safely. In the human-active mode, the MR actuator functions as a brake to provide resistance to help strengthen muscles. The working mode is determined by the human motion intention, which is detected via the skin surface electromyography (EMG) signals. The human-robot interaction torques are estimated using the EMG-driven impedance model. The biomechanical analysis based on AnyBody Modeling System (AMS) is used to help optimization. Then, an adaptive control method is proposed to realize the assist-as-needed (AAN) training strategy, where the robot can switch between these two modes. Experiments are conducted to validate the proposed design.

In the paper, a robotic framework is proposed for hemiparesis rehabilitation. Mirror therapy is applied to transfer therapeutic training from the patients function limb (FL) to the impaired limb (IL). The IL mimics the action prescribed by the FL with the assistance of the wearable robot, stimulating and strengthening the injured muscles through repetitive exercise. A master-slave robotic system is presented to implement the mirror therapy. Especially, the reinforcement learning is involved in the human-robot interaction control to enhance the rehabilitation efficacy and guarantee safety. Multi-channel sensed information, including the motion trajectory, muscle activation and the users emotion, are incorporated in the learning algorithm. The muscle activation is expressed via the skin surface electromyography (EMG) signals, and the emotion is shown as the facial expression. The reinforcement learning approach is realized by the normalized advantage functions (NAF) algorithm. Then, a lower extremity rehabilitation robot with magnetorheological (MR) actuators is specially developed. The clinical experiments are carried out using the robot to verify the performance of the framework.

Recognizing facial expression has attracted much more attention due to its broad range of applications in human–computer interaction systems. Although facial representation is crucial to final recognition accuracy, traditional handcrafted representations only reflect shallow characteristics and it is uncertain whether the convolutional layer can extract better ones. In addition, the policy that weights are shared across a whole image is improper for structured face images. To overcome such limitations, a novel method based on patches of interest, the Patch Attention Layer (PAL) of embedding handcrafted features, is proposed to learn the local shallow facial features of each patch on face images. Firstly, a handcrafted feature, Gabor surface feature (GSF), is extracted by convolving the input face image with a set of predefined Gabor filters. Secondly, the generated feature is segmented as nonoverlapped patches that can capture local shallow features by the strategy of using different local patches with different filters. Then, the weighted shallow features are fed into the remaining convolutional layers to capture high-level features. Our method can be carried out directly on a static image without facial landmark information, and the preprocessing step is very simple. Experiments on four databases show that our method achieved very competitive performance (Extended Cohn–Kanade database (CK+): 98.93%; Oulu-CASIA: 97.57%; Japanese Female Facial Expressions database (JAFFE): 93.38%; and RAF-DB: 86.8%) compared to other state-of-the-art methods.

Hemiparesis has become prevalent in recent years, and robot-assisted rehabilitation provides great therapeutic potential. Robotic mirror therapy is a promising approach for hemiplegic patients by transmitting the motion of the healthy limb (HL) to the impaired limb (IL), where the wearable robot assists the IL to mimic the HL's action to stimulate the active participation of the injured muscles. However, complete replication of the HL's movement trajectory without considering the IL's muscle strength is not satisfactory for rehabilitation. In this article, a motion generation scheme is proposed for robotic mirror therapy. The robot's movement trajectory is modeled with dynamic movement primitive (DMP), and the physical human–robot interaction is formulated as an impedance model and coupled with the DMP model. The subject's muscle strength is evaluated with skin surface electromyography signals and transferred to the robot's joint stiffness. In order to adapt the human–robot coupled DMP model parameters to varying subject performance, a reinforcement learning algorithm is designed to optimize the robot's movement trajectory and stiffness profile, where the training safety and rehabilitation improvement are both guaranteed. The proposed method is validated using a lower extremity rehabilitation robot, and experimental results demonstrate its feasibility and superiority.

Soft exoskeleton robots (exosuits) have exhibited promising potentials in walking assistance with comfortable wearing experience. In this paper, a twisted string actuator (TSA) is developed and equipped with the exosuit to provide powerful driving force and variable assistance intensity for hemiplegic patients, which provides human-domain and robot-domain training modes for subjects with different movement capabilities. Since the human-exosuit coupling dynamics is difficult to be modeled due to the soft structure of the exosuit and incomplete knowledge of the wearer’s performance, accurate control and efficient assistance cannot be guaranteed in current exosuits. By taking advantage of the motion characteristic of hemiplegic patients, a mirror adaptive impedance control is proposed, where the robotic actuation is modulated based on the motion and physiological reference of the healthy limb (HL) as well as the performance of the impaired limb (IL). A linear quadratic regulation (LQR) is formulated to minimize the bilateral trajectory tracking errors and human effort, and the adaptation between the human-domain and robot-domain modes can be realized. A reinforcement learning (RL) algorithm is designed to solve the given LQR problem to optimize the impedance parameters with little information of the human or robot model. The proposed robotic system is validated through experiments to perform its effectiveness and superiority. Note to Practitioners —To assist walking for hemiplegic patients, it is crucial to provide comfortable and compliant driving force that can minimize the patients’ voluntary effort. The development of soft exoskeleton is able to realize comfortable wearing experience, and the proposed TSA can output compliant driving force with different training modes and assistance intensities. To address the problem in accurately building the human-exosuit coupled model, this work achieves the adaptive control strategy for patients with various movement capabilities in two steps. Firstly, a mirror adaptive impedance controller is proposed to make the patient’s HL tightly follow the IL’s motion for high safety guarantee performance and training autonomy. Secondly, a reinforcement learning-based LQR framework is constructed to minimize the patient’s voluntary effort by optimizing the prescribed impedance model parameters, which can significantly facilitate assistance efficiency for different patients. The experiments demonstrate that the proposed robotic system can obtain appropriate training modes and efficient walking assistance for human subjects. In the future study, it will be investigated how to assist patients in walking on different terrains with highly stable and adaptive actuation, which will accelerate the application of the proposed exosuit into activities of daily living.