Lower Extremity Rehabilitation Robot Research Articles

The Human–Machine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review

The Human–Machine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review

A DMP-Based Motion Generation Scheme for Robotic Mirror Therapy

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.

A rehabilitation robot control framework with adaptation of training tasks and robotic assistance.

Robot-assisted rehabilitation has exhibited great potential to enhance the motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic motion can be regulated through physical human-robot interaction for comfortable support and appropriate assistance level. However, it is hardly possible, especially for patients with severe motor disabilities, to continuously exert force to guide the robot to complete the prescribed training task. Conversely, reduced task difficulty cannot facilitate stimulating patients' potential movement capabilities. Moreover, challenging more difficult tasks with minimal robotic assistance is usually ignored when subjects show improved performance. In this paper, a control framework is proposed to simultaneously adjust both the training task and robotic assistance according to the subjects' performance, which can be estimated from the users' electromyography signals. Concretely, a trajectory deformation algorithm is developed to generate smooth and compliant task motion while responding to pHRI. An assist-as-needed (ANN) controller along with a feedback gain modification algorithm is designed to promote patients' active participation according to individual performance variance on completing the training task. The proposed control framework is validated using a lower extremity rehabilitation robot through experiments. The experimental results demonstrate that the control scheme can optimize the robotic assistance to complete the subject-adaptation training task with high efficiency.

Learning robotic motion with mirror therapy framework for hemiparesis rehabilitation

The number of hemiplegic patients has rapidly increased in recent years, and intelligent robots have high rehabilitation potential. Robotic mirror therapy (RMT) is a promising therapeutic measure for hemiparesis by voluntarily transferring the motion of the healthy limb (HL) to the impaired limb (IL), in which a robot interacts with and assists the IL to mimic the action of the HL to stimulate the active participation of the injured muscles. Nonetheless, complete replication of the HL movement trajectory to the robot without considering the IL muscle strength cannot ensure safety or facilitate rehabilitation. In this study, a learning-based robotic motion generation scheme was developed for RMT. The robot movement trajectories were modeled with dynamic movement primitives (DMPs), and the physical human-robot interaction was formulated as an impedance model coupled with the DMP model. To adapt the robotic motion to different pilots, reinforcement learning was used to optimize the coupled DMP model parameters. The reinforcement learning approach was implemented based on policy improvement and the path integral (PI2) algorithm, and the cost function was designed to simultaneously ensure training safety and enhance muscle strength. The proposed method was validated using a lower-extremity rehabilitation robot with magnetorheological actuators, and the experimental results demonstrated the feasibility and superiority.

A Multi-Channel Reinforcement Learning Framework for Robotic Mirror Therapy

In the letter, a robotic framework is proposed for hemiparesis rehabilitation. Mirror therapy is applied to transfer therapeutic training from the patient's 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 user's 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.

Mechanical Impedance of the Non-loaded Lower Leg with Relaxed Muscles in the Transverse Plane.

This paper describes the protocols and results of the experiments for the estimation of the mechanical impedance of the humans’ lower leg in the External–Internal direction in the transverse plane under non-load bearing condition and with relaxed muscles. The objectives of the estimation of the lower leg’s mechanical impedance are to facilitate the design of passive and active prostheses with mechanical characteristics similar to the humans’ lower leg, and to define a reference that can be compared to the values from the patients suffering from spasticity. The experiments were performed with 10 unimpaired male subjects using a lower extremity rehabilitation robot (Anklebot, Interactive Motion Technologies, Inc.) capable of applying torque perturbations to the foot. The subjects were in a seated position, and the Anklebot recorded the applied torques and the resulting angular movement of the lower leg. In this configuration, the recorded dynamics are due mainly to the rotations of the ankle’s talocrural and the subtalar joints, and any contribution of the tibiofibular joints and knee joint. The dynamic mechanical impedance of the lower leg was estimated in the frequency domain with an average coherence of 0.92 within the frequency range of 0–30 Hz, showing a linear correlation between the displacement and the torques within this frequency range under the conditions of the experiment. The mean magnitude of the stiffness of the lower leg (the impedance magnitude averaged in the range of 0–1 Hz) was determined as 4.9 ± 0.74 Nm/rad. The direct estimation of the quasi-static stiffness of the lower leg results in the mean value of 5.8 ± 0.81 Nm/rad. An analysis of variance shows that the estimated values for the stiffness from the two experiments are not statistically different.

Robust Controller Design for the Cooperative Control of Lower Extremity Rehabilitation Robot

In this paper, a robust controller design approach is proposed for Lower Extremity Rehabilitation Robot (LERR) with automatic gait adaption. Based on the developed LERR dynamic model, the robot system is first decoupled into some independent second-order integral systems using inverbility decoupling method. Then a robust optimal PID controller is proposed for each sub-system to cope with the uncertainty in the system modeling by

Design of the Lower Extremity Rehabilitation Robot Communication System

According to this analysis, we designed a scenario on lower extremity rehabilitation robot communication system based on CAN bus and efficiently solved the problems of low signal processing, computing power and data transfer rate ability for the traditional means of communication of RS232 and RS485.The research deals with the characteristics of communication systems and topological structure, and specifically, with CAN bus protocol design and hardware and software implementation aspects of Communication System designing for some robot sensor.

Design of the Lower Extremity Rehabilitation Robot Communication System

According to this analysis, we designed a scenario on lower extremity rehabilitation robot communication system based on CAN bus and efficiently solved the problems of low signal processing, computing power and data transfer rate ability for the traditional means of communication of RS232 and RS485.The research deals with the characteristics of communication systems and topological structure, and specifically, with CAN bus protocol design and hardware and software implementation aspects of Communication System designing for some robot sensor.

Standardized voluntary force measurement in a lower extremity rehabilitation robot

BackgroundIsometric force measurements in the lower extremity are widely used in rehabilitation of subjects with neurological movement disorders (NMD) because walking ability has been shown to be related to muscle strength. Therefore muscle strength measurements can be used to monitor and control the effects of training programs. A new method to assess isometric muscle force was implemented in the driven gait orthosis (DGO) Lokomat. To evaluate the capabilities of this new measurement method, inter- and intra-rater reliability were assessed.MethodsReliability was assessed in subjects with and without NMD. Subjects were tested twice on the same day by two different therapists to test inter-rater reliability and on two separate days by the same therapist to test intra-rater reliability.ResultsResults showed fair to good reliability for the new measurement method to assess isometric muscle force of lower extremities. In subjects without NMD, intraclass correlation coefficients (ICC) for inter-rater reliability ranged from 0.72 to 0.97 and intra-rater reliability from 0.71 to 0.90. In subjects with NMD, ICC ranged from 0.66 to 0.97 for inter-rater and from 0.50 to 0.96 for intra-rater reliability.ConclusionInter- and intra- rater reliability of an assessment method for measuring maximal voluntary isometric muscle force of lower extremities was demonstrated. We suggest that this method is a valuable tool for documentation and controlling of the rehabilitation process in patients using a DGO.