Compliance adaptation of an intrinsically soft ankle rehabilitation robot driven by pneumatic muscles

Pneumatic muscles (PMs)-driven robots become more and more popular in medical and rehabilitation field as the actuators are intrinsically complaint and thus are safer for patients than traditional rigid robots. This paper proposes a new compliance adaptation method of a soft ankle rehabilitation robot that is driven by four pneumatic muscles enabling three rotational movement degrees of freedom (DoFs). The stiffness of a PM is dominated by the nominal pressure. It is possible to control the robot joint compliance independently of the robot movement in task space. The controller is designed in joint space to regulate the compliance property of the soft robot by tuning the stiffness of each active link. Experiments in actual environment were conducted to verify the control scheme and results show that the robot compliance can be adjusted when provided changing nominal pressures and the robot assistance output can be regulated, which provides a feasible solution to implement the patient-cooperative training strategy

Advanced Method for Motion Control of a 3 DOFs Lower Limb Rehabilitation Robot

This paper presents two motion control methods for a lower limb rehabilitation robot based on compensate gravity proportional-derivative and inverse dynamic proportional-derivative (PD) control algorithms. The Robot’s mechanism is comprised of three active joints: hip joint, knee joint and ankle joint, which are driven by DC motors. Firstly, based on Robot’s mechanism, a dynamic model of the Robot is built. Then, based on Robot’s model, motion control systems for Robot are designed. Simulation results show good performances and workability of these proposed controllers. Finally, the calculation of the joint angle errors and toque of each controller is performed. The comparison of simulation results between proposed controllers and the adaptive fuzzy controller allows to choice suitable motion control methods for Robot that can meet the requirements of a 3 DOFs lower limb rehabilitation robot for post-stroke patient

Path Control of a Rehabilitation Robot Using Virtual Tunnel and Adaptive Impedance Controller

Interactive control strategies have been widely used in many rehabilitation robotic devices. The distinctive feature of these strategies is that the patient can be encouraged to actively participant in the therapy program. In this paper, a novel adaptive impedance control method, which allows the patient to actively influence the robot movement trajectory, is presented. The control algorithm developed in this paper is capable of regulating the desired impedance according to the patient's actual deviation from the desired path and the dynamic relationship between patients' motion intention and the reference trajectory. A virtual tunnel surrounding the reference trajectory is designed to ensure the patient's range of motion is always physiologically meaningful. The proposed rehabilitation strategy encourages participants to make contributions to rehabilitation training task as much as possible, which may facilitate provoking motor plasticity and motor recovery. Preliminary experiments with several healthy subjects were conducted to evaluate the feasibility and effectiveness of this strategy. Experimental results demonstrated that subjects could successfully finish the tracking task assisted by robot with the proposed control algorithm

Design and simulation analysis of an improved lower limb exoskeleton

The lower extremity exoskeleton robot is a type of power assisted robot which can enhance the human walking function. A fundamental problem in the development of the exoskeleton is the choice of lightweight actuators. Thus in the mechanical structure design in this paper, the linear motor is selected as it greatly reduces the complexity of the mechanical structure. Furthermore, the limit switch inside the motor improves the safety performance. Based on the last version of the exoskeleton, the band positions, length adjusting holes and mechanical limit structures are increased. In addition, a control system based on DSP is designed. Furthermore, a kinematics analysis is carried out using the D-H parameter method and a dynamic analysis is developed using the Newton-Euler method. The driving force of every joint is obtained during the simulation using ADAMS software

Design and simulation analysis of an improved wearable power knee exoskeleton

The wearable lower limb power robotic exoskeleton is a device that can improve the human walking ability. In this paper, an improved exoskeleton device for knee joint is designed, including the improvement of mechanical structure and hydraulic cylinder. In order to verify the effectiveness of the improvement of the hydraulic cylinder, we have carried out the following studies. Firstly, in terms of mechanical structure, length adjusting device is added to meet the needs of different people. At the same time, a limit device is added to the knee joint to improve the safety performance and comfort. Secondly, the dynamics of the model is carried out by Lagrange, and the exoskeleton model is established for ADAMS motion simulation. The force of ADAMS simulation, the calculated by Lagrange equation and the force of the first edition of hydraulic cylinder are compared, and the force selection of hydraulic cylinder is analyzed. By comparison with the first edition, the optimization rate of the improved hydraulic cylinder reaches 8 %. Finally, in order to verify the rationality of ADAMS simulation and the effectiveness of hydraulic cylinder improvement, the wear test is carried out, the average errors of leg centroid in normal walking, wearing exoskeleton walking and ADAMS simulation data are compared. The average error rate is less than 10 %. The results show that the simulation model design is reasonable, and the effectiveness of the hydraulic cylinder improvement is verified. The exoskeleton device designed can well follow the human motion. The simulation analysis of the exoskeleton provides important parameters for the manufacture and it also provides theoretical basis for the later control theory

An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

This paper proposes an electromyography (EMG)-based method for online force prediction and control of a lower limb rehabilitation robot. Root mean square (RMS) features of EMG signals from four muscles of the lower limb are used as the inputs to a support vector regression (SVR) model to estimate the human-robot interaction force. The autoregressive algorithm is utilized to construct the relationship between EMG signals and the impact force. Combining the force prediction model with the position-based impedance controller, the robot can be controlled to track the desired force of the lower limb, and so as to achieve an adaptive and active rehabilitation mode, which is adaptable to the individual muscle strength and movement ability. Finally, the method was validated through experiments on a healthy subject. The results show that the EMG-based SVR model can predict the lower limb force accurately and the robot can be controlled to track the estimated force by using simplified impedance model

An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

This paper proposes an electromyography (EMG)-based method for online force prediction and control of a lower limb rehabilitation robot. Root mean square (RMS) features of EMG signals from four muscles of the lower limb are used as the inputs to a support vector regression (SVR) model to estimate the human-robot interaction force. The autoregressive algorithm is utilized to construct the relationship between EMG signals and the impact force. Combining the force prediction model with the position-based impedance controller, the robot can be controlled to track the desired force of the lower limb, and so as to achieve an adaptive and active rehabilitation mode, which is adaptable to the individual muscle strength and movement ability. Finally, the method was validated through experiments on a healthy subject. The results show that the EMG-based SVR model can predict the lower limb force accurately and the robot can be controlled to track the estimated force by using simplified impedance model

Towards human-knee orthosis interaction based on adaptive impedance control through stiffness adjustment

Rehabilitation interventions involving powered, wearable lower limb orthoses that can provide high-challenging locomotor tasks for repetitive training sessions, mainly when assist-as-needed strategies, such as adaptive impedance control, are designed. In this study, the adaptive behavior was ensured by software control of the robotic stiffness involved in the human-knee orthosis interaction in function of the gait cycle and speed. To estimate the stiffness, we analyzed the interaction torque-angle characteristics with experimental data. The speed-stiffness dependency was more evident when high stiffness values are demanded by the user's effort. Experimental evidence from five healthy subjects highlight that the adaptive control strategy provides a more comfortable, natural motion, and kinematic freedom as compared to the trajectory tracking control, allowing the user to contribute to the gait training. Future insights cover the implementation of gravitational compensation and real-time estimation and control of all inner dynamic properties of the impedance control law.This work has been supported by the FCT - Fundacao para a Ciencia e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, and by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) - with the reference project POCI-01-0145-FEDER-006941, and partially supported with grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Assist-as-needed impedance control strategy for a wearable ankle robotic orthosis

The use of robots in rehabilitation attempts an effective, compliant, and time-efficient gait recovery while adapting the assistance to the user's needs. Assist-as-needed strategies (AAN), such as adaptive impedance control, have been reported as prominent strategies to enable this recovery effects. This study proposes an interaction-based assist-as-needed impedance control strategy for an ankle robotic orthosis that adapts the robotic assistance by changing the Human-Robot interaction stiffness. The adaptability of the interaction stiffness allows the real-time passage from passive assistance to an active one, approaching AAN gait training. The interaction stiffness was successfully estimated by linear regression of the Human-Robot interaction torque vs angle trajectory curve. From the validation with seven able-bodied subjects, we verified the suitability of this adaptive impedance control for a more compliant, natural, and comfortable motion than the trajectory tracking control. Moreover, the proposed strategy considers the users' motion intention and encourages them to interact closely with the robotic device while guiding their ankle trajectory according to desired trajectories. These achievements contribute to AAN gait training.This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941