Lower Limb Rehabilitation Exoskeleton Research Articles

Lower Limb Rehabilitation Exoskeleton Robots, A review

Using a lower limb exoskeleton for rehabilitation (LLE) Lower limb exoskeleton rehabilitation robots (LER) are designed to assist patients with daily duties and help them regain their ability to walk. Even though a substantial portion of them is capable of doing both, they have not yet succeeded in conducting agile and intelligent joint movement between humans and machines, which is their ultimate goal. The typical LLE products, rapid prototyping, and cutting-edge techniques are covered in this review. Restoring a patient's athletic prowess to its pr-accident level is the aim of rehabilitation treatment. The core of research on lower limb exoskeleton rehabilitation robots is the understanding of human gait. The performance of common prototypes might be used to match wearable robot shapes to human limbs. To imitate a normal stride, robot-assisted treatment needs to be able to control the movement of the robot at each joint and move the patient's limb.

Modeling and Strength Calculations of Parts Made Using 3D Printing Technology and Mounted in a Custom-Made Lower Limb Exoskeleton.

This study is focused on the application of 3D-printed elements and conventional elements to create a prototype of a custom-made exoskeleton for lower limb rehabilitation. The 3D-printed elements were produced by using Fused Deposition Modeling technology and acrylonitrile butadiene styrene (ABS) material. The scope of this work involved the design and construction of an exoskeleton, experimental testing of the ABS material and numerical research by using the finite element method. On the basis of the obtained results, it was possible to deduce whether the load-bearing 3D-printed elements can be used in the proposed mechanical construction. The work contains full data of the material models used in FEM modeling, taking into account the orthotropic properties of the ABS material. Various types of finite elements were used in the presented FE models. The work is a comprehensive combination of material testing issues with the possibility of implementing the obtained results in numerical strength models of machine parts.

Lower limb rehabilitation exoskeleton using brain–computer interface based on multiband filtering with classifier fusion

AbstractIn this study, we propose two adaptive frameworks based on evolutionary algorithms for improving the accuracy of motor imagery (MI) task classification. In an MI task, each participant has a different reaction time delay. In this study, the reaction delay was used as a parameter. Specifically, the time point after the reaction delay was defined as the starting point (SP) when MI began. Various classification and optimization algorithms were employed. The methods include the use of a fixed and dynamic SP for each individual, a genetic algorithm, particle swarm optimization (PSO), spatial filter tuning, the use of common spatial patterns, linear discriminant analysis, bagged decision trees, convolutional neural network (CNN), and long short‐term memory (LSTM). Moreover, a dynamic optimization method for multiband filtering (MBF) method for the subbands of EEG signals was developed that improved accuracy by 5.4%. Combining MBF with bagged decision trees with PSO‐optimized parameters achieved the highest accuracy of 82.3%. Moreover, after optimization by PSO, the CNN‐LSTM method achieved a classification accuracy of 81.4% for 1‐s signals, shorter than those in other studies. The method enables the use of EEG signals to control an exoskeleton for patient rehabilitation.

Lower Limb Exoskeleton for Rehabilitation with Flexible Joints and Movement Routines Commanded by Electromyography and Baropodometry Sensors.

This paper presents the development of an instrumented exoskeleton with baropodometry, electromyography, and torque sensors. The six degrees of freedom (Dof) exoskeleton has a human intention detection system based on a classifier of electromyographic signals coming from four sensors placed in the muscles of the lower extremity together with baropodometric signals from four resistive load sensors placed at the front and rear parts of both feet. In addition, the exoskeleton is instrumented with four flexible actuators coupled with torque sensors. The main objective of the paper was the development of a lower limb therapy exoskeleton, articulated at hip and knees to allow the performance of three types of motion depending on the detected user's intention: sitting to standing, standing to sitting, and standing to walking. In addition, the paper presents the development of a dynamical model and the implementation of a feedback control in the exoskeleton.

Control of an Exoskeleton for Lower Limb Rehabilitation Using ANFIS

Exoskeletons are powered robotic devices designed to be worn by humans to provide physical assistance or power augmentation. In this work, a control system for a powered exoskeleton is designed. This exoskeleton is aimed at aiding in the rehabilitation of Spinal Bifidas. Spinal Bifida is the most common disability in childhood after Cerebral Palsy, it is a defective development of the spinal cord during conception. Two phases for this work are presented: system identification and control using ANFIS. While it is difficult to attain an accurate dynamical model of complex system, this work employed ANFIS to help control and stabilize the system. Gait trajectories were obtained by modeling the system as a linear inverted pendulum, a simulation was performed with a traditional controller. Afterwards, trajectory data was obtained and used to train and test ANFIS to create the model and controller. One, two and three inputs were investigated to train the ANFIS. Results showed that the one-input model visibly failed to follow the trajectory. The average RMSE for the two-input model was 0.096, and for the three-inputs, the RMSE on average was higher; 0.19, making it worse, however the knee model contrastingly showed improvement, as the RMSE was lower by 2% for the knee specifically.

Dynamic analysis and design of a multipurpose lower limb exoskeleton for rehabilitation

To solve some defects of exoskeleton robot at present, this article establishes the dynamic model of human lower limb. The torque curves for hip joint and knee joint are obtained. A dynamics simulation is conducted in ADAMS which will guide the selection of motors and reducers for exoskeleton joints. Three structural design projects for leg and an integrated joint with the function of force perception are proposed. Then a lightweight exoskeleton is put forward and a kinematics simulation of man–machine coupling system is carried out in ADAMS. This article sets up a 24-V low-voltage control electrical system and a rehabilitation training expert system. Some performance tests and clinical experiments are carried out by an experimental prototype. The results show that the joints have sufficient driving torque. Leg structure has large adjustment range and self-locking function. The exoskeleton has lightweight and does not interfere with human body during movement. The expert system has a friendly operation interface and abundant functions. Clinical experimental results show that lower limb exoskeleton has good rehabilitation effect for some diseases.

The Wearable Lower Limb Rehabilitation Exoskeleton Kinematic Analysis and Simulation.

In recent years, due to the increase in the incidence of traffic accidents, the number of people with limb injuries has also increased. At the same time, among the aging population, neurological diseases or cardiovascular and cerebrovascular diseases have caused many people to have limb hemiplegia. It has been clinically proven that the use of rehabilitation equipment can help patients with limb injuries to restore limb motor function. This paper takes wearable lower limb rehabilitation exoskeleton as the research object, and its main contents are mechanical structure design, kinematics analysis, gait planning, virtual prototype simulation, and experimental verification and analysis. Based on the physiological characteristics of human body and the principle of comfortable and reliable wearing, this paper designs wearable exoskeleton for lower limb rehabilitation. Firstly, the physiological structure characteristics and movement mechanism of human lower limbs were studied and analyzed. By referring to the rotation range and height and size of each joint of human lower limbs, the overall scheme of wearable lower limb rehabilitation exoskeleton was designed and the degree of freedom was allocated. At the same time, Solidworks was used to establish a three-dimensional model. On the basis of a 3D model, a kinematics model was established, and the forward kinematics solution was obtained by using homogeneous coordinate transformation. Since the inverse kinematics solution was relatively complicated, the inverse kinematics solution was conducted in this paper according to the geometric relations of the joints of the lower limbs. Kinematics analysis of the exoskeleton structure of wearable lower limb rehabilitation was carried out to lay a theoretical foundation for gait planning. The off-line gait planning was carried out by using the method based on ZMP stability criterion, and the gait planning was divided into five stages: squat, start, middle step, stop and rise, and the motion trajectory of the center of mass and ankle joint was planned. Based on the inverse kinematics formula, the function of the change of joint angle with time in walking process is derived. The virtual prototype is established in ADAMS, and the simulation of virtual prototype is carried out by using the function of gait planning's joint angle and time. The correctness of structural design and gait planning was verified by measuring the trajectories of the centroid and ankle joints in each gait stage and the functional relationship between the rotation angle of each joint and time. Then, using a 3D dynamic capture system to capture the human lower limb motion trajectory of each joint, each joint trajectory data output, using the MATLAB software to output data, gets the joint trajectory change over time function curve and is used to verify feasibility and applicability of human gait planning. Through the research and analysis of the joints of the lower limbs of the human body, it can be concluded that the hip joint and the knee joint have 3 degrees of freedom, respectively, and the knee joint has 1 degree of freedom.

Design and analysis of a lightweight lower extremity exoskeleton with novel compliant ankle joints.

The exoskeleton for lower limb rehabilitation is an uprising field of robot technology. However, since it is difficult to achieve all the optimal design values at the same time, each lower extremity exoskeleton has its own focus. This study aims to develop a modular lightweight lower extremity exoskeleton (MOLLEE) with novel compliant ankle joints, and evaluate the movement performance through kinematics analysis. The overall structure of the exoskeleton was proposed and the adjustable frames, active joint modules, and compliant ankle joints were designed. The forward and inverse kinematics models were established based on the geometric method. The theoretical models were validated by numerical simulations in ADAMS, and the kinematic performance was demonstrated through walking experiments. The proposed lower extremity offers six degrees of freedom (DoF). The exoskeleton frame was designed adjustable to fit wearers with a height between 1.55m and 1.80m, and waist width from 37cm to 45cm. The joint modules can provide maximum torque at 107Nm for adequate knee and hip joint motion forces. The compliant ankle can bear large flexible deformation, and the relationship between its angular deformation and the contact force can be fitted with a quadratic polynomial function. The kinematics models were established and verified through numerical simulations, and the walking experiments in different action states have shown the expected kinematic characteristics of the designed exoskeleton. The proposed MOLLEE exoskeleton is adjustable, modular, and compliant. The designed adjustable frame and compliant ankle can ensure comfort and safety for different wearers. In addition, the kinematics characteristics of the exoskeleton can meet the needs of daily rehabilitation activities.

Robust Iterative Learning Control Algorithm for Lower Limb Rehabilitation Proactive Human-robot Collaboration

At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

Active Assistive Design and Multiaxis Self-Tuning Control of a Novel Lower Limb Rehabilitation Exoskeleton

This paper presented the mechanical design and control of a lower limb rehabilitation exoskeleton named “the second lower limb rehabilitation exoskeleton (LLRE-II)”. The exoskeleton with a lightweight mechanism comprises a 16-cm stepless adjustable thigh and calf rod. The LLRE-II weighs less than 16 kg and has four degrees of freedom on each leg, including the waist, hip, knee, and ankle, which ensures fitted wear and comfort. Motors and harmonic drives were installed on the joints of the hip and knee to operate the exoskeleton. Meanwhile, master and slave motor controllers were programmed using a Texas Instruments microcontroller (TMS320F28069) for the walking gait commands and evaluation boards (TMS320F28069/DRV8301) of the joints. A self-tuning multiaxis control system was developed, and the performance of the controller was investigated through experiments. The experimental results showed that the mechanical design and control system exhibit adequate performance. Trajectory tracking errors were eliminated, and the root mean square errors reduced from 6.45 to 1.22 and from 4.15 to 3.09 for the hip and knee, respectively.

A Review on the Rehabilitation Exoskeletons for the Lower Limbs of the Elderly and the Disabled

Research on the lower limb exoskeleton for rehabilitation have developed rapidly to meet the need of the aging population. The rehabilitation exoskeleton system is a wearable man–machine integrated mechanical device. In recent years, the vigorous development of exoskeletal technology has brought new ideas to the rehabilitation and medical treatment of patients with motion dysfunction, which is expected to help such people complete their daily physiological activities or even reshape their motion function. The rehabilitation exoskeletons conduct assistance based on detecting intention, control algorithm, and high-performance actuators. In this paper, we review rehabilitation exoskeletons from the aspects of the overall design, driving unit, intention perception, compliant control, and efficiency validation. We discussed the complexity and coupling of the man–machine integration system, and we hope to provide a guideline when designing a rehabilitation exoskeleton system for the lower limbs of elderly and disabled patients.

A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton.

Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.

Design and virtual model of an exoskeleton for lower limb rehabilitation

In this paper the design and the virtual model of an exoskeleton for human lower limb rehabilitation is developed. The proposed exoskeleton is anthropomorphic, low cost and easy to adapt on the human subject. The design aspect concerns the exoskeleton mechatronic structure achieved in Solid Works virtual environment. In order to realize the multibody model, the virtual spatial model of a human mannequin developed in SolidWorks was transferred to the ADAMS database. The movement laws experimentally collected are introduced in each of the 6 rotational joints of the both lower limbs. Simulation of the mannequin walking is performed.

Development a multi-loop modulation method on the servo drives for lower limb rehabilitation exoskeleton

This paper proposed a multi-loop modulation method (MMM; 3M) on the servo drives applied for the lower limb rehabilitation exoskeleton (LLRE). This proposed 3M included the current, speed and position 3-loop with the auto-tuning, inertia identifier and external load torque observer. The controller gains by searching the optimal bandwidth and by identifying the inertia of the LLRE system were derived from the 3-loop auto-tuning. The controller gains could be instantly adjusted according to the system's oscillation during the LLRE gait motion. In particular, a concept of the phase margin (PM) was introduced in the calculation of the controller gain to ensure system stability. The proposed method could avoid system turbulence due to the excessive bandwidth. On the other hand, a torque observer and inertia identifier were adopted to compensate the external load. Compared the traditional parameter design method and traditional auto-tuning method with this proposed 3M in terms of tracking error and root mean square error (RMSE), the result showed that this proposed 3M provided better response and stability for gait tracking. Also, this proposed 3M could be adapted to variations in different loads.

Robotic mechanotherapy: the possibility to use an exoskeleton for lower limb rehabilitation in patients with multiple sclerosis and impaired walking function

Background: Robotic mechanotherapy is considered as a promising area of physical rehabilitation of multiple sclerosis patients, while it ensures high training efficacy. Aim: To study the effect of physical training using with the ExoAtlet exoskeleton for lower extremities the functioning of patients with multiple sclerosis. Materials and methods: This was a prospective, open, uncontrolled, single center study. The rehabilitation course with the ExoAtlet exoskeleton included 43 patients (14 male and 29 female, aged from 28 to 59 years, mean age 43,5 ± 9,12 years) with remitting multiple sclerosis in remission (RMS-R) (n = 20) and secondary progressive course (VPRS) (n = 23), with the EDSS scores from 3 to 8. One of the inclusion criteria was the presence of motor paresis of the lower extremities. Training with the ExoAtlet exoskeleton was performed 5 days a week for two weeks. The neurological deficits and functioning were assessed with the Kurtzke expanded disability status scale (EDSS), the multiple sclerosis functional composite (MSFC) test, including the assessment of walking (Timed 25 Footwalk), of upper limb functions (9-Hole PegTest, 9-HPT), and of mental functioning (Symbol Digit Modalities Test, SDMT) before and after the rehabilitation course. Cognitive functions were assessed by the Montreal Cognitive Assessment (MoCA) scale. Results: The rehabilitation course resulted in a significant decrease of neurological deficiency by EDSS (by 0.26 score, 5%, p < 0.001). The MSFC test showed an improvement in all subtests: SDMT by 2 points, or 4.9% (p = 0.018), Timed 25-Footwalk by 3.2 seconds, or 19.6% (p < 0.001), 9-HPT for the dominant hand by 1.6 seconds, or 5% (p = 0.004), and for the non-dominant hand by 2.1 seconds, or 6.2% (p = 0.006). The improvement in the MoCA test after the rehabilitation course was 1.6 points, or 6% (p < 0.001). Conclusion: The study confirmed the positive effect of the exoskeleton in the lower extremities, such as restoration of the walking function in multiple sclerosis patients. There was a positive trend towards restoring of hand motor skills and cognitive functions.

A linear discrete-time extended state observer-based intelligent PD controller for a 12 DOFs lower limb exoskeleton LLE-RePA

To perform the motion control of a 12 DOFs lower limb exoskeleton for rehabilitation and power augmentation (LLE-RePA), this paper proposes a linear discrete-time extended state observer (LDESO) based intelligent PD (iPD) controller with sigmoid function-based tracking differentiator (STD). A three-order discrete-time STD is designed to obtain smooth real-time reference velocity and acceleration signal. The adopted iPD controller is a kind of model free method based on ultra-local model, which formulates complex human-exoskeleton dynamics in a simple way and make closed-loop PD parameters easy to be tuned. The LDESO is utilized to estimate and eliminate the lumped total disturbance in ultra-local model which contains unknown model dynamics and unpredictable human-exoskeleton interaction influence. The estimation result of LDESO is used to compensate the control input. Simulation and experimental results with exoskeleton LLE-RePA demonstrate the high performance of the proposed method.

Development of MMG sensors using PVDF piezoelectric electrospinning for lower limb rehabilitation exoskeleton

In this study, a new type of fiber-based mechanomyography (MMG) sensor was developed and used as a motion on/off trigger sensor for a lower limb rehabilitation exoskeleton (LLRE). Piezoelectric material, polyvinylidene difluoride (PVDF), was applied to fabricate a MMG sensor, using the near-field electrospinning technology to electrically spin PVDF fibers to ∼5-10 μm in diameter. PVDF fibers attached on an interdigitated (IDT) electrode with different pole pairs and interspaces were packaged into a MMG sensor. This MMG sensor was stuck on the thigh muscles to detect and acquire human motion intention signal, then trigger the controller and actuate the LLRE. The LLRE multi-axis control system included a master and four slave controllers. The master controller released the command signal to control the slave controllers via controller area network (CAN). A multi-axis control system was developed to actuate the joints of hips and knees of both legs by the LLRE. In the study, commercial electromyography (EMG) sensors were also to compare with the as-made MMG sensors. Results showed that the maximum signal amplitude of the commercail EMG sensor was about ∼0.2 V, whereas the MMG sensor was about ∼2.8 V. The signal-to-noise ratio (SNR) of EMG was about ∼4, while that of MMG was about ∼25. From physiological signals captured from the MMG sensor for the human walking with the LLRE, this new sensor improved the sensitivity in driving the LLRE.

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Stroke is a standout amongst the most imperative reasons of incapacity on the planet. Due to partial or full paralysis, the majority of patients are compelled to rely upon parental figures and caregivers in residual life. With post-stroke rehabilitation, different types of assistive technologies have been proposed to offer developments to the influenced body parts of the incapacitated. In a large portion of these devices, the clients neither have control over the tasks nor can get feedback concerning the status of the exoskeleton. Additionally, there is no arrangement to detect user movements or accidental fall. The proposed framework tackles these issues utilizing a brain-controlled lower limb exoskeleton (BCLLE) in which the exoskeleton movements are controlled based on user intentions. An adaptive mechanism based on sensory feedback is integrated to reduce the system false rate. The BCLLE uses a flexible design which can be customized according to the degree of disability. The exoskeleton is modeled according to the human body anatomy, which makes it a perfect fit for the affected body part. The BCLLE system also automatically identifies the status of the paralyzed person and transmits information securely using Novel-T Symmetric Encryption Algorithm (NTSA) to caregivers in case of emergencies. The exoskeleton is fitted with motors which are controlled by the brain waves of the user with an electroencephalogram (EEG) headset. The EEG headset captures the human intentions based on the signals acquired from the brain. The brain-computer interface converts these signals into digital data and is interfaced with the motors via a microcontroller. The microcontroller controls the high torque motors connected to the exoskeleton's joints based on user intentions. Classification accuracy of more than 80% is obtained with our proposed method which is much higher compared with all existing solutions.

Enhanced neural network control of lower limb rehabilitation exoskeleton by add-on repetitive learning

Abstract This paper addresses neural network (NN) control of a lower limb exoskeleton for rehabilitation. Two control schemes are presented for exoskeleton to conduct trajectory tracking tasks, in the presence of unmodeled dynamics of exoskeleton, interaction between human and exoskeleton, as well as additional disturbances. The first controller is purely NN-based, and a combined error factor (CEF), which consists of the weighted sum of tracking error and its derivative, is adopted to enhance the human safety by improved transient response. The second controller is developed based on a combined scheme of repetitive learning control (RLC) and NN, where the add-on RLC is used to learn periodic uncertainties that attribute to the repetitive motion of the exoskeleton leg. Stabilities of the controllers are proved rigorously in a Lyapunov way. It is worthwhile to highlight that although the pure NN controller can deal with periodic and non-periodic uncertainties simultaneously, the main feature of exoskeleton motion during rehabilitation therapy, namely, the repetitiveness, is fully ignored, thus could degrade the tracking performance. Compared simulation reveals that, the proposed control method has achieved a significant control effect with remarkable transient performance.

Lower Limb Exoskeleton for Rehabilitation with Improved Postural Equilibrium

In this work we present a lower limb haptic exoskeleton suitable for patient rehabilitation, specifically in the presence of illness on postural equilibrium. Exoskeletons have been mostly conceived to increase strength, while in this work patient compliance with postural equilibrium enhancement is embedded. This is achieved with two hierarchical feedback loops. The internal one, closing the loop on the joint space of the exoskeleton offers compliance to the patient in the neighborhood of a reference posture. It exploits mechanical admittance control in a position loop, measuring the patient’s Electro Miographical (EMG) signals. The problem is solved using multi variable robust control theory with a two degrees of freedom setting. A second control loop is superimposed on the first one, operating on the Cartesian space so as to guarantee postural equilibrium. It controls the patient’s Center of Gravity (COG) and Zero Moment Point (ZMP) by moving the internal loop reference. Special attention has been devoted to the mechanical multi-chain model of the exoskeleton which exploits Kane’s method using the Autolev symbolic computational environment. The aspects covered are: the switching system between single and double stance, the system’s non-holonomic nature, dependent and independent joint angles, redundancy in the torque controls and balancing weight in double stance. Physical experiments to validate the compliance method based on admittance control have been performed on an elbow joint at first. Then, to further validate the haptic interaction with the patient in a realistic situation, experiments have been conducted on a first exoskeleton prototype, while the overall system has been simulated in a realistic case study.