Lower Limb Rehabilitation Research Articles

Position–Force Control of a Lower-Limb Rehabilitation Robot Using a Force Feed-Forward and Compensative Gravity Proportional Derivative Method

The design and control of lower-limb rehabilitation robots for patients after a stroke has gained significant attention. This paper presents the dynamic analysis and control of a 3-degrees-of-freedom lower-limb rehabilitation robot using combined position–force control based on the force feed-forward and compensative gravity proportional derivative methods. In the lower-limb rehabilitation robot, the interaction force between the patient with the joints and links of the robot is uncertain and nonlinear due to the disturbance effect of Coriolis force, centrifugal force, gravitational force, and friction force. During recovery stages, the forces exerted by the patient’s lower limbs are also considered disturbances. Therefore, to meet the quality requirements in using the rehabilitation robot with different recovery stages of patient training, combining position control and force control is essential. In this paper, we proposed a combination of proportional–derivative gravity compensation motion control and force feed-forward control to form an advanced combined controller (position–force feed-forward control—PFFC) for a 3 DOF lower-limb functional rehabilitation robot. The forces can be sensed using a 3-axis force sensor. In addition, the robot’s position parameters are also measured by encoders. The control algorithm is implemented on the STM32F4 Discovery board. A verified test of the proposed control method is shown in the experiments, showing the good performance of the system.

Upper and Lower Limb Training Evaluation System Based on Virtual Reality Technology.

Upper and lower limb rehabilitation training is essential for restoring patients' physical movement ability and enhancing muscle strength and coordination. However, traditional rehabilitation training methods have limitations, such as high costs, low patient participation, and lack of real-time feedback. The purpose of this study is to design and implement a rehabilitation training evaluation system based on virtual reality to improve the quality of patients' rehabilitation training. This paper proposes an upper and lower limb rehabilitation training evaluation system based on virtual reality technology, aiming to solve the problems existing in traditional rehabilitation training. The system provides patients with an immersive and interactive rehabilitation training environment through virtual reality technology, aiming to improve patients' participation and rehabilitation effects. This study used Kinect 2.0 and Leap Motion sensors to capture patients' motion data and transmit them to virtual training scenes. The system designed multiple virtual scenes specifically for different upper and lower limbs, with a focus on hand function training. Through these scenes, patients can perform various movement training, and the system will provide real-time feedback based on the accuracy of the patient's movements. The experimental results show that patients using the system show higher participation and better rehabilitation training effects. Compared with patients receiving traditional rehabilitation training, patients using the virtual reality system have significantly improved movement accuracy and training participation. The virtual reality rehabilitation training evaluation system developed in this study improves the quality of patients' rehabilitation and provides personalized treatment information to medical personnel through data collection and analysis, promoting the systematization and personalization of rehabilitation training. This system is innovative and has broad application potential in the field of rehabilitation medicine.

Feasibility of a new bicycle ergometer with adjustable pedaling configuration for personalized lower limb rehabilitation

Muscle atrophy due to prolonged immobilization leads to severe dysfunction and progression of disease and injury. This highlights the necessity for early rehabilitation, even during the non-ambulatory stages. As manifestations vary among individuals, target-specific rehabilitation is crucial for achieving optimal outcomes. Conventional bicycle ergometers, despite their wide usage in rehabilitation and sports training, fail to adequately recruit the muscles that predominantly operate in the coronal or axial plane. This is thought to be due to the restriction of pedaling trajectory in the sagittal plane. This study introduces a new bicycle ergometer capable of tilting the pedaling plane and adjusting the pedal orientation, in order to alter muscle recruitment patterns and limb alignments. A biomechanical analysis of eight healthy volunteers suggests that our device can elicit dynamic valgus/varus alignment and alter the axial rotations of the lower extremities. An electromyography analysis revealed a significant increase in the activation of specific muscle groups, particularly the abductors and adductors, suggesting promising results for targeted muscle recruitment. We expect this ergometer to provide a new method for target-specific rehabilitation and the treatment of bedridden patients or those with weak muscles.

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.

A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking

Lower limb exoskeletons represent a relevant tool for rehabilitating gait in patients with lower limb movement disorders. Partial assistance exoskeletons adaptively provide the joint torque needed, on top of that produced by the patient, for a correct and stable gait, helping the patient to recover an autonomous gait. Thus, the device needs to identify the different phases of the gait cycle to produce precisely timed commands that drive its joint motors appropriately. In this study, EMG signals have been used for gait phase detection considering that EMG activations lead limb kinematics by at least 120 ms. We propose a deep learning model based on bidirectional LSTM to identify stance and swing gait phases from EMG data. We built a dataset of EMG signals recorded at 1500 Hz from four muscles from the dominant leg in a population of 26 healthy subjects walking overground (WO) and walking on a treadmill (WT) using a lower limb exoskeleton. The data were labeled with the corresponding stance or swing gait phase based on limb kinematics provided by inertial motion sensors. The model was studied in three different scenarios, and we explored its generalization abilities and evaluated its applicability to the online processing of EMG data. The training was always conducted on 500-sample sequences from WO recordings of 23 subjects. Testing always involved WO and WT sequences from the remaining three subjects. First, the model was trained and tested on 500 Hz EMG data, obtaining an overall accuracy on the WO and WT test datasets of 92.43% and 91.16%, respectively. The simulation of online operation required 127 ms to preprocess and classify one sequence. Second, the trained model was evaluated against a test set built on 1500 Hz EMG data. The accuracies were lower, yet the processing times were 11 ms faster. Third, we partially retrained the model on a subset of the 1500 Hz training dataset, achieving 87.17% and 89.64% accuracy on the 1500 Hz WO and WT test sets, respectively. Overall, the proposed deep learning model appears to be a valuable candidate for entering the control pipeline of a lower limb rehabilitation exoskeleton in terms of both the achieved accuracy and processing times.

Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation

Abstract Recent advances in robotics and artificial intelligence have highlighted the potential for the integration of computational intelligence in enhancing the functionality and adaptability of robotic systems, particularly in rehabilitation. Designing robotic exoskeletons for the lower limb rehabilitation of post-stroke patients requires frequent adjustments to accommodate individual differences in leg anatomy. This complex engineering challenge necessitates a deep understanding of human physiology, robotics, and optimization to develop adaptive robotic systems and also to swiftly quantify the required adjustments and implement them for each patient. The conventional approaches, which mostly rely on heuristics and manual tuning, often struggle to achieve optimal results. This paper presents a novel method that integrates a genetic algorithm (GA) with a deep learning approach to generate a gait trajectory of the ankle joint from a six-bar linkage mechanism of fixed dimensions. Later, using the same approach, the inverse kinematics solution for this mechanism is also devised whereby, the set of the link dimensions of the six-bar linkage mechanism is obtained for the given gait trajectory of an individual to achieve customization. We simulated the kinematic behavior of the six-bar linkage mechanism within defined mechanical constraints and utilized the generated data for training a feedforward neural network and long short-term memory models. The proposed model, when trained, can produce accurate lengths for the desired gait trajectories in the sagittal plane and vice-versa which further validates our proposed approach for inverse kinematics solution.

Application of blood flow restriction training in lower limb rehabilitation

With the popularity of national fitness campaigns, sports injuries, especially lower limb injuries such as anterior cruciate ligament tears and ankle sprains, are increasing year by year. There are limitations in traditional treatment such as muscle atrophy. In recent years, the blood flow restriction training method (BFRT) which simulates high-intensity exercise with low intensity and limits arterial and venous blood flow to promote muscle growth and strength enhancement, has drawn attention as a novel approach to rehabilitation. This article reviews the application of BFRT in the rehabilitation of lower limb injuries, discusses its effects on muscle function, pain management and quality of life, and evaluates its safety and prospects for clinical application. BFRT reduces mechanical stress, relieves pain, improves endurance and aerobic capacity, and improves cardiovascular function. However, there are discomfort and potential risks, such as muscle injury and thrombosis, which require individualised adjustments and standardised training. BFRT has shown positive result(reducing pain &oedema and improving function)s in rehabilitation of ACL reconstruction (ACLR) and chronic ankle instability (CAI). In addition, BFRT has been shown to aid in the functional recovery of the lower extremity in patients with Achilles tendon injuries and strokes. Despite the positive effects of BFRT, relevant studies have problems such as small sample sizes and non-uniform parameters. BFRT is considered a safe training modality, but contraindications should be examined before clinical application. Future research endeavours should investigate the impact of BFRT in many rehabilitation contexts and enhance tailored rehabilitation strategies. In order to maximize the therapeutic benefit, a rehabilitation training program should be developed and refined with a minimum effective dose based on the quantification of the in vivo physiological response to BFRT. The identification of the optimal prognostic approach will help to advance the standardisation of BFR treatment methods.

Active Disturbance Rejection Control via Neural Networks for a Lower-Limb Exoskeleton

This article presents the design of a control algorithm based on Artificial Neural Networks (ANNs) applied to a lower-limb exoskeleton, which is aimed to carry out walking trajectories during lower-limb rehabilitation. The interaction between the patient and the exoskeleton leads to model uncertainties and external disturbances that are always present. For this reason, the proposed control considers that the non-linear part of the model is unknown and is perturbed by external disturbances, which are estimated by an active disturbance rejection control via Artificial Neural Networks. To validate the proposed approach, a numerical simulation and an experimental implementation of the ANN-Controller are developed.

State of the Art of Brain Function Detection Technologies in Robot-Assisted Lower Limb Rehabilitation.

Background: With an aging population, the prevalence of neurological disorders is increasing, leading to a rise in lower limb movement disorders and, in turn, a growing need for rehabilitation training. Previous neuroimaging studies have shown a growing scientific interest in the study of brain mechanisms in robot-assisted lower limb rehabilitation (RALLR). Objective: This review aimed to determine differences in neural activity patterns during different RALLR tasks and the impact on neurofunctional plasticity. Methods: Sixty-five articles in the field of RALLR were selected and tested using three brain function detection technologies. Results: Most studies have focused on changes in activity in various regions of the cerebral cortex during different lower limb rehabilitation tasks but have also increasingly focused on functional changes in other cortical and deep subcortical structures. Our analysis also revealed a neglect of certain task types. Conclusion: We identify and discuss future research directions that may contribute to a clear understanding of neural functional plasticity under different RALLR tasks. Impact Statement The evaluation of robot-assisted lower limb rehabilitation based on brain function detection technology can assess the neurological changes of patients in the rehabilitation process by monitoring brain activities and can also provide more accurate guidance for robot-assisted lower limb rehabilitation. By monitoring the patient's brain activity, the robot can adjust according to the real-time status of the patient to achieve more effective rehabilitation training. This has potential impact on improving the rehabilitation effect and speeding up the rehabilitation process of patients.

Customized stiffness control strategy for a six-bar linkage-based gait rehabilitation robot

Abstract Lower limb rehabilitation robots based on linkage-based mechanisms have recently drawn significant attention in the field due to their numerous advantages. The control of previously proposed linkage-based gait rehabilitation robotic orthoses has been achieved using constant speed control without consideration for the interaction forces. However, such an approach can be harmful to people with stroke since the level of disability varies among individuals, and it may cause potential injuries when excessive force is applied by the robot. To overcome this limitation and improve the rehabilitation process, it is necessary to recognize the force exerted by the person during walking and adjust the robot’s assistive torque accordingly, to provide synchronized motion. Thus, in this work, a human-cooperative approach based on a stiffness control strategy for the six-bar linkage-based gait rehabilitation robot is presented. The proposed methodology can serve as a solid foundation for developing a human-cooperative approach for linkage-based lower limb rehabilitation robotic orthoses. The control was validated and tested with eight healthy human subjects. As a result, customized robotic assistance with this mechanism can be provided during training to meet the individual needs of stroke patients, which can lead to increased engagement and contribution, thus improving treatment outcomes.

Prediction of abnormal gait behavior of lower limbs based on depth vision

Abstract As a kind of lower-limb motor assistance device, the intelligent walking aid robot plays an essential role in helping people with lower-limb diseases to carry out rehabilitation walking training. In order to enhance the safety performance of the lower-limb walking aid robot, this study proposes a deep vision-based abnormal lower-limb gait prediction model construction method for the problem of abnormal gait prediction of patients’ lower limbs. The point cloud depth vision technique is used to acquire lower-limb motion data, and a multi-posture angular prediction model is trained using long and short-term memory networks to build a model of the user’s lower-limb posture characteristics during normal walking as well as a real-time lower-limb motion prediction model. The experimental results indicate that the proposed lower-limb abnormal behavior prediction model is able to achieve a 97.4% prediction rate of abnormal lower-limb movements within 150 ms. Additionally, the model demonstrates strong generalization ability in practical applications. This paper proposes further ideas to enhance the safety performance of lower-limb rehabilitation robot use for patients with lower-limb disabilities.

Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm.

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR's right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR.

Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection

Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.

Activities-specific balance confidence scale: a Rasch evaluation of the Arabic version in lower-limb prosthetic users

Purpose To evaluate the psychometric properties of the Arabic version of the Activities-Specific Balance Confidence Scale using the 5-option response categories for individuals with lower limb amputation (ABC-5/Ar). Materials and methods This was a methodological study on a convenience sample of individuals with unilateral lower-limb amputation attending outpatient rehabilitation centres in Saudi Arabia and Turkey (N = 155). Rasch analysis (WINSTEPS version 4.6.5) was used to examine the 5-categories rating scale structure, item fit, item difficulty hierarchy, person separation index, unidimensionality, local item dependency, and differential item functioning. Results The ABC-5/Ar 5-response option demonstrated an appropriate model fit. Most items fit the Rasch model, except for item #12 “walk in a crowded mall,” which showed an overfitting value of 0.63. The person separation indices 2.95 (Cronbach’s α = 0.96). Principal component analysis of residuals confirmed the unidimensionality of the scale; however, local dependency was detected between item #14 “Ride in escalator holding rail,” and item #15 “Ride in escalator not holding rail.” Conclusions The findings suggest that the ABC-5/Ar shows promise in assessing balance confidence in Arabic-speaking lower-limb prosthesis users. However, further studies with larger sample sizes and in diverse clinical contexts are needed to confirm its effectiveness in various clinical settings.

Effects of the intermittent theta burst stimulation on gait, balance and lower limbs motor function in stroke: study protocol for a double-blind randomised controlled trial with multimodal neuroimaging assessments

IntroductionApproximately, 50% of stroke survivors experience impaired walking ability 6 months after conventional rehabilitation and standard care. However, compared with upper limb motor function, research on lower limbs rehabilitation through...

Wearable gait recognition system based on time domain features of Bluetooth inertial sensor network

Abstract As an important part of medical monitoring, gait recognition has been used in lower limb rehabilitation diagnosis and gait assessment. To meet practical requirements, we designed a wearable gait recognition system based on time domain features of Bluetooth inertial sensor network, which can be used for real-time acquisition and evaluation of gait signals. In this research, we used 6 sensor units to integrate 10 kinds of gait information, and designed a gait monitoring system covering the main joints of lower limbs based on Bluetooth network. The gait monitoring system can collect the inertial sensing data of lower limbs at a maximum rate of 90 Hz through the 6 sensor units, and has good electrical performance. In daily use, each unit can work for more than 3 hours and maintain a received signal strength indicator (RSSI) of more than -90 dBm at a spatial distance of 10 meters. On the gait dataset collected in the experiment, we used sliding window to segment the time domain signals and extract 10 kinds of time domain features for classification algorithm. We achieved 97.1% average identification accuracy of 7 different gait patterns by support vector machine (SVM).

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.

Dimensional synthesis of motion generation of a planar four-bar mechanism

A motion generation method for a planar four-bar mechanism without prescribed timing is proposed in this article. A characteristic of coupler points is found: for the coupler points of a four-bar mechanism in a standard installation position rotated by the corresponding input angle clockwise around the origin of the Cartesian coordinate system xOy, the generated points lie on the feature coupler circles. Next, based on this, the design process is divided into three steps. In the first step, the relative input angles, the relative coupler angles and five geometric parameters of the desired four-bar mechanism are optimized. In the second step, the basic dimensional types and initial input angle are determined, and then, the input angles are obtained. In the third step, the real size of the desired linkage mechanism is calculated. Because the dimension of the design variables in each step is exceptionally small, the motion synthesis method can yield design solutions with high precision in a short time. Six comparison examples are presented to demonstrate the efficacy of the proposed method. In addition, the method is used to design a robot for lower limb rehabilitation for application to the human foot.

Hierarchical Trajectory Deformation Algorithm With Hybrid Controller for Active Lower Limb Rehabilitation

Hierarchical Trajectory Deformation Algorithm With Hybrid Controller for Active Lower Limb Rehabilitation

State of the Art on Lower Limb Rehabilitation Robots

Due to the increasing number of people suffering from walking disorders, physiotherapy is becoming the process of treating this dysfunction and helping people to recover their natural motor ability as much as possible. Due to the lack of therapists, rehabilitation robots aim to reduce their tasks, help patients recover their natural walking ability as much as possible, and promote self-rehabilitation. The present work reviewed the state of the art of lower limb rehabilitation robots and introduced their features, including structure, drive mode, control and safety precautions. Then, we discussed the problem of developing the current rehabilitation robots and the orientation towards the future.