Lower Limb Exoskeleton Robot Research Articles

Progress and development trends of lower limb exoskeleton rehabilitation robots

Abstract. Currently, as the aging population intensifies, many elderly people are trapped by lower limb dysfunctions caused by diseases such as stroke sequelae, spinal cord injuries, and bone injuries. Research on lower limb exoskeleton robots has made progress both domestically and internationally. Foreign lower limb exoskeleton robot technology is very mature and widely applied; although starting later, domestic development in this field has been rapid. This paper studies the key technologies of lower limb exoskeleton robots from several aspects: the degrees of freedom in human lower limb movement, the cyclic movement of human gait, structural design, driving methods, and interactive control. With the continuous advancement of technology, lower limb exoskeleton robots will become more intelligent and focus more on personalized services and wearing comfort in future development, aiming to bring more convenience and help to disabled individuals, rehabilitation patients, and groups with special needs.

Structural Design and Experimental Analysis of the Self-Balancing Lower Limb Exoskeleton Robot

To facilitate walking rehabilitation training for individuals with lower limb paralysis, a self-balancing exoskeleton robot with 12 degrees of freedom was conceived. The principal structural design was conducted in line with the biomechanics of the human lower limbs, and a kinematic model was formulated. The stipulated gait was resolved by reverse kinematics in MATLAB to derive the joint angle actuation curves. These curves served as the motive input in ADAMS kinematic simulation experiments, yielding a gait trajectory with an error margin of less than 2 mm compared to the prearranged gait, which is within a reasonable range of deviation. Experiments involving walking with the exoskeleton were also executed. The analysis of the six-axis force sensor data from the sole demonstrated that the ground reaction force curve consistently remained within the bounds of the foot’s support area, substantiating the exoskeleton’s capability for stable ambulation with a load. The simulations and walking experiments together verified the soundness of the exoskeleton’s structural design.

Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals

The human lower limb movements recognition (LLMR) plays a pivotal role in active lower limb exoskeleton robots. Employing surface electromyography (sEMG) signals for LLMR allows for the convenient, rapid and stable capture of signal variations, facilitating efficient identification of lower limb motion patterns. However, current sEMG-based LLMR methods face challenges such as incomplete feature extraction, limited contextual information and restricted feature extraction scales during feature extraction. This paper proposed a LLMR method based on Gramian Angular Fields (GAF) and multiscale fusion of Residual Neural Network (MS-ResNet). The denoised sEMG time series was transformed into Gramian Angular Difference Field (GADF) matrix based on GAF. The MS-ResNet model, incorporating ResNet and multiscale feature fusion concepts, was proposed to comprehensively capture global and local information through different-scale feature extraction and fusion, so as to improve recognition performance. sEMG signals from 11 muscles of the preferred leg of 15 healthy subjects were recorded during six common lower limb movements. Experimental analysis investigated the impact of the convolutional kernel size (k × k) in Stream 2 of MS-ResNet and the number of muscles involved on recognition performance. The study revealed that selecting k as 13, coupled with 11 muscles, yielded optimal model performance with the average cross-individual recognition accuracy reaching 97.62 %, demonstrating the model’s efficiency in LLMR. This method could provide a viable solution for developing more efficient and reliable LLMR systems, applicable to lower limb exoskeleton robots and intelligent prosthetics.

Development of an individualized stable and force-reducing lower-limb exoskeleton

In this study, an individualized and stable passive-control lower-limb exoskeleton robot was developed. Users’ joint angles and the center of pressure (CoP) of one of their soles were input into a convolutional neural network (CNN)–long short-term memory (LSTM) model to evaluate and adjust the exoskeleton control scheme. The CNN–LSTM model predicted the fitness of the control scheme and output the results to the exoskeleton robot, which modified its control parameters accordingly to enhance walking stability. The sole’s CoP had similar trends during normal walking and passive walking with the developed exoskeleton; the y-coordinates of the CoPs with and without the exoskeleton had a correlation of 91%. Moreover, electromyography signals from the rectus femoris muscle revealed that it exerted 40% less force when walking with a stable stride length in the developed system than when walking with an unstable stride length. Therefore, the developed lower-limb exoskeleton can be used to assist users in achieving balanced and stable walking with reduced force application. In the future, this exoskeleton can be used by patients with stroke and lower-limb weakness to achieve stable walking.

Optimization of Joint Space Trajectories for Assistive Lower Limb Exoskeleton Robots: Real-Time and Flexible Gait Patterns

This research focuses on designing a real-time, flexible gait planner for lower limb exoskeleton robots to assist patients with lower limb disabilities. Given the dynamic nature of gait parameters, which vary according to ground conditions and user intent, the challenge lies in developing a gait planner capable of adapting to these changes in real-time. To avoid planning complications in the cartesian space and to comply with the speed constraints of joint motors, this paper proposes planning in joint space. Furthermore, the approach also considers the maximum speed capabilities of the joint motors, aiming to generate an executable gait pattern and simultaneously enhance the robot’s walking speed by determining the minimum time required for implementation. The introduced gait planner optimizes joint trajectories for minimal angular acceleration. To provide flexibility, generalized boundary conditions suitable for different scenarios are defined. The effectiveness of the proposed planner is validated through comprehensive performance analysis, simulations, and successful implementation trials on the Exoped® robot in various scenarios.

Active Power Assist with Equivalent Force on Connection for Lower Limb Exoskeleton Robots

Active power-assist lower limb exoskeleton robots aim to enhance wearer assistance while ensuring wearer comfort and simplifying the exoskeleton’s design and control. This study proposes an active assistance method known as Equivalent Force on Connection (EFOC). The EFOC method effectively addresses the limitations encountered in conventional Joint Torque Proportional Compensation (JTPC) approaches. These limitations include the necessity for exoskeleton robot configurations to align with human limb structures for parallel assistance at each lower limb joint, as well as the exoskeleton’s inability to contribute a greater proportion of assistance due to the excessive load on specific skeletal and muscular structures, resulting in wearer discomfort. Furthermore, the effectiveness of the EFOC method is evaluated and validated for assistance during both the stance and swing phases of single-leg movements. Finally, the proposed EFOC method is implemented on a hydraulic-driven lower limb exoskeleton robot to assist wearers in squatting, stepping, and jumping locomotion. The experimental results demonstrate that the proposed EFOC method can effectively achieve the desired assistance effect.

BiLSTM-KNN network for locomotion mode recognition in lower limb exoskeleton robot

Abstract Lower limb exoskeleton robots have become a research hotspot among various enterprises, universities, and institutions. This paper proposes a deep learning network based on the recognition of five movement patterns of lower limb exoskeletons, including walking on level ground, going upstairs, going downstairs, uphill, and downhill. The network uses BiLSTM to capture the forward and backward relationships of time series data, which has better resolution than the original features. In addition, the network uses the KNN algorithm based on shortest-distance voting to improve the recognition accuracy. The network achieved a recognition rate of 99.18% for five motion patterns. In summary, the proposed network has excellent performance in terms of accuracy and generalization.

Current research on exoskeletal lower limb rehabilitation robots

Stroke is characterized by a high incidence rate, and walking is one of the essential human functions. Lower limb rehabilitation robots can offer safe and effective walking training to patients with walking impairments caused by conditions such as neural injuries, aiding in their recovery or assisting in walking. For the needs of patients themselves, restoring their physical state and motor function as much as possible is the primary purpose of rehabilitation, and rehabilitation efficiency is an important factor affecting the quality of life of patients after surgery. Research on exoskeleton robots varies both domestically and internationally, each with its own unique characteristics and features. This paper outlines the design of lower limb rehabilitation robots based on gait analysis and control methods. It also provides an overview of the current state of research on lower limb exoskeleton robots in terms of degrees of freedom, actuation methods, and clinical applications. Furthermore, it offers insights into future directions for research on lower limb exoskeleton robots.

Adaptive backstepping sliding mode subject-cooperative control for a pediatric lower-limb exoskeleton robot

The passive-assist rehabilitation mode with a fixed desired trajectory neglects the subject’s active involvement and degrades the therapeutic performance in case of partial muscle strength. Therefore, this study proposes a novel subject-cooperative control based on a variable admittance control scheme and a robust trajectory control scheme for a pediatric lower-limb exoskeleton robot. Initially, the system description and dynamic modeling are briefly explained. Thereafter, a neural-fuzzy–based variable admittance control ( nf VAC) is designed to incorporate a realistic subject-exoskeleton interaction and consider the subject’s active participation. Finally, a robust adaptive backstepping sliding mode control with rapid reaching law is used to handle parametric uncertainties and external disturbances. A stepwise selection of Lyapunov functions is utilized to address the stability of the trajectory control. The effectiveness of the proposed adaptive backstepping sliding mode–neural-fuzzy variable admittance control (ABSM- nf VAC) scheme is compared with two contrast control schemes, namely, adaptive backstepping-fixed admittance control (AB-FAC) and adaptive terminal sliding mode-fuzzy variable admittance control (ATSM- f VAC) for the active-assist mode with the effect of sudden reflex. Based on the numerical results, the suggested cooperative controller has demonstrated favorable tracking performance, compliant interaction, and safety aspects during gait training.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots.

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human-machine-environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions.

Design and Simulation of Exoskeleton Robot

Based on the research of lower limb exoskeleton robots and extensive literature review, combined with human anatomy and kinematics, a four-degree-of-freedom lower limb exoskeleton robot was designed, and its three-dimensional model was established using UG software. In the design of the exoskeleton robot, it is required to have a simple structure, zero sensitivity in response speed, and be easy to realize automatic control. The exoskeleton robot designed in this paper uses electric push rods as the driving elements, and the dimensions of each part of the robot are obtained from the national standard human body size table; based on the analysis of the human gait cycle, and using cubic polynomial interpolation to plan the spatial posture of each joint; Adams was used to simulate the movement of the exoskeleton robot, and the motion curves of each joint were obtained.

Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

BackgroundThis research focused on the development of a motor imagery (MI) based brain–machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.MethodsA total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants’ neural activity using the second deep learning approach for the decoding.ResultsThe three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.ConclusionThis research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study’s discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Can lower-limb exoskeletons support sit-to-stand motions in frail elderly without crutches? A study combining optimal control and motion capture.

With the global geriatric population expected to reach 1.5 billion by 2050, different assistive technologies have been developed to tackle age-associated movement impairments. Lower-limb robotic exoskeletons have the potential to support frail older adults while promoting activities of daily living, but the need for crutches may be challenging for this population. Crutches aid safety and stability, but moving in an exoskeleton with them can be unnatural to human movements, and coordination can be difficult. Frail older adults may not have the sufficient arm strength to use them, or prolonged usage can lead to upper limb joint deterioration. The research presented in this paper makes a contribution to a more detailed study of crutch-less exoskeleton use, analyzing in particular the most challenging motion, sit-to-stand (STS). It combines motion capture and optimal control approaches to evaluate and compare the STS dynamics with the TWIN exoskeleton with and without crutches. The results show trajectories that are significantly faster than the exoskeleton's default trajectory, and identify the motor torques needed for full and partial STS assistance. With the TWIN exoskeleton's existing motors being able to support 112 Nm (hips) and 88 Nm (knees) total, assuming an ideal contribution from the device and user, the older adult would need to contribute a total of 8 Nm (hips) and 50 Nm (knees). For TWIN to provide full STS assistance, it would require new motors that can exert at least 121 Nm (hips) and 140 Nm (knees) total. The presented optimal control approaches can be replicated on other exoskeletons to determine the torques required with their mass distributions. Future improvements are discussed and the results presented lay groundwork for eliminating crutches when moving with an exoskeleton.

Estimating human joint moments unifies exoskeleton control, reducing user effort.

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

Coordinated energy-efficient walking assistance for paraplegic patients by using the exoskeleton-walker system

Overground walking can be achieved for patients with gait impairments by using the lower limb exoskeleton robots. Since it is a challenge to keep balance for patients with insufficient upper body strength, a robotic walker is necessary to assist with the walking balance. However, since the walking pattern varies over time, controlling the robotic walker to follow the walking of the human-exoskeleton system in coordination is a critical issue. Inappropriate control strategy leads to the unnecessary energy cost of the human-exoskeleton-walker (HEW) system and also results in the bad coordination between the human-exoskeleton system and the robotic walker. In this paper, we proposed a Coordinated Energy-Efficient Control (CEEC) approach for the HEW system, which is based on the extremum seeking control algorithm and the coordinated motion planning strategy. First, the extremum seeking control algorithm is used to find the optimal supporting force of the support joint in real time to maximize the energy efficiency of the human-exoskeleton system. Second, the appropriate reference joint angles for wheels of the robotic walker can be generated by the coordinated motion planning strategy, causing the good coordination between the human-exoskeleton system and the robotic walker. The proposed approach has been tested on the HEW simulation model, and the experimental results indicate that the coordinated energy-efficient walking can be achieved with the proposed approach, which is increased by 60.16% compared to the conventional passive robotic walker.

RBFNN-Based Adaptive Integral Sliding Mode Feedback and Feedforward Control for a Lower Limb Exoskeleton Robot

In this paper, an adaptive trajectory tracking control method combining proportional–integral–derivative (PID) control, Radial Basis Function neural network (RBFNN)-based integral sliding mode control (ISMC), and feedforward control, i.e., the PIDFF-ISMC method, is proposed. The PIDFF-ISMC method aims to deal with the dynamic uncertainties, disturbances, and slow response in lower limb exoskeleton robot systems. Firstly, the Lagrange function is utilized to establish dynamic models that include frictional force and unmodeled dynamics. Secondly, the feedback controller is composed of PID and RBFNN-based ISMC to improve tracking performance and decrease the chattering phenomenon. The feedforward controller is adopted to reduce the response time by employing inverse dynamic models. Finally, the Lyapunov function proves the stability of the proposed control method. The experimental results show that the proposed control method can effectively reduce the trajectory tracking error and response time at two different speeds while alleviating control input chattering.

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting.

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.

Generation & Clinical Validation of Individualized Gait Trajectory for Stroke Patients Based on Lower Limb Exoskeleton Robot

Generation & Clinical Validation of Individualized Gait Trajectory for Stroke Patients Based on Lower Limb Exoskeleton Robot

Effect of robotic exoskeleton training on lower limb function, activity and participation in stroke patients: a systematic review and meta-analysis of randomized controlled trials.

The current lower limb robotic exoskeleton training (LRET) for treating and managing stroke patients remains a huge challenge. Comprehensive ICF analysis and informative treatment options are needed. This review aims to analyze LRET' s efficacy for stroke patients, based on ICF, and explore the impact of intervention intensities, devices, and stroke phases. We searched Web of Science, PubMed, and The Cochrane Library for RCTs on LRET for stroke patients. Two authors reviewed studies, extracted data, and assessed quality and bias. Standardized protocols were used. PEDro and ROB2 were employed for quality assessment. All analyses were done with RevMan 5.4. Thirty-four randomized controlled trials (1,166 participants) were included. For function, LRET significantly improved motor control (MD = 1.15, 95%CI = 0.29-2.01, p = 0.009, FMA-LE), and gait parameters (MD = 0.09, 95%CI = 0.03-0.16, p = 0.004, Instrumented Gait Velocity; MD = 0.06, 95%CI = 0.02-0.09, p = 0.002, Step length; MD = 4.48, 95%CI = 0.32-8.65, p = 0.04, Cadence) compared with conventional rehabilitation. For activity, LRET significantly improved walking independence (MD = 0.25, 95%CI = 0.02-0.48, p = 0.03, FAC), Gait Velocity (MD = 0.07, 95%CI = 0.03-0.11, p = 0.001) and balance (MD = 2.34, 95%CI = 0.21-4.47, p = 0.03, BBS). For participation, social participation (MD = 0.12, 95%CI = 0.03-0.21, p = 0.01, EQ-5D) was superior to conventional rehabilitation. Based on subgroup analyses, LRET improved motor control (MD = 1.37, 95%CI = 0.47-2.27, p = 0.003, FMA-LE), gait parameters (MD = 0.08, 95%CI = 0.02-0.14, p = 0.006, Step length), Gait Velocity (MD = 0.11, 95%CI = 0.03-0.19, p = 0.005) and activities of daily living (MD = 2.77, 95%CI = 1.37-4.16, p = 0.0001, BI) for the subacute patients, while no significant improvement for the chronic patients. For exoskeleton devices, treadmill-based exoskeletons showed significant superiority for balance (MD = 4.81, 95%CI = 3.10-6.52, p < 0.00001, BBS) and activities of daily living (MD = 2.67, 95%CI = 1.25-4.09, p = 0.00002, BI), while Over-ground exoskeletons was more effective for gait parameters (MD = 0.05, 95%CI = 0.02-0.08, p = 0.0009, Step length; MD = 6.60, 95%CI = 2.06-11.15, p = 0.004, Cadence) and walking independence (MD = 0.29, 95%CI = 0.14-0.44, p = 0.0002, FAC). Depending on the training regimen, better results may be achieved with daily training intensities of 45-60 min and weekly training intensities of 3 h or more. These findings offer insights for healthcare professionals to make effective LRET choices based on stroke patient needs though uncertainties remain. Particularly, the assessment of ICF participation levels and the design of time-intensive training deserve further study. https://www.crd.york.ac.uk/PROSPERO, Unique Identifier: CRD42024501750.