Lower Limb Exoskeleton Research Articles

Task-agnostic exoskeleton control via biological joint moment estimation.

Lower-limb exoskeletons have the potential to transform the way we move1-14, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We introduce a task-agnostic controller that assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network. By estimating both hip and knee moments in-the-loop, our approach provided multi-joint, coordinated assistance through our autonomous, clothing-integrated exoskeleton. When deployed during 28 activities, spanning cyclic locomotion to unstructured tasks (for example, passive meandering and high-speed lateral cutting), the network accurately estimated hip and knee moments with an average R2 of 0.83 relative to ground truth. Further, our approach significantly outperformed a best-case task classifier-basedmethod constructed from splines and impedance parameters. When tested on ten activities (including level walking, running, lifting a 25 lb (roughly 11 kg) weight and lunging), our controller significantly reduced user energetics (metabolic cost or lower-limb biological joint work depending on the task) relative to the zero torque condition, ranging from 5.3 to 19.7%, without any manual controller modifications among activities. Thus, this task-agnostic controller can enable exoskeletons to aid users across a broad spectrum of human activities, a necessity for real-world viability.

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.

Design and analysis of a multi-motion self-balancing lower limb exoskeleton based on flat terrain

In this study, a multi-motion self-balancing lower limb exoskeleton is designed to provide crutches-free rehabilitation training for quadriplegic patients on flat terrain without hand support. First, based on the biomechanical characteristics of the human body and the bionic mechanism configuration of the human lower limb, an exoskeleton with a full-drive series-parallel hybrid structure was established. Secondly, the D-H method, geometric method and Newton iteration method were used to establish the forward and reverse kinematic solutions of the exoskeleton. In addition, the static self-balancing principle was used to complete the planning of the multi-motion self-balancing trajectory. Finally, in order to verify the effectiveness of the proposed method, some numerical simulations were performed in this paper. The theoretical trajectories of the waist, ankle joint and foot center were compared with the simulated trajectories. The research results show that the forward and reverse kinematics model of the exoskeleton is feasible. Under the condition of contact force parameters, the exoskeleton has the ability of multi-motion self-balancing walking on flat terrain.

Immersive virtual reality for learning exoskeleton-like virtual walking: a feasibility study

PurposeVirtual Reality (VR) has proven to be an effective tool for motor (re)learning. Furthermore, with the current commercialization of low-cost head-mounted displays (HMDs), immersive virtual reality (IVR) has become a viable rehabilitation tool. Nonetheless, it is still an open question how immersive virtual environments should be designed to enhance motor learning, especially to support the learning of complex motor tasks. An example of such a complex task is triggering steps while wearing lower-limb exoskeletons as it requires the learning of several sub-tasks, e.g., shifting the weight from one leg to the other, keeping the trunk upright, and initiating steps. This study aims to find the necessary elements in VR to promote motor learning of complex virtual gait tasks.MethodsIn this study, we developed an HMD-IVR-based system for training to control wearable lower-limb exoskeletons for people with sensorimotor disorders. The system simulates a virtual walking task of an avatar resembling the sub-tasks needed to trigger steps with an exoskeleton. We ran an experiment with forty healthy participants to investigate the effects of first- (1PP) vs. third-person perspective (3PP) and the provision (or not) of concurrent visual feedback of participants’ movements on the walking performance – namely number of steps, trunk inclination, and stride length –, as well as the effects on embodiment, usability, cybersickness, and perceived workload.ResultsWe found that all participants learned to execute the virtual walking task. However, no clear interaction of perspective and visual feedback improved the learning of all sub-tasks concurrently. Instead, the key seems to lie in selecting the appropriate perspective and visual feedback for each sub-task. Notably, participants embodied the avatar across all training modalities with low cybersickness levels. Still, participants’ cognitive load remained high, leading to marginally acceptable usability scores.ConclusionsOur findings suggest that to maximize learning, users should train sub-tasks sequentially using the most suitable combination of person’s perspective and visual feedback for each sub-task. This research offers valuable insights for future developments in IVR to support individuals with sensorimotor disorders in improving the learning of walking with wearable exoskeletons

Research on Human-Machine Synergy Control Method of Lower Limb Exoskeleton Based on Multisensor Fusion Information

Research on Human-Machine Synergy Control Method of Lower Limb Exoskeleton Based on Multisensor Fusion Information

Adaptive Vision-Based Gait Environment Classification for Soft Ankle Exoskeleton

Lower limb exoskeletons have been developed to improve functionality and assist with daily activities in various environments. Although these systems utilize sensors for gait phase detection, they lack anticipatory information about environmental changes, which limits their adaptability. This paper presents a vision-based intelligent gait environment detection algorithm for a lightweight ankle exosuit designed to enhance gait stability and safety for stroke patients, particularly during stair negotiation. The proposed system employs YOLOv8 for real-time environment classification, combined with a long short-term memory (LSTM) network for spatio-temporal feature extraction, enabling the precise detection of environmental transitions. An experimental study evaluated the classification algorithm and soft ankle exosuit performance through three conditions using kinematic analysis and muscle activation measurements. The algorithm achieved an overall accuracy of over 95% per class, which significantly enhanced the exosuit’s ability to detect environmental changes, and thereby improved its responsiveness to various conditions. Notably, the exosuit increased the ankle dorsiflexion angles and reduced the muscle activation during the stair ascent, which enhanced the foot clearance. The results of this study indicate that advanced spatio-temporal feature analysis and environment classification improve the exoskeleton’s gait assistance, improving adaptability in complex environments for stroke patients.

Enhancing gait recognition in lower limb exoskeletons: adaptive feature selection and random forest with Bayesian optimization

Abstract To improve human-machine cooperation and enhance the accuracy of gait recognition in wearable lower limb exoskeletons, an enhancement method of gait recognition based on adaptive feature selection and an optimized machine learning algorithm was proposed. In this study, surface electromyography (sEMG) signals of rectus femoris, medialis femoris, lateralis femoris, semitendinosus and biceps femoris were recorded during level-ground walking. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features, and the mean power frequency and median frequency were selected as the frequency domain features. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. To reduce feature dimension and improve the calculation efficiency, adaptive feature selection was performed by particle swarm optimization combined with sigmoid function. Then, the feature matrix was determined as the input for a random forest classifier to recognize different gait phases. An adaptive optimization mechanism based on Bayesian optimization was applied to random forest. Compared with random forest, the overall performance of the optimized model was improved. Its accuracy was increased by 3.57%. The feature selection and adaptive optimization mechanisms in gait recognition not only enhance the accuracy of random forest algorithms applied to sEMG for gait prediction, but also facilitate the flexibility and consistency required for lower limb exoskeleton gait control.

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.

Lower limb exoskeleton for gait rehabilitation with adaptive nonsingular sliding mode control

Abstract This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.

Variable Admittance Control of High Compatibility Exoskeleton Based on Human–Robotic Interaction Force

The wearable exoskeleton system is a typical strongly coupled human–robotic system. Human–robotic is the environment for each other. The two support each other and compete with each other. Achieving high human–robotic compatibility is the most critical technology for wearable systems. Full structural compatibility can improve the intrinsic safety of the exoskeleton, and precise intention understanding and motion control can improve the comfort of the exoskeleton. This paper first designs a physiologically functional bionic lower limb exoskeleton based on the study of bone and joint functional anatomy and analyzes the drive mapping model of the dual closed-loop four-link knee joint. Secondly, an exoskeleton dual closed-loop controller composed of a position inner loop and a force outer loop is designed. The inner loop of the controller adopts the PID control algorithm, and the outer loop adopts the adaptive admittance control algorithm based on human–robot interaction force (HRI). The controller can adaptively adjust the admittance parameters according to the HRI to respond to dynamic changes in the mechanical and physical parameters of the human–robot system, thereby improving control compliance and the wearing comfort of the exoskeleton system. Finally, we built a joint simulation experiment platform based on SolidWorks/Simulink to conduct virtual prototype simulation experiments and recruited volunteers to wear rehabilitation exoskeletons to conduct related control experiments. Experimental results show that the designed physiologically functional bionic exoskeleton and adaptive admittance controller can significantly improve the accuracy of human–robotic joint motion tracking, effectively reducing human–machine interaction forces and improving the comfort and safety of the wearer. This paper proposes a dual-closed loop four-link knee joint exoskeleton and a variable admittance control method based on HRI, which provides a new method for the design and control of exoskeletons with high compatibility.

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.

Gait self-learning control based on reference trajectory generation online for an asymmetric limb rehabilitation exoskeleton

Lower limb exoskeleton (LEX) are widely used to assist stoke survivors with walking dysfunction, which is lack of a more flexible trajectory and fails to address the control challenge posed by gait variability and asymmetry in rehabilitation training. This paper introduces an asymmetric self-learning lower exoskeleton (AS-LEX) based on reference trajectory generation for the affected side. Motor intent of the unaffected limb based on thresholds was identified to classify the gait phase of stance and swing. A parameterized gait trajectory was generated online, namely a combination of circular trajectory in the stance phase and an elliptical trajectory in the swing phase. Gait self-learning control is presented to make the affected limb adaptively learn the gait parameters generated by the unaffected limb. Feasibility of the AS-LEX is demonstrated experimentally using three healthy subjects. Resuls demonstrate that overground walking in a more natural speed (with a stride length 600 mm and 700 mm) make subjects more actively learn gait of the affected side from the unaffected side. Additionally, experiments of the fatigue level of the affected limb and human-robot interaction torques were carried out, and the results indicate a more natural gait and reduced interaction forces with the AS-LEX.

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.

Torque, speed, and power requirements for the design of a lower limb exoskeleton to augment human finned swimming

Abstract The authors seek to design a lower limb exoskeleton to augment human finned swimming; however, data associated with human finned swimming previously did not exist, particularly data that characterizes the active joint torque requirements for human-scale finned swimming motion and the corresponding thrust generation. Since these data are not directly measurable nor easily computed in human subject experiments, the authors instead employed a human-scale robotic platform to characterize the relationship between joint torque, speed, power, and thrust production during flutter kick swimming, specifically at the hip joints. Among the useful insights from this study: (1) the underwater environment can be accurately modeled as a simple viscous load as seen by the hip joints, where viscous coefficient depends on the type of fin; (2) accordingly, for a given fin, movement at any amplitude and frequency is invariant when motion is normalized by amplitude; velocity and torque by the product of amplitude and frequency; and power and thrust by the square of the product of amplitude and frequency; (3) the power-specific thrust is invariant, regardless of fin type, amplitude of motion, and frequency of motion; and 4) the phasing between right and left legs does not have a significant effect on thrust generation (i.e., kicking in-phase and kicking in opposition behave similarly). The authors hope this data will be useful to other researchers interested in developing lower limb exoskeletons to augment underwater human finned swimming.

A comprehensive review on lower limb exoskeleton: from origin to future expectations

A comprehensive review on lower limb exoskeleton: from origin to future expectations

Efficacy and safety of using a unilateral lower limb exoskeleton combined with conventional treatment in post-stroke rehabilitation: a randomized controlled trial.

The incidence of hemiplegia caused by stroke is high. In particular, lower limb dysfunction affects the daily activities of patients, and lower limb robotic devices have been proposed to provide rehabilitation therapy to improve balance function in this patient population. To assess the effectiveness of the LiteStepper® unilateral lower limb exoskeleton (ULLE) combined with conventional treatment for balance function training in patients with post-stroke hemiplegia. This multicenter randomized controlled trial, conducted in the convalescent rehabilitation ward of four hospitals, involved 92 patients in their post-stroke phase. Participants were randomized into an experimental group (EG) or a conventional group (CG). The EG adopted the LiteStepper® ULLE combined with conventional treatment for 21days. The CG underwent a standard daily rehabilitation routine for 21days. The Berg Balance Scale (BBS), Functional Ambulation Category scale (FAC), 6-min walk test (6MWT), and Barthel Index (Barthel) were used for evaluations before and after 21days of rehabilitative training. The BBS scores in EG was significantly elevated compared to CG, exhibiting a profound statistical difference (P< 0.0001). Notably, these disparities persisted at both day 21 (P < 0.0001) and day 14 (P < 0.0047) post-intervention, underscoring the efficacy of the treatment in the EG. The EG demonstrated a markedly greater improvement in BBS scores from pre-rehabilitation to 21days post-training, significantly outperforming the CG. Furthermore, at both day 14 and day 21, functional assessments including the FAC, 6MWT, and Barthel revealed improvements in both groups. However, the improvements in the EG were statistically significant compared to the CG at both time points: day 14 (FAC, P = 0.0377; 6MWT, P = 0.0494; Barthel, P = 0.0225) and day 21 (FAC, P = 0.0015; 6MWT, P = 0.0005; Barthel, P = 0.0004). These findings highlight the superiority of the intervention in the EG in enhancing functional outcomes. Regarding safety, the analysis revealed a solitary adverse event (AEs) related to the LiteStepper®ULLE device during the study period, affirming the combination therapy's safety profile when administered alongside conventional balance training in post-stroke hemiplegic patients. This underscores the feasibility and potential of incorporating LiteStepper®ULLE into rehabilitation protocols for this patient population. The LiteStepper® ULLE combined with conventional treatment is effective and safe for balance function training in patients with post-stroke hemiplegia.

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.

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.

Design and Validation of an Ambulatory User Support Gait Rehabilitation Robot: NIMBLE

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.