Lower Limb Exoskeleton Research Articles

Trajectory Generation and Control of a Lower Limb Exoskeleton for Gait Assistance

Trajectory Generation and Control of a Lower Limb Exoskeleton for Gait Assistance

A GMM-DTW-Based Locomotion Mode Recognition Method in Lower Limb Exoskeleton

Exoskeleton robots can provide support and protection to the human body as well as be able to perform a series of tasks under control. Locomotion mode recognition is an essential prerequisite for assisted control of lower limb exoskeleton robots. A system is designed to be applied to a variety of individuals with quick and accurate identification. In this article, we propose a novel real-time locomotion mode recognition algorithm based on Gaussian mixture model (GMM) features aligned by dynamic time warping (DTW). The spatial distribution of the sensor data is represented by the GMM features, which emphasize the coupling between joints. This peculiarity promotes the distinction of similarity judgment and shows superior generalization performance. Furthermore, we expand the study of the warping path. We further validate the reasonableness of the DTW algorithm for feature matching to improve the recognition rate. The data of several inertial measurement units (IMUs) and pressure sensors were collected by 17 subjects in three terrains, including level ground, stairs, and ramp. We define the five locomotion modes as walking on level ground, stair ascent (SA), stair descent, ramp ascent, and ramp descent. Compared with the algorithms in previous papers, the GMM-DTW algorithm can achieve substantial recognition rates and good generalization performance with low latency. This work establishes the groundwork for the soft control of exoskeleton robots in the future.

Gait phases recognition based on lower limb sEMG signals using LDA-PSO-LSTM algorithm

Gait phases are widely used in exoskeleton movement control. Surface electromyography (sEMG) is predictive and plays an important role in gait phase recognition. The purpose of this study is to improve the stability and accuracy of gait recognition methods based on the sEMG signals of lower limbs. First, we presented a LDA-PSO-LSTM algorithm based on feature combination selection and verified its recognition accuracy through experiments. LDA-PSO-LSTM had an average recognition rate of94.89% and a maximum accuracy of 97.02%. Second, we tested and compared the recognition accuracy of LDA-LSTM (92.17%). Experiments showed that the PSO optimization model had good recognition performance. Finally, we compared LDA-LSTM with all classifier combinations and concluded that the LDA-LSTM method has the highest recognition rate among a series of method combinations. The results indicated that LDA-PSO-LSTM as a classification model has apparent advantages in gait recognition. LDA-PSO-LSTM provides more accurate gait phase results for lower limb exoskeleton control. This method is beneficial to the development of the exoskeleton gait recognition system.

Biomechanical Effects of Adding an Ankle Soft Actuation in a Unilateral Exoskeleton.

Stroke disease leads to a partial or complete disability affecting muscle strength and functional mobility. Early rehabilitation sessions might induce neuroplasticity and restore the affected function or structure of the patients. Robotic rehabilitation minimizes the burden on therapists by providing repetitive and regularly monitored therapies. Commercial exoskeletons have been found to assist hip and knee motion. For instance, unilateral exoskeletons have the potential to become an effective training system for patients with hemiparesis. However, these robotic devices leave the ankle joint unassisted, essential in gait for body propulsion and weight-bearing. This article evaluates the effects of the robotic ankle orthosis T-FLEX during cooperative assistance with the AGoRA unilateral lower-limb exoskeleton (hip and knee actuation). This study involves nine subjects, measuring muscle activity and gait parameters such as stance and swing times. The results showed a reduction in muscle activity in the Biceps Femoris of 50%, Lateral Gastrocnemius of 59% and Tibialis Anterior of 35% when adding T-FLEX to the AGoRA unilateral lower-limb exoskeleton. No differences were found in gait parameters. Nevertheless, stability is preserved when comparing the two legs. Future works should focus on evaluating the devices in ground tests in healthy subjects and pathological patients.

Design and Analysis of a Lower Limb Loadbearing Exoskeleton

In recent years, the lower limb exoskeleton has been more and more widely used in military, medical and other fields. In this paper, the muscle–bone model of the lower limb during the human walking process is analyzed, and a lower limb exoskeleton with the purpose of loadbearing is designed. The exoskeleton is driven by four hydraulic cylinders to the hip and knee joints whose design load is 50 kg. The kinematic and dynamic model of the exoskeleton designed in this paper is established and analyzed, and it is simulated. Finally, the experiments were carried out on the exoskeleton test platform to verify that the stability, bearing capacity, tracking effect and durability of the exoskeleton can meet the requirements.

Motor control of the lower limbs while walking with the TWIN exoskeleton operated by TWINActa in healthy subjects

Lower limb exoskeletons are medical devices used in the rehabilitation to provide physical therapy to individuals with neurological disorders [1]. These robotic devices were originally designed and developed for spinal cord injury subjects, who do not have any residual walking abilities, and are therefore mainly based on an assistive approach. To successfully use these machines in subjects with residual capacities such as persons post stroke, the control system must be adapted to an assist-as- needed control scheme. This approach requires the subject active participation to stimulate the voluntary muscle contraction of the paretic limb and manage the somatosensory feedbacks subsequent to the action. For these reasons, we have developed TWINActa, a control suite for gait training following stroke, that provide asymmetric assistance to the user. The assistance-as-needed can be provided in the following modes: (i) support during the toe-off phase of the non-paretic leg; (ii) pelvic tilt damping during stance phase; (iii) stabilization of the knee joint of the supporting leg during the double support phases; and (iv) hip extension support of paretic leg during the stance phase. This study was aimed at verifying if motor control during a walking task performed with an exoskeleton, operated by TwinActa, is similar to the physiological control of walking in healthy subjects. Five healthy volunteers were asked to perform motor tasks (i.e., overground walking, stairs ascending and stair descending) wearing the TWIN exoskeleton [2] operated by TWINActa control system. During the task EMG signals were acquired from 12 muscles (10 placed on the lower limb and 2 on the upper limbs per side) and movement data were recorded from 2 IMUs placed on the shanks. Non-negative matrix factorization was carried out on the EMG recorded signal to identify muscle synergies [3]. The EMG activity patterns were also mapped onto the rostro-caudal location of ipsilateral motoneurons pools in the human spinal cord [4]. Based on the Kendall myotomal charts of segmental localization [5], we reconstructed the sacral (S1-S2) and lumbard (L2-L5) motor pool activation profiles. Pearson's correlation coefficient was used to evaluate the similarity degree of 1) muscle synergies and 2) sacral and lumbar motor pool activation profiles of walking with exoskeleton with respect to the normative reference (without device) provided by Eurobench project (Pepato scenario). Four muscle synergies accounted for a R2 > 0.85 for all participants during overground walking. A strong degree of similarity (correlation) of activation of sacral and lumbar motor pools with respect to the reference group was found (mean ± SD, 0.77 ± 0.06 and 0.78 ± 0.11, for sacral and lumbar spinal output respectively). Also the mean similarly of the muscle synergies activation profiles was strong (mean ± SD, 0.70 ± 0.14) with respect to the four physiological muscle synergies involved in gait control [6]. The four muscle synergies involved in the gait control [6] were found while walking with the exoskeleton operated by TWINActa in neurologically intact subjects. We found also a strong degree of similarity between the motor pools wearing the exoskeleton with respect to the normative reference of walking without the device. Since the spinal motor pools are associated with functional grouping of motoneurons of the lower limb muscles, our findings suggest that the Twin exoskeleton operated by TWINActa can be used as rehabilitation device to enhance the recovery of the physiological motor control in persons with residual ability. Further studies need to be conducted to verify this finding in subjects with neurological diseases.

Evaluation of Muscle Synergy During Exoskeleton-Assisted Walking in Persons With Multiple Sclerosis.

Gait deficit after multiple sclerosis (MS) can be characterized by altered muscle activation patterns. There is preliminary evidence of improved walking with a lower limb exoskeleton in persons with MS. However, the effects of exoskeleton-assisted walking on neuromuscular modifications are relatively unclear. The objective of this study was to investigate the muscle synergies, their activation patterns and the differences in neural strategies during walking with (EXO) and without (No-EXO) an exoskeleton. Ten subjects with MS performed walking during EXO and No-EXO conditions. Electromyography signals from seven leg muscles were recorded. Muscle synergies and the activation profiles were extracted using non-negative matrix factorization. The stance phase duration was significantly shorter during EXO compared to the No-EXO condition (p<0.05). Moreover, typically 3-5 modules were extracted in each condition. The module-1 (comprising Vastus Medialis and Rectus Femoris muscles), module-2 (comprising Soleus and Medial Gastrocnemius muscles), module-3 (Tibialis Anterior muscle) and module-4 (comprising Biceps Femoris and Semitendinosus muscles) were comparable between conditions. During EXO condition, Semitendinosus and Vastus Medialis emerged in module-5 in 7/10 subjects. Compared to No-EXO, average activation amplitude was significantly reduced corresponding to module-2 during the stance phase and module-3 during the swing phase during EXO. Exoskeleton-assistance does not alter the existing synergy modules, but could induce a new module to emerge, and alters the control of these modules, i.e., modifies the neural commands indicated by the reduced amplitude of the activation profiles. The work provides insights on the potential underlying mechanism of improving gait functions after exoskeleton-assisted locomotor training.

Sample-Efficient Policy Adaptation for Exoskeletons Under Variations in the Users and the Environment

Controlling lower-limb exoskeletons is extremely challenging due to their direct physical interaction with users wearing them which imposes additional safety concerns. Furthermore, the control policy needs to adapt for different users and surfaces the robot is traversing. Hence, it is crucial to design a control framework that can perform robustly in the presence of these variations. In this letter, we propose a sample-efficient method based on Bayesian Optimization (BO) to adapt a model-based walking controller for a lower-limb exoskeleton, XoMotion. In order to mitigate safety risks, we use a set of dummy weights with realistic inertial distributions in the experiments with the robot to find optimal policies. An extensive set of experimental results shows that the proposed controller can successfully adapt for different users and different terrains, in less than 15 real-world trials.

VP.26 Potential of the MyoSuit, a lightweight wearable lower-limb cable-actuated exoskeleton in patients with neuromuscular disorders: preliminary findings

Neuromuscular disorders (NMD) are characterized by progressive muscle weakness leading to dramatic impairments in functional capacities and quality of life. A new class of promising lightweight lower-limb wearable powered exoskeletons is currently emerging. However, data regarding the potential of such devices in patients with NMD are scarce. Within this work, we investigated the potential of the MyoSuit (MyoSwiss AG, Zurich, Switzerland), a lightweight wearable robotic device providing assistance for hip and knee extension against gravity using actuated cables. Four patients with NMD (2 with muscular dystrophies, 2 with inclusion body myositis) were studied. Without using the MyoSuit, 2-min walking distance (2MWD) was 128±30 m (66±17% of predicted values), number of repetitions during a 30-s sit-to-stand test (30s-STS) was 8±3, and MRC scores for knee extension and hip flexion were 3±1.1 and 3.9±0.6, respectively. Changes (n patients improved/impaired) in performance while using the MyoSuit during the following tests were as follows: 2MWD (1/3), 30-STS (1/3), TUG (0/4), 4-stairs climbing test (1/3). Regarding perceptions during the 2MWD as assessed using visual analogue scales, changes were: stability (0/4), exertion (2/2), and dyspnea (3/1). One fall occurred in one patient while wearing the device without consequences. Usability of the Myosuit as assessed using the system usability scale (/100) was 77±20, corresponding to good to excellent usability. Discomfort as assessed using the modified Nordic questionnaire (/10) was 1.0±0.5, corresponding to a low level of discomfort. The limited sample size limits the use of insightful statistics so far. Within this small group of patients, the use of the MyoSuit was associated with variable changes in performance and perceptions depending on the functional test. Further research in a larger sample is warranted to further investigate the potential of this device in patients with NMD.

Subject-Independent Continuous Locomotion Mode Classification for Robotic Hip Exoskeleton Applications.

Autonomous lower-limb exoskeletons must modulate assistance based on locomotion mode (e.g., ramp or stair ascent) to adapt to the corresponding changes in human biological joint dynamics. However, current mode classification strategies for exoskeletons often require user-specific tuning, have a slow update rate, and rely on additional sensors outside of the exoskeleton sensor suite. In this study, we introduce a deep convolutional neural network-based locomotion mode classifier for hip exoskeleton applications using an open-source gait biomechanics dataset with various wearable sensors. Our approach removed the limitations of previous systems as it is 1) subject-independent (i.e., no user-specific data), 2) capable of continuously classifying for smooth and seamless mode transitions, and 3) only utilizes minimal wearable sensors native to a conventional hip exoskeleton. We optimized our model, based on several important factors contributing to overall performance, such as transition label timing, model architecture, and sensor placement, which provides a holistic understanding of mode classifier design. Our optimized DL model showed a 3.13% classification error (steady-state: 0.80 ± 0.38% and transitional: 6.49 ± 1.42%), outperforming other machine learning-based benchmarks commonly practiced in the field (p<0.05). Furthermore, our multi-modal analysis indicated that our model can maintain high performance in different settings such as unseen slopes on stairs or ramps. Thus, our study presents a novel locomotion mode framework, capable of advancing robotic exoskeleton applications toward assisting community ambulation.

Enhanced algorithm for energy optimization and improvised synchronization in knee exoskeleton system

The purpose of the study is to develop an augmented algorithm with optimised energy and improvised synchronisation to assist the knee exoskeleton design. This enhanced algorithm is used to estimate the accurate left and right movement signals from the brain and accordingly moves the lower-limb exoskeleton with the help of motors. An optimised deep learning algorithm is developed to differentiate the right and left leg movements from the acquired brain signals. The obtained test signals are then compared with the signals obtained from the conventional algorithm to find the accuracy of the algorithm. The obtained average accuracy rate of about 63% illustrates the improvised differentiation in identifying the right and left leg movement. The future work involves the comparative study of the proposed algorithm with other classification technologies to extract more reliable results. A comparative analysis of the replaceable and rechargeable battery will be done in the future study to exhibit the effectiveness of the proposed model. This study involves the extended study of five frequency regions namely alpha, beta, gamma, delta and theta, to handle the real-time EEG signal processing exoskeleton, model.

Fuzzy radial-based impedance controller design for lower limb exoskeleton robot

AbstractThe lower extremity rehabilitation exoskeleton is mainly used to help patients with movement disorders complete rehabilitation training. For the human-machine interaction problem of the lower limb rehabilitation exoskeleton, a fuzzy radial-based impedance (RBF-FVI) controller is proposed in this study. A six degree of freedom (DOF) lower extremity rehabilitation exoskeleton was developed, and the human-machine coupling dynamics model was established. To realize the compliance control of the human-machine coupling system, a novel RBF-FVI controller is designed, which includes an inner-loop fuzzy position control module and an outer-loop impedance control module. The inner-loop fuzzy position control module is mainly used to achieve the tracking control of the desired training trajectory and position adjustment amount. The outer-loop impedance control module regulates the impedance parameters and compensates for the uncertainty terms. The superiority of the proposed controller in trajectory following is verified through simulation and comparison tests. The hardware test of the human-machine coupling system was carried out, and the test results showed that the subject and the exoskeleton system could realize a coordinated and smooth movement.

A composite position control of flexible lower limb exoskeleton based on second-order sliding mode

Aiming at the problem that the trajectory tracking error of flexible lower limb exoskeleton robot is too large under the condition of external disturbance and parameters uncertainty, a composite position control method based on second-order sliding mode control was proposed. Firstly, the flexible lower limb exoskeleton robot is modeled by Lagrange function. Secondly, considering that the system is affected not only by matched disturbance but also by unmatched disturbance, two finite time state observers are used to observe and compensate the two disturbances in real time. The super-twisting method is employed in the position control section to ensure that the trajectory tracking error of the knee joint converges to zero in finite time. Finally, the Lyapunov function proves the stability of the suggested control technique. The experimental findings reveal that the suggested control approach has a better trajectory tracking effect and resilience than classic PD control and sliding mode control, indicating its superiority.

Training-Induced Muscle Fatigue with a Powered Lower-Limb Exoskeleton: A Preliminary Study on Healthy Subjects.

Powered lower-limb exoskeletons represent a promising technology for helping the upright stance and gait of people with lower-body paralysis or severe paresis from spinal cord injury. The powered lower-limb exoskeleton assistance can reduce the development of lower-limb muscular fatigue as a risk factor for spasticity. Therefore, measuring powered lower-limb exoskeleton training-induced fatigue is relevant to guiding and improving such technology’s development. In this preliminary study, thirty healthy subjects (age 23.2 ± 2.7 years) performed three motor tasks: (i) walking overground (WO), (ii) treadmill walking (WT), (iii) standing and sitting (STS) in three separate exoskeleton-based training sessions of 60 min each. The changes in the production of lower-limb maximal voluntary isometric contraction (MVIC) were assessed for knee and ankle dorsiflexion and extension before and after the three exoskeleton-based trained motor tasks. The MVIC forces decreased significantly after the three trained motor tasks except for the ankle dorsiflexion. However, no significant interaction was found between time (before-, and after-training) and the training sessions except for the knee flexion, where significant fatigue was induced by WO and WT trained motor tasks. The results of this study pose the basis to generate data useful for a better approach to the exoskeleton-based training. The STS task leads to a lower level of muscular fatigue, especially for the knee flexor muscles.

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model.

The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human–robot interaction, but the internal relationship of EEG and sEMG signals is not clear, so their fusion still has some shortcomings. A precise fusion method of EEG and sEMG using the CNN-LSTM model was investigated to detect lower limb voluntary movement in this study. At first, the EEG and sEMG signal processing of each stage was analyzed so that the response time difference between EEG and sEMG can be estimated to detect lower limb voluntary movement, and it can be calculated by the symbolic transfer entropy. Second, the data fusion and feature of EEG and sEMG were both used for obtaining a data matrix of the model, and a hybrid CNN-LSTM model was established for the EEG and sEMG-based decoding model of lower limb voluntary movement so that the estimated value of time difference was about 24 ∼ 26 ms, and the calculated value was between 25 and 45 ms. Finally, the offline experimental results showed that the accuracy of data fusion was significantly higher than feature fusion-based accuracy in 5-fold cross-validation, and the average accuracy of EEG and sEMG data fusion was more than 95%; the improved average accuracy for eliminating the response time difference between EEG and sEMG was about 0.7 ± 0.26% in data fusion. In the meantime, the online average accuracy of data fusion-based CNN-LSTM was more than 87% in all subjects. These results demonstrated that the time difference had an influence on the EEG and sEMG fusion to detect lower limb voluntary movement, and the proposed CNN-LSTM model can achieve high performance. This work provides a stable and reliable basis for human–robot interaction of the lower limb exoskeleton.

A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons.

Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.

Immediate effects of the honda walking assist on spatiotemporal gait characteristics in individuals after stroke

The use of unconstrained lower limb exoskeletons has become a promising approach to assist individuals with gait impairments. The Honda Walking Assist (HWA) is a hip-assistive exoskeleton functioning as a gait trainer and has been shown to improve several gait related outcomes after training. Studies investigating its immediate effects on spatiotemporal gait parameters other than walking speed in stroke survivors are lacking. The aim of this study was to investigate immediate differences in spatiotemporal gait parameters of stroke survivors between normal overground walking, walking with an unpowered, non-assisting HWA and walking with an optimally assisting HWA. Five ischemic stroke survivors (mean time since stroke 115 ​± ​213.6 days) walked 3 times 5 ​m in each condition. Differences in 14 spatiotemporal gait parameters between all 3 conditions were registered and reported in a descriptive manner. With optimal assistance, 4 patients walked faster (0.057–0.095 ​m/s) with longer strides of the paretic (0.055–0.069 ​m) and non-paretic (0.053–0.077 ​m) leg compared to normal walking. Compared to unpowered walking, all patients walked faster (0.020–0.063 ​m/s) in the optimal assist condition, with longer strides of the paretic (0.036–0.072 ​m) and non-paretic leg (0.045–0.082 ​m). During unpowered walking, gait velocity remained unchanged in 2 patients, increased (0.012–0.051 ​m/s) in 2 patients and decreased (−0.022 ​m/s) in 1 patient compared to normal walking. Changes in paretic stride lengths ranged from −0.066 to 0.029 ​m. The optimal individualized motor assistance provided by the HWA induces small, positive changes in gait parameters. This indicates that this light-weight hip-assistive exoskeleton can be of value in rehabilitation setting, where multiple training sessions with the device are possible.

Review of current developments in lower extremity exoskeleton systems

A lower extremity exoskeleton is device which can enhance the human strength and speed while walking and running. It mimics the human gait motion and enhance the endurance of human with help of actuators to carry heavy loads. It can help to carry heavy loads in rough terrain where a wheeled vehicle can’t reach. The most prominent option for elderly people and people with walking disabilities such as paraplegia is to use a wheelchair, which is not convenient in every environment. A lower extremity exoskeleton with the particular purpose of either power augmentation or gait assistance can solve this problem. This paper discusses an overview on the historical developments in the field of exoskeleton type system along with the biomechanical considerations and the human gait cycle for the development of a lower extremity exoskeleton and analyses various parameters needed for the design of a lower limb exoskeleton system. The kinematic analysis of human lower limb is studied using Denavit-Hartenberg (DH) parameters. Furthermore, a comparison of the degrees of freedom and their ranges along with the actuators used in various exoskeleton developed is discussed.

Gait Phase Detection in Walking and Stairs Using Machine Learning.

Machine learning-based activity and gait phase recognition algorithms are used in powered motion assistive devices to inform control of motorized components. The objective of this study was to develop a supervised multiclass classifier to simultaneously detect activity and gait phase (stance, swing) in real-world walking, stair ascent, and stair descent using inertial measurement data from the thigh and shank. The intended use of this algorithm was for control of a motion assistive device local to the knee. Using data from 80 participants, two decision trees and five long short-term memory (LSTM) models that each used different feature sets were initially tested and evaluated using a novel performance metric: proportion of perfectly classified strides (PPCS). Based on the PPCS of these initial models, five additional posthoc LSTM models were tested. Separate models were developed to classify (i) both activity and gait phase simultaneously (one model predicting six states), and (ii) activity-specific models (three individual binary classifiers predicting stance/swing phases). The superior activity-specific model had an accuracy of 98.0% and PPCS of 55.7%. The superior six-phase model used filtered inertial measurement data as its features and a median filter on its predictions and had an accuracy of 92.1% and PPCS of 22.9%. Pooling stance and swing phases from all activities and treating this model as a binary classifier, this model had an accuracy of 97.1%, which may be acceptable for real-world lower limb exoskeleton control if only stance and swing gait phases must be detected. Keywords: machine learning, deep learning, inertial measurement unit, activity recognition, gait.

Modelling and analysis of coupling dynamics of swinging a lower limb exoskeleton

Modelling and analysis of coupling dynamics of swinging a lower limb exoskeleton