Control Of Exoskeleton Research Articles

Robust Payload Recognition Based on Sensor-Over-Muscle-Independence Deep Learning for the Control of Exoskeletons

Force myography (FMG) can detect changes in the muscle volume which can be interpreted to recognize human intention. FMG data, however, is highly dependent on the placement of sensors over muscles, and interpretation becomes challenging if the sensors get displaced. This paper presents a robust sensor over muscle independence (SOMI) preprocessing algorithm combined with lightweight deep neural network (DNN) which shows high classification accuracy of the FMG data. SOMI organizes the irregular sensory data into regular patterns. Proposed algorithm makes the DNN insensitive to not only position and rotation shift but also to the flip of the sensors arrangement. A custom designed FMG band is used for payload recognition to experimentally validate the proposed method with five payload statuses and eight subjects. A five-fold cross validation comparative study demonstrated that the proposed method is 19.8% and 7.1% more accurate than support vector machine (SVM) and DNN without SOMI, respectively, and showed superior performance against k-nearest neighbors (KNN) and decision tree (DT). SOMI empowered a lightweight DNN to maintain the accuracy over 98% for different arbitrary wearing schemes of the FMG band over both left and right upper arms.

Gaze Behavior and Mental Workload While Using a Whole-Body Powered Exoskeleton: A Pilot Study

The mental demands associated with operating complex whole-body powered exoskeletons are poorly understood. This study aimed to explore the overall workload associated with using a powered wholebody exoskeleton among expert and novice users, as well as the changes in workload resulting from novices adapting to exoskeleton-use over time. We used eye-tracking measures to quantify the differences in workload of six novices and five experts while they performed a levelwalking task, with and without wearing a whole-body powered exoskeleton. We found that only novices’ pupil dilation (PD) increased, while experts showed a greater proportion of downward-directed pathfixations (PF) compared to novices while wearing the exoskeleton. These results indicate that novices’ mental demands were higher, and that experts and novices exhibited distinct visuomotor strategies. Eyetracking measures may potentially be used to detect differences in workload and skill-level associated with using exoskeletons, and also considered as inputs for future adaptive exoskeleton control algorithms.

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification.

Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface. To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model. Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed. The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

Design and Gait Control of an Active Lower Limb Exoskeleton for Walking Assistance

In the development of assistive lower-limb exoskeletons, both exoskeleton design, and gait control are critical for their successful applications. This paper introduces an assistive lower-limb exoskeleton (ALEXO) for active walking assistance. The development of the ALEXO including mechanical design, sensors and gait control is described. The exoskeleton adopts a hierarchical control. A 2-link model is built for dynamic analysis and lower-level control purposes. A trajectory tracking control method based on the computed torque control is proposed, in which physical interaction between the exoskeleton and the user is included. Simulations were conducted for different levels of interaction forces to verify the feasibility of the gait control. Moreover, walking trials of a healthy subject were performed, with muscle activities measured through EMG systems. Both simulation and system test results demonstrated the effectiveness of the developed exoskeleton with the proposed control method for walking assistance.

Recognition of walking directional intention employed ground reaction forces and center of pressure during gait initiation

BackgroundMovement intentions are generally classified by Electroencephalogram (EEG) and have been used in gait initiation prediction. However, it is not easy to collect EEG data and practical in reality. Alternatively, ground reaction force (GRF) and the center of pressure (COP) is produced by the contact between the foot and the ground during a specific period of walking, which are the characteristics of evaluating gait performance Research questionThe study aims to use a deep learning technique to recognize the data of the COP and GRF to classify straight walking and right turn. Second, the study aims to reveal gait characteristics that could replace EEG to predict walking directional intentions MethodsTen healthy male adults were instructed to stand on the force platform and self-selected to perform three conditions: standstill, straight walking, and right turn. The onset of gait initiation was evaluated by muscle activation of the right tibialis anterior, and EEG and the COP displacement evaluated the onset of gait intention. Subsequently, GRF and COP would be treated as features to classify the gait intention in the Long Short-Term Memory (LSTM) model. ResultsThe results revealed that the onset of EEG and the COP displacement initiation were statistically significant differences between straight walking and right turn. For the classification, the average accuracy of the LSTM model with GRF and COP as features reached the highest one, 94.79 %, depending on the heel- or toe-off of the swing leg. The results indicated that gait intentions could be classified based on the GRF and COP. SignificanceThe machine learning technique of LSTM with gait parameters can recognize the gait intention of changing walking orientation. Our model and approach would be expected to provide advanced predictions, such as exoskeleton control or pedestrian traffic flow.

AI-enabled Hybrid Model for Lower-Limb Movements Recognition using Estimated Muscular Activity from Cortical Brain Signals

Decades of research determined electromyography (EMG) and electroencephalography (EEG), individually, as paramount controls aimed at rehabilitation. However, correlating commands from the central nervous system to lower-limb movements are highly complex raising challenges for anthropomorphic lower-limb prosthesis control. This work establishes a hierarchical relationship between 12-channel EEG and 4-channel surface EMG using a hybrid model (LRG-2L-LSTM) for lower-limb ankle movement recognition by estimating muscular activity from cortical brain signals. The proposed model achieved R 2 = 0.742 ± 0.03, RMSE = 0.067 ± 0.002 for EMG estimation and averaged recognition accuracy of 84.86 ± 0.27% for ankle movements using estimated EMG, thereby, establishing lower-limb prosthesis and exoskeleton control for amputees with little to no muscular strength.

Neural Encoding of Pavement Textures during Exoskeleton Control: A Pilot Study

This paper investigates the changes in sensory neural activity during exoskeleton control. Exoskeletons are becoming reliable tools for neurorehabilitation, as recent studies have shown that their use enhances neural plasticity. However, the specific neural correlates associated with exoskeleton control have not yet been described in detail. Therefore, in this pilot study, our aim was to investigate the effects of different pavement textures on the neural signals of participants (n = 5) while controlling a lower limb ExoAtlet®-powered exoskeleton. Subjects were instructed to walk on various types of pavements, including a flat surface, carpet, foam, and rubber circles, both with and without the exoskeleton. This setup resulted in eight different experimental conditions for classification (i.e., Exoskeleton/No Exoskeleton in one of four different pavements). Four-minute Electroencephalography (EEG) signals were recorded in each condition: (i) the power of the signals was compared for electrodes C3 and C4 across different conditions (Exoskeleton/No Exoskeleton on different pavements), and (ii) the signals were classified using four models: the linear support vector machine (L-SVM), the K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), and the artificial neural network (ANN). the results of power analysis showed increases and decreases in power within the delta frequency bands in electrodes C3 and C4 across the various conditions. The results of comparison between classifiers revealed that LDA exhibited the highest performance with an accuracy of 85.71%. These findings support the notion that the sensory processing of pavement textures during exoskeleton control is associated with changes in the delta band of the C3 and C4 electrodes. From the results, it is concluded that the use of classifiers, such as LDA, allow for a better offline classification of different textures in EEG signals, with and without exoskeleton control, than the analysis of power in different frequency bands.

Machine Learning for Human Motion Intention Detection.

The gait pattern of exoskeleton control conflicting with the human operator's (the pilot) intention may cause awkward maneuvering or even injury. Therefore, it has been the focus of many studies to help decide the proper gait operation. However, the timing for the recognization plays a crucial role in the operation. The delayed detection of the pilot's intent can be equally undesirable to the exoskeleton operation. Instead of recognizing the motion, this study examines the possibility of identifying the transition between gaits to achieve in-time detection. This study used the data from IMU sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully separate the transition period from the motion periods. 2. The detection of gait change from walking to sitting can be as fast as 0.17 s, which is adequate for future control applications. However, detecting the transition from standing to walking can take as long as 1.2 s. 3. This study also find that the network trained for one person can also detect movement changes for different persons without deteriorating the performance.

Tracking Control for a Lower Extremity Exoskeleton Based on Adaptive Dynamic Programing.

The utilization of lower extremity exoskeletons has witnessed a growing presence across diverse domains such as the military, medical treatment, and rehabilitation. This paper introduces a novel design of a lower extremity exoskeleton specifically tailored for individuals engaged in heavy object carrying tasks. The exoskeleton incorporates an impressive 12 degrees of freedom (DOF), with four of them being effectively controlled through hydraulic cylinders. To achieve optimal control of this intricate lower extremity exoskeleton system, the authors propose an adaptive dynamic programming (ADP) algorithm. Several crucial components are established to implement this control scheme. These include the formulation of the state equation for the lower extremity exoskeleton system, which is well-suited for the ADP algorithm. Additionally, a corresponding performance index function based on the tracking error is devised, along with the game algebraic Riccati equation. By employing the value iteration ADP scheme, the lower extremity exoskeleton demonstrates highly effective tracking control. This research not only highlights the potential of the proposed control approach but also showcases its ability to enhance the overall performance and functionality of lower extremity exoskeletons, particularly in scenarios involving heavy object carrying. Overall, this study contributes to the advancement of lower extremity exoskeleton technology and offers valuable insights into the application of ADP algorithms for achieving precise and efficient control in demanding tasks.

Rehabilitation exoskeleton torque control based on PSO-RBFNN optimization.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.

Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study.

Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user's performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS). Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957). Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70-0.80; MAE = 0.48-0.58Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00-0.19; MAE = 1.15-1.29Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter. Discussion: Our preliminary results indicate that the stride-by-stride estimations of shank-worn IMUs show potential to design automatic and adaptive exoskeleton control strategies for people with moderate impairments in gait function due to stroke.

Closed-Loop FES Control of a Hybrid Exoskeleton during Sit-to-Stand Exercises: Concept and First Evaluation

Rehabilitation of paralysis caused by a stroke or a spinal cord injury remains a complex and time-consuming task. This work proposes a hybrid exoskeleton approach combining a traditional exoskeleton and functional electrical stimulation (FES) as a promising method in rehabilitation. However, hybrid exoskeletons with a closed-loop FES control strategy are functionally challenging to achieve and have not been reported often. Therefore, this study aimed to investigate a powered lower-limb exoskeleton with a closed-loop FES control for Sit-to-Stand (STS) movements. A body motion capture system was applied to record precise hip and knee trajectories of references for establishing the human model. A closed-loop control strategy with allocation factors is proposed featuring a two-layer cascaded proportional–integral–derivative (PID) controller for both FES and exoskeleton control. Experiments were performed on two participants to examine the feasibility of the hybrid exoskeleton and the closed-loop FES control. Both open- and closed-loop FES control showed the desired performance with a relatively low root-mean-squared error (max 1.3∘ in open-loop and max 4.1∘ in closed-loop) in hip and knee trajectories. Notably, the closed-loop FES control strategy can achieve the same performance with nearly 60% of the electrical power input compared to the open-loop control, which reduced muscle fatigue and improved robustness during the training. This study provides novel insights into body motion capture application and proposes a closed-loop FES control for hybrid exoskeletons.

Output Constrained Control of Lower Limb Exoskeleton Based on Knee Motion Probabilistic Model With Finite-Time Extended State Observer

Due to randomness and uncertainty of individual walking gait, this study uses the sparse Gaussian process (SGP) to construct a knee motion probabilistic model related to its corresponding hip motion. Since the SGP can quantify output uncertainty, a time-varying constrained boundary of knee position is presented to guarantee the operator's safety and comfort in human-exoskeleton cooperative motion. First, a low-cost gait acquisition system (called CJD-1) composed of inertial measurement unit is constructed to collect the operator's gait information. Then, a virtual interaction torque field and an output-dependent universal barrier function are designed to realize the coordinated transformation of the time-varying constrained boundary generated by the SGP. Hence, the exoskeleton joint position is constrained by the desired output constrained boundary, which is complied with the operator's gait law. Then, a backstepping controller with a finite-time extended state observer is proposed to estimate and compensate unmeasured joint velocity and lumped uncertainty. The effectiveness of the proposed control scheme is verified by both simulations and experimental results of human-exoskeleton cooperative motion.

Walking Speed Learning and Generalization Using Seq2Seq Gated and Adaptive Continuous-Time Recurrent Neural Network (S2S-GACTRNN) for a Hip Exoskeleton

The objective of this article is to provide a new approach for walking speed learning and generalization in the speed-adaptation control of exoskeletons. By combining the gated and adaptive continuous-time recurrent neural network (GACTRNN), which has the potential to process periodic signals, and the “sequence to sequence” structure, the S2S-GACTRNN model is proposed for walking gait generation at different speeds. The “proactive loop” and “reactive loop” were presented for learning and generalization capability evaluation, respectively, in the experiments of sinusoidal signals and walking gait signals. First, simulation experiments were carried out to evaluate the S2S-GACTRNN's learning and generalization capabilities of sinusoidal signals with different frequencies. Second, in offline experiments, the model was applied to deal with walking gait signals to evaluate the capabilities of learning and generalization of walking speeds. Third, a client-server system was constructed and an online prediction method was proposed for online experiments to further evaluate the trained model's performance in walking speed generalization. The mean absolute errors of S2S-GACTRNN trained using walking data at three speeds were reduced by 24%, 38%, and 24% compared with that trained using walking data at one speed. The results show that the model has learning and generalization potential for gait signals of different walking speeds and may be utilized as a new approach to the adaptive control of walking speeds in the field of exoskeletons.

Stable Walking Feedback Control for a Self-Balanced Lower Limb Exoskeleton for Paraplegics

Self-Balanced lower limb exoskeleton (SBLLE) is designed for rehabilitation and walking assistance for dyskinesia persons with spinal or lower limb muscle injury to regain the locomotion ability in daily activities. In this paper, an innovative feedback control strategy for a fully actuated SBLLE is presented, which helps users to walk stably without crutches or other assistive devices. Exoskeleton is approximated as a simplified center of mass (CoM) model. Based on this simplified dynamic model, the trajectory generation and walking feedback control strategy, which enhances the locomotion stability, is introduced. The proposed strategy is implemented on our exoskeleton, AutoLEE-II, and its stability and dynamic performance are demonstrated in the stable walking simulations and experiments for exoskeleton with a female subject.

A Novel BILSTM-CNN Method for Pattern Recognition in Real Time Under Triple Physical Loads in Lower Extremity Exoskeleton

Pattern recognition is of great importance in compliant control of lower extremity exoskeleton. We propose a novel method based on the BILSTM-CNN model for pattern recognition in real time under triple physical loads on different terrains. BILSTM is skilled in dealing with temporal series data while CNN is proficient in coping with spatial series data. Five patterns include level ground walking (LW), stair ascending (SA), stair descending (SD), ramp ascending (RA), and ramp descending (RD). Their accuracies in multigrade loads of 0kg, 20kg, and 40kg reach 98.81%, 98.24%, and 97.04% respectively. Moreover, compared with the universal methods of LSTM, CNN, and BP, the hybrid method has competitive advantage in evaluation indexes of accuracy, precision, recall, and F1 score. In addition to five steady patterns, eight pattern transitions are identified between two neighboring states, such as LW to SA, LW to SD, LW to RA, LW to RD, SA to LW, SD to LW, RA to LW, and RD to LW. For pattern transitions, prediction time (Pre-T) of next pattern in multilevel loads of 0kg, 20kg, and 40kg are 190-620 ms, 180-420 ms, and 50-90 ms respectively prior to the step into that pattern. Pattern transition time (Aug-In) in multilevel loads of 0kg, 20kg, and 40kg are 50-190 ms, 30-310 ms, and 0-280 ms respectively before Pre-T of next pattern. Eventually, the experimental results indicate the proposed method has excellent performance on pattern recognition and pattern transition.

The economic value of augmentative exoskeletons and their assistance

For augmentative exoskeletons that assist able-bodied users, a clear metric of success remains an open question. Here we leverage the Vickrey second-price auction to quantify the economic value added by lower-limb exoskeletons and their assistance. We posited that if exoskeletons provided helpful assistance during a difficult task, this value could be quantified through a lowering of participant auction bids to continue walking. The bidding results were compared across different conditions to determine the economic value of the exoskeleton, bearing in mind also the cost of wearing the added mass of the exoskeleton. Results show that the total value of the exoskeleton and assistance was modest. While most participants found the assistance itself valuable, this value was mostly offset by the extra mass added of wearing the exoskeleton. Our approach provides insight into how exoskeleton wearers may value different aspects of user experience. These results suggest economic value may be a powerful tool in the design and control of exoskeletons that maximize user benefit.

Elbow-sideWINDER (Elbow-side Wearable INDustrial Ergonomic Robot): design, control, and validation of a novel elbow exoskeleton.

Musculoskeletal Disorders associated with the elbow are one of the most common forms of work-related injuries. Exoskeletons have been proposed as an approach to reduce and ideally eliminate these injuries; however, exoskeletons introduce their own problems, especially discomfort due to joint misalignment. The Elbow-sideWINDER with its associated control strategy is a novel elbow exoskeleton to assist elbow flexion/extension during occupational tasks. This study describes the exoskeleton showing how this can minimize discomfort caused by joint misalignment, maximize assistive performance, and provide increased robustness and reliability in real worksites. The proposed medium-level control strategy can provide effective assistive torque using three control units as follows: an arm kinematics estimator, a load estimator, and a friction compensator. The combined hardware/software system of the Elbow-sideWINDER is tested in load-lifting tasks (2 and 7 kg). This experiment focuses on the reduction in the activation level of the biceps brachii and triceps brachii in both arms and the change in the range of motion of the elbow during the task. It is shown that using the Elbow-sideWINDER, the biceps brachii, responsible for the elbow flexion, was significantly less activated (up to 38.8% at 2 kg and 25.7% at 7 kg, on average for both arms). For the triceps brachii, the muscle activation was reduced by up to 37.0% at 2 kg and 35.1% at 7 kg, on average for both arms. When wearing the exoskeleton, the range of motion of the elbow was reduced by up to 13.0° during the task, but it was within a safe range and could be compensated for by other joints such as the waist or knees. There are extremely encouraging results that provide good indicators and important clues for future improvement of the Elbow-sideWINDER and its control strategy.

Design procedure and control of a small-scale knee exoskeleton using shape memory alloy springs

Design procedure and control of a small-scale knee exoskeleton using shape memory alloy springs

Design and Control of a Single-Leg Exoskeleton with Gravity Compensation for Children with Unilateral Cerebral Palsy.

Children with cerebral palsy (CP) experience reduced quality of life due to limited mobility and independence. Recent studies have shown that lower-limb exoskeletons (LLEs) have significant potential to improve the walking ability of children with CP. However, the number of prototyped LLEs for children with CP is very limited, while no single-leg exoskeleton (SLE) has been developed specifically for children with CP. This study aims to fill this gap by designing the first size-adjustable SLE for children with CP aged 8 to 12, covering Gross Motor Function Classification System (GMFCS) levels I to IV. The exoskeleton incorporates three active joints at the hip, knee, and ankle, actuated by brushless DC motors and harmonic drive gears. Individuals with CP have higher metabolic consumption than their typically developed (TD) peers, with gravity being a significant contributing factor. To address this, the study designed a model-based gravity-compensator impedance controller for the SLE. A dynamic model of user and exoskeleton interaction based on the Euler-Lagrange formulation and following Denavit-Hartenberg rules was derived and validated in Simscape™ and Simulink® with remarkable precision. Additionally, a novel systematic simplification method was developed to facilitate dynamic modelling. The simulation results demonstrate that the controlled SLE can improve the walking functionality of children with CP, enabling them to follow predefined target trajectories with high accuracy.