Lower Limb Exoskeleton Research Articles

Preliminary Validation of Proportional Myoelectric Control of A Commercially Available Robotic Ankle Exoskeleton.

Proportional myoelectric control of robotic lower limb exoskeletons can increase the variability and adaptability of biomechanical behaviors for assisting human movement compared to traditional state-based control. Previous exoskeletons using proportional myoelectric control have relied on pneumatic actuators and been limited to laboratory use. We applied proportional myoelectric control to a robotic ankle exoskeleton using a brushless DC motor (Dephy) and enabled it to work in community settings. Benchtop testing verified electromechanical responses similar to biological values (electromechanical delay of 22 ms and time to peak activation of 123 ms). Four healthy participants trained for thirty minutes each using bilateral ankle exoskeletons. From minute one of powered walking to minute 30 of powered walking, peak soleus EMG reduced by 17.9% as they learned to walk with exoskeleton assistance. Our future work will extend the powered walking period, measure metabolic cost, and measure gait variability between participants using proportional myoelectric control on fully portable, electromechanical ankle exoskeletons.

Exploring the Utility of Crutch Force Sensors to Predict User Intent in Assistive Lower Limb Exoskeletons.

The adoption of assistive lower limb exoskeletons in built environments is reliant on the further development of these devices to handle the varied conditions experienced in everyday life. The required development includes more varied and flexible gait patterns, but also appropriate user interfaces to enable fluid gait. This work explores the properties of an algorithm used to predict user intent based on sensors onboard a user-balanced robotic exoskeleton system. Specifically, classification algorithms built with different input data sets are compared - with varying detail of the interaction forces between the crutches and the ground, and the duration of the data sample used to make the prediction. Data were collected with one able-bodied participant using an exoskeleton, training three independent classifiers corresponding to different exoskeleton states. The results indicate the value of including information about the interaction forces between the crutches and the ground in improving prediction accuracy, with increasing prediction window also generally resulting in an increase in prediction accuracy. Whilst no categorical recommendation can be made with respect to either parameter, these results provide a baseline which can be used in conjunction deliberate consideration of the costs associated with implementation.

Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system, which conducts natural and close cooperation with the human by recognizing human locomotion timely. Requiring subject-specific training is the main challenge of the existing approaches, and most methods have the problem of insufficient recognition. This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition (LMR) method based on GA-CNN for a lower limb exoskeleton system. The LMR method is a combination of Convolutional Neural Networks (CNN) and Genetic Algorithm (GA)-based multi-sensor information selection. To improve network performance, the hyper-parameters are optimized by Bayesian optimization. An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method. Twelve locomotion modes, which composed an integral locomotion system for the daily application of the exoskeleton, can be recognized by the proposed method. According to a series of experiments, the recognizer shows strong comprehensive abilities including high accuracy, low delay, and sufficient adaption to different subjects.

Estimation of the interaction force between human and passive lower limb exoskeleton device during level ground walking

To develop sophisticated and efficient control strategies for exoskeleton devices, acquiring the information of interaction forces between the wearer and the wearable device is essential. However, obtaining the interaction force via conventional methods, such as direct measurement using force sensors, is problematic. This paper proposes a kinematic data-based estimation method to evaluate the interaction force between human lower limbs and passive exoskeleton links during level ground walking. Unlike conventional methods, the proposed method requires no force sensors and is computationally cheaper to obtain the calculation results. To obtain more accurate kinematic data, a marker refinement algorithm based on bilevel optimization framework is adopted. The interaction force is evaluated by a spring model, which is used to imitate the binding behavior between human limbs and the exoskeleton links. The deflection of the spring model is calculated based on the assumption that the phase delay between human limb and exoskeleton link can be presented by the sequence of frames of kinematic data. Experimental results of six subjects indicate that our proposed method can estimate the interaction forces during level ground walking. Moreover, a case study of bandage location optimization is conducted to demonstrate the usefulness of obtaining the interaction information.

Distinct Kinematic and Neuromuscular Activation Strategies During Quiet Stance and in Response to Postural Perturbations in Healthy Individuals Fitted With and Without a Lower-Limb Exoskeleton.

Many individuals with disabling conditions have difficulty with gait and balance control that may result in a fall. Exoskeletons are becoming an increasingly popular technology to aid in walking. Despite being a significant aid in increasing mobility, little attention has been paid to exoskeleton features to mitigate falls. To develop improved exoskeleton stability, quantitative information regarding how a user reacts to postural challenges while wearing the exoskeleton is needed. Assessing the unique responses of individuals to postural perturbations while wearing an exoskeleton provides critical information necessary to effectively accommodate a variety of individual response patterns. This report provides kinematic and neuromuscular data obtained from seven healthy, college-aged individuals during posterior support surface translations with and without wearing a lower limb exoskeleton. A 2-min, static baseline standing trial was also obtained. Outcome measures included a variety of 0 dimensional (OD) measures such as center of pressure (COP) RMS, peak amplitude, velocities, pathlength, and electromyographic (EMG) RMS, and peak amplitudes. These measures were obtained during epochs associated with the response to the perturbations: baseline, response, and recovery. T-tests were used to explore potential statistical differences between the exoskeleton and no exoskeleton conditions. Time series waveforms (1D) of the COP and EMG data were also analyzed. Statistical parametric mapping (SPM) was used to evaluate the 1D COP and EMG waveforms obtained during the epochs with and without wearing the exoskeleton. The results indicated that during quiet stance, COP velocity was increased while wearing the exoskeleton, but the magnitude of sway was unchanged. The OD COP measures revealed that wearing the exoskeleton significantly reduced the sway magnitude and velocity in response to the perturbations. There were no systematic effects of wearing the exoskeleton on EMG. SPM analysis revealed that there was a range of individual responses; both behaviorally (COP) and among neuromuscular activation patterns (EMG). Using both the OD and 1D measures provided a more comprehensive representation of how wearing the exoskeleton impacts the responses to posterior perturbations. This study supports a growing body of evidence that exoskeletons must be personalized to meet the specific capabilities and needs of each individual end-user.

The Effect of Lower Limb Exoskeleton Alignment on Knee Rehabilitation Efficacy.

This study focuses on a musculoskeletal analysis of human lower extremity and associated muscle forces during different rehabilitative tasks and exoskeleton alignment models. By changing the size and orientation of the impairment levels that could be caused by the misalignment of the exoskeleton and biological knee joint, muscle stress variations were observed. This indicates an increase in force such as that generated by the Vastus lateralis muscle up to 4.3% due to a 5 mm lateral offset from an anatomically healthy knee joint location. In another setting, while a subject moved the shank through a circular trajectory using an exoskeleton support, muscle strain due to misalignment was reflected at the rectus femoris with a variation of 44%, the biceps femoris large head with 32% and the gastrocnemius muscles with 31–33% variation. These results suggest that misalignment should be taken into account while using exoskeletons with certain trajectories for knee rehabilitation purposes. Based on the shortcomings of conventional physiotherapeutic tasks, the outcome of this study can be helpful in prescribing an impactful yet convenient configuration toward a safe and promising rehabilitation process. Assessment of exoskeleton alignment during rehabilitation is important to ensure user safety with a better therapy efficacy.

Decoding Brain Signals to Classify Gait Direction Anticipation.

The use of brain-computer interface (BCI) technology has emerged as a promising rehabilitation approach for patients with motor function and motor-related disorders. BCIs provide an augmentative communication platform for controlling advanced assistive robots such as a lower-limb exoskeleton. Brain recordings collected by an electroencephalography (EEG) system have been employed in the BCI platform to command the exoskeleton. To date, the literature on this topic is limited to the prediction of gait intention and gait variations from EEG signals. This study, however, aims to predict the anticipated gait direction using a stream of EEG signals collected from the brain cortex. Three healthy participants (age range: 29-31, 2 female) were recruited. While wearing the EEG device, the participants were instructed to initiate gait movement toward the direction of the arrow triggers (pointing forward, backward, left, or right) being shown on a screen with a blank white background. Collected EEG data was then epoched around the trigger timepoints. These epochs were then converted to the time-frequency domain using event- related synchronization (ERS) and event-related desynchronization (ERD) methods. Finally, the classification pipeline was constructed using logistic regression (LR), support vector machine (SVM), and convolutional neural network (CNN). A ten-fold cross-validation scheme was used to evaluate the classification performance. The results revealed that the CNN classifier outperforms the other two classifiers with an accuracy of 0.75. Clinical Relevance - The outcome of this study has the potential to be ultimately used for interactive navigation of the lower-limb exoskeletons during robotic rehabilitation therapy and enhance neurodegeneration and neuroplasticity in a wide range of individuals with lower-limb motor function disabilities.

Assessing user experience with BMI-assisted exoskeleton in patients with spinal cord injury.

Spinal Cord Injury (SCI) refers to damage to the spinal cord that can affect different body functionalities. Recovery after SCI depends on multiple factors, being the rehabilitation therapy one of them. New approaches based on robot-assisted training offer the possibility to make training sessions longer and with a reproducible pattern of movements. The control of these robotic devices by means of Brain-Machine Interfaces (BMIs) based on Motor Imagery (MI) favors the patient cognitive engagement during the rehabilitation, promoting mechanisms of neuroplasticity. This research evaluates the acceptance and feedback received from patients with incomplete SCI about the usage of a MI-based BMI with a lower-limb exoskeleton. Clinical Relevance- Patients experienced satisfaction when using the exoskeleton and levels of mental and physical workload were withing reasonable limits. In addition results from the BMI were promising for the inclusion of this type of systems in rehabilitation programs.

Users Maintain Task Accuracy and Gait Characteristics During Missed Exoskeleton Actuations Through Adaptations In Joint Kinematics.

In operational settings, lower-limb active exoskeletons may experience errors, where an actuation that should be present is missed. These missed actuations may impact users' trust in the system and the adapted human-exoskeleton coordination strategies. In this study, we introduced pseudorandom catch trials, in which an assistive exoskeleton torque was not applied, to understand the immediate responses to missed actuations and how users' internal models to an exoskeleton adapt upon repeated exposure to missed actuations. Participants (N = 15) were instructed to complete a stepping task while wearing a bilateral powered ankle exoskeleton. Human-exoskeleton coordination and trust were inferred from task performance (step accuracy), step characteristics (step length and width), and joint kinematics at selected peak locations of the lower limb. Step characteristics and task accuracy were not impacted by the loss of exoskeleton torque as hip flexion was modulated to support completing the stepping task during catch trials, which supports an impacted human-exoskeleton coordination. Reductions in ankle plantarflexion during catch trials suggest user adaptation to the exoskeleton. Trust was not impacted by catch trials, as there were no significant differences in task performance or gait characteristics between earlier and later strides. Understanding the interactions between human-exoskeleton coordination, task accuracy, and step characteristics will support development of exoskeleton controllers for non-ideal operational settings.

MEASUREMENT, PREDICTION AND VALIDATION OF HUMAN GAIT TORQUE FOR LOWER LIMB ASSISTIVE DEVICES

The lower limb rehabilitation process requires external control inputs based on joint kinematics and kinetics to its actuator for generating the motion of a robotic assistive device. This paper describes the procedures for measuring, predicting and validating the joint kinematics and kinetics of human gait for the lower limb exoskeleton. The high-speed multi-camera-based video system associated with passive optical markers and a force plate sensor is used to measure the gait data. The lower limb joint torques have also been predicted using three different machine learning regression models, viz. Decision Tree (DT), Gaussian Process Regression (GPR) and Support Vector Machine (SVM). The analysis is further validated with the geometry-based analytical method. Three lower limb joint torques have been predicted by utilizing gait period, joint angle, joint velocity and joint acceleration as the predictors. The lowest average 10-fold cross-validation Root Mean Square Error (RMSE) using the DT model between measured and predicted torques have been recorded for the ankle, knee and hip joint as 1.9521, 1.5527 and 1.5684[Formula: see text]Nm, respectively. This study enhances the field of medical rehabilitation by providing the required knowledge of human gait kinematics/kinetics and predicting the joint torque profile without any force sensor.

Predictive Locomotion Mode Recognition and Accurate Gait Phase Estimation for Hip Exoskeleton on Various Terrains

In recent years, lower-limb exoskeletons have been applied to assist people with weak mobility in daily life, which requires enhanced adaptability to complex environments. To achieve a smooth transition between different assistive strategies and provide proper assistance at the desired timing during locomotion on various terrains, two significant issues should be addressed: the delay of locomotion mode recognition (LMR) and the accuracy of gait phase estimation (GPE), which are yet critical challenges for exoskeleton controls. To tackle these challenges, a high-level exoskeleton control, including a depth sensor-based LMR method and an adaptive oscillator-based GPE approach, is developed in this study for terrain-adaptive assistive walking. An experimental study was conducted to evaluate the effectiveness and usability of the proposed control in a real-world scenario. Experimental results suggested that the depth sensor-based LMR method can detect the locomotion mode change 0.5 step ahead of the assistive strategy switch of the leading leg, while the average environment classification accuracy across five subjects was higher than 98 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . The accuracy is comparable with the state-of-the-art LMR methods, but its predictive capability is beyond existing LMR methods applied in lower-limb exoskeletons. Moreover, the adaptive oscillator-based GPE approach accurately estimated the user’s gait phase during locomotion on various terrains, with a root-mean-square (RMS) gait phase reset error of only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.27 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , outperforming the literature standard.

An sEMG-Based Human-Exoskeleton Interface Fusing Convolutional Neural Networks With Hand-Crafted Features.

In recent years, the human-robot interfaces (HRIs) based on surface electromyography (sEMG) have been widely used in lower-limb exoskeleton robots for movement prediction during rehabilitation training for patients with hemiplegia. However, accurate and efficient lower-limb movement prediction for patients with hemiplegia remains a challenge due to complex movement information and individual differences. Traditional movement prediction methods usually use hand-crafted features, which are computationally cheap but can only extract some shallow heuristic information. Deep learning-based methods have a stronger feature expression ability, but it is easy to fall into the dilemma of local features, resulting in poor generalization performance of the method. In this article, a human-exoskeleton interface fusing convolutional neural networks with hand-crafted features is proposed. On the basis of our previous study, a lower-limb movement prediction framework (HCSNet) in patients with hemiplegia is constructed by fusing time and frequency domain hand-crafted features and channel synergy learning-based features. An sEMG data acquisition experiment is designed to compare and analyze the effectiveness of HCSNet. Experimental results show that the method can achieve 95.93 and 90.37% prediction accuracy in both within-subject and cross-subject cases, respectively. Compared with related lower-limb movement prediction methods, the proposed method has better prediction performance.

A Novel Method for Detecting Misclassifications of the Locomotion Mode in Lower-Limb Exoskeleton Robot Control

Lower-limb exoskeleton robots can support hemiplegic patients’ affected limbs and assist in their rehabilitation. In order to set effective control strategies, it is necessary to obtain the user’s motion intention accurately and timeously. These requirements pose many challenges. The surface electromyography (sEMG) signal has long been used for detecting a person’s locomotion mode. However, most traditional myoelectric motion recognition methods need to collect multi-channel sEMG signals (more than eight) and use feature engineering to obtain the desired accuracy. Additionally, traditional methods make decisions when the recognition probability is still uncertain, resulting in misclassification during continuous recognition. In order to overcome these limitations, a dual-purpose autoencoder-guided temporal convolution network (DA-TCN) is proposed, which is used as a motion detection model for a lower-limb exoskeleton robot based on sEMG. In the proposed method, we first obtain four-channel sEMG signals without any feature engineering. Next, the DA-TCN is used to obtain the discriminative deep features of the sEMG signals. Compared with separable features from common TCNs, discriminative features assist in obtaining a more discriminative twins-loss. Finally, the classification result is redetermined by the decoder, based on the reconstruction of deep features, and misclassifications are rejected based on the value of the twins-loss. The performance of the proposed method was evaluated by obtaining data from seven volunteer subjects in seven locomotion modes. The proposed method was able to effectively detect misclassifications and significantly improved the stability of continuous recognition.

Enhancing Voluntary Motion with Modular, Backdrivable, Powered Hip and Knee Orthoses.

Mobility disabilities are prominent in society with wide-ranging deficits, motivating modular, partial-assist, lower-limb exoskeletons for this heterogeneous population. This paper introduces the Modular Backdrivable Lower-limb Unloading Exoskeleton (M-BLUE), which implements high torque, low mechanical impedance actuators on commercial orthoses with sheet metal modifications to produce a variety of hip- and/or knee-assisting configurations. Benchtop system identification verifies the desirable backdrive properties of the actuator, and allows for torque prediction within ±0.4 Nm. An able-bodied human subject experiment demonstrates that three unilateral configurations of M-BLUE (hip only, knee only, and hip-knee) with a simple gravity compensation controller can reduce muscle EMG readings in a lifting and lowering task relative to the bare condition. Reductions in mean muscular effort and peak muscle activation were seen across the primary squat musculature (excluding biceps femoris), demonstrating the potential to reduce fatigue leading to poor lifting posture. These promising results motivate applications of M-BLUE to additional populations, and the expansion of M-BLUE to bilateral and ankle configurations.

Ergonomic Assessment of a Lower-Limb Exoskeleton through Electromyography and Anybody Modeling System.

The aim of this study was to determine the muscle load reduction of the upper extremities and lower extremities associated with wearing an exoskeleton, based on analyses of muscle activity (electromyography: EMG) and the AnyBody Modeling System (AMS). Twenty healthy males in their twenties participated in this study, performing bolting tasks at two working heights (60 and 85 cm). The muscle activities of the upper trapezius (UT), middle deltoid (MD), triceps brachii (TB), biceps brachii (BB), erector spinae (ES), biceps femoris (BF), rectus femoris (RF), and tibialis anterior (TA) were measured by EMG and estimated by AMS, respectively. When working at the 60 cm height with the exoskeleton, the lower extremity muscle (BF, RF, TA) activities of EMG and AMS decreased. When working at the 85 cm height, the lower extremity muscle activity of EMG decreased except for TA, and those of AMS decreased except for RF. The muscle activities analyzed by the two methods showed similar patterns, in that wearing the exoskeleton reduced loads of the lower extremity muscles. Therefore, wearing an exoskeleton can be recommended to prevent an injury. As the results of the two methods show a similar tendency, the AMS can be used.

The AGoRA V2 Unilateral Lower-Limb Exoskeleton: Mechatronic Integration and Biomechanical Assessment

People who suffer from stroke have a higher difficulty performing gait activity, affecting their quality of life. New technologies have been developed to assist and rehabilitate the affected limbs. This paper presents the AGoRA V2 unilateral lower-limb exoskeleton and the assessment of physiological and spatiotemporal parameters of gait in 10 subjects who participated in a test. AGORA V2 combine a stiff structure for the Hip and Knee and the T-FLEX ankle exoskeleton (soft structure). The results showed a significant decrease in muscle activity compared to the condition without an exoskeleton. This decrease was 8% in Biceps Femoris (BF) muscle activity and 4% in Vastus Medialis (VM) muscle activity, generated by proper assistance of the devices thigh muscles. Tibialis Anterior (TA) and Lateral Gastrocnemius (LG) muscles and gait times did not show significant changes, which can be interpreted as a correct synchronisation of the devices with the person’s gait. The results obtained can be used as a baseline for future studies with pathological patients.

Design and Electromechanical Performance Evaluation of a Powered Parallel-Elastic Ankle Exoskeleton

The widespread adoption of powered lower-limb exoskeletons for augmenting mobility requires energy-efficient actuation to provide meaningful assistance over relevant walking durations. We designed, modeled, and validated an ankle exoskeleton design with a parallel elastic element in the form of a carbon fiber leaf spring that stored and returned energy in parallel to a cable-drive ankle joint during stance phase. We assessed the impact of the parallel elastic element on the performance of the untethered robotic ankle exoskeleton at walking speeds of 0.75–1.25 m/s using a previously validated-controller, and a controller designed specifically to maximize the spring's benefit. The spring selected for our adult cohort had a stiffness of 97.2 Nm/rad, engaged best at 0-degrees ankle dorsiflexion, and produced 10–15 Nm peak assistive torque at all walking speeds. When tracking the previously validated exoskeleton controller, peak motor current was reduced by 14–20% and integrated current was reduced by 16–19% for parallel-elastic design vs. without spring engagement; this translated to 15–26% more assisted steps for the same battery capacity. When utilizing the controller designed to take advantage of the parallel spring torque, the number of assisted steps for the same battery capacity increased 46–76% compared to the no spring configuration depending on walking speed. Seeking to facilitate real-world adoption, this powered parallel elastic ankle exoskeleton design holds potential to significantly extend powered walking duration, and improve battery and motor life of operation by reducing peak and mean motor current requirements.

Robust Iterative Learning Control Algorithm for Lower Limb Rehabilitation Proactive Human-robot Collaboration

At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

Optimal Design and Command Filtered Backstepping Control of Exoskeleton With Series Elastic Actuator

Abstract The lower limb exoskeleton can improve mobility and safety during rehabilitation for human. However, most current exoskeleton systems are not capable of providing variable joint stiffness in response to changing external demands. In this paper, a knee exoskeleton based on the series elastic actuator (SEA) is designed for safe human-computer interaction. The structural dimensions of the exoskeleton actuation mechanism were optimized based on gait biomechanics to ensure stability and compactness. While maintaining the mechanism range of motion (ROM), this optimization ensures that less peak force is required during the gait cycle. However, the insertion of series elastic actuators inevitably brings new challenges for high precision control of the exoskeleton, such as the problems of modeling errors, compliance, friction, and external disturbances in the exoskeleton joint. To achieve high precision control of the exoskeleton, an extended disturbance observer (EDO) based command filtered backstepping control (CFBC) of the knee exoskeleton is developed. The effective observation of friction, external disturbances, and modeling errors in the system is obtained by the EDO. Compared with conventional backstepping control, the CFBC can not only solve the “explosion of complexity” problem through a command filter but also reduce filter errors by an error compensation mechanism. Based on the Lyapunov stability, all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, comparison simulation results demonstrate the effectiveness of the proposed control approach.

A Framework for Determining the Performance and Requirements of Cable-Driven Mobile Lower Limb Rehabilitation Exoskeletons.

The global increase in the number of stroke patients and limited accessibility to rehabilitation has promoted an increase in the design and development of mobile exoskeletons. Robot-assisted mobile rehabilitation is rapidly emerging as a viable tool as it could provide intensive repetitive movement training and timely standardized delivery of therapy as compared to conventional manual therapy. However, the majority of existing lower limb exoskeletons continue to be heavy and induce unnecessary inertia and inertial vibration on the limb. Cable-driven exoskeletons can overcome these issues with the provision of remote actuation. However, the number of cables and routing can be selected in various ways posing a challenge to designers regarding the optimal design configuration. In this work, a simulation-based generalized framework for modelling and assessment of cable-driven mobile exoskeleton is proposed. The framework can be implemented to identify a ‘suitable’ configuration from several potential ones or to identify the optimal routing parameters for a given configuration. For a proof of concept, four conceptual configurations of cable-driven exoskeletons (one with a spring) were developed in a manner where both positive and negative moments could be generated for each joint (antagonistic configuration). The models were analyzed using the proposed framework and a decision metric table has been developed based on the models’ performance and requirements. The weight of the metrics can be adjusted depending on the preferences and specified constraints. The maximum score is assigned to the configuration with minimum requirement or error, maximum performance, and vice versa. The metric table indicated that the 4-cable configuration is a promising design option for a lower limb rehabilitation exoskeleton based on tracking performance, model requirements, and component forces exerted on the limb.