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This paper introduces a compact end-effector ankle rehabilitation robot (CEARR) system for addressing ankle range of motion (ROM) rehabilitation. The CEARR features a bilaterally symmetrical rehabilitation structure, with each side possessing three degrees of freedom (DOF) driven by three independently designed actuators. The working intervals of each actuator are separated by a series connection, ensuring they operate without interference to accommodate the dorsiflexion/plantarflexion (DO/PL), inversion/eversion (IN/EV), and adduction/abduction (AD/AB) DOF requirements for comprehensive ankle rehabilitation. In addition, we integrated an actuator and foldable brackets to accommodate patients in varied postures. We decoded the motor intention based on the surface electromyography (sEMG) and torque signals generated by the subjects' ankle joints in voluntary rehabilitation. Besides, we designed a real-time voluntary-triggered control (VTC) strategy to enhance the rehabilitation effect, in which the root mean square (RMS) of sEMG was utilized to trigger and adjust the CEARR rehabilitation velocity support. We verified the consistency of voluntary movement with CEARR rehabilitation support output for four healthy subjects on a nonlinear sEMG signal with an R 2 metric of approximately 0.67. We tested the consistency of triggering velocity trends with a linear torque signal for one healthy individual with an R 2 metric of approximately 0.99.

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Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions.

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Background: With an aging population, the prevalence of neurological disorders is increasing, leading to a rise in lower limb movement disorders and, in turn, a growing need for rehabilitation training. Previous neuroimaging studies have shown a growing scientific interest in the study of brain mechanisms in robot-assisted lower limb rehabilitation (RALLR). Objective: This review aimed to determine differences in neural activity patterns during different RALLR tasks and the impact on neurofunctional plasticity. Methods: Sixty-five articles in the field of RALLR were selected and tested using three brain function detection technologies. Results: Most studies have focused on changes in activity in various regions of the cerebral cortex during different lower limb rehabilitation tasks but have also increasingly focused on functional changes in other cortical and deep subcortical structures. Our analysis also revealed a neglect of certain task types. Conclusion: We identify and discuss future research directions that may contribute to a clear understanding of neural functional plasticity under different RALLR tasks.

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Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.

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Most lower limb rehabilitation robots are limited to specific training postures to adapt to stroke patients in multiple stages of recovery. In addition, there is a lack of attention to the switching functions of the training side, including left, right, and bilateral, which enables patients with hemiplegia to rehabilitate with a single device. This article presents an exoskeleton robot named the multistage hemiplegic lower-limb rehabilitation robot, which has been designed to do rehabilitation in multiple training postures and training sides. The mechanism consisting of the thigh, calf, and foot is introduced. Additionally, the design of the multi-mode limit of the hip, knee, and ankle joints supports delivering therapy in any posture and training sides to aid patients with hemiplegia in all stages of recovery. The gait trajectory is planned by extracting the gait motion trajectory model collected by the motion capture device. In addition, a control system for the training module based on adaptive iterative learning has been simulated, and its high-precision tracking performance has been verified. The gait trajectory experiment is carried out, and the results verify that the trajectory tracking performance of the robot has good performance.

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Lower limb rehabilitation is essential for recovery post-injury, stroke, or surgery, improving functional mobility and quality of life. Traditional therapy, dependent on therapists' expertise, faces challenges that are addressed by rehabilitation robotics. In the domain of lower limb rehabilitation, machine learning is progressively manifesting its capabilities in high personalization and data-driven approaches, gradually transforming methods of optimizing treatment protocols and predicting rehabilitation outcomes. However, this evolution faces obstacles, including model interpretability, economic hurdles, and regulatory constraints. This review explores the synergy between machine learning and robotic-assisted lower limb rehabilitation, summarizing scientific literature and highlighting various models, data, and domains. Challenges are critically addressed, and future directions proposed for more effective clinical integration. Emphasis is placed on upcoming applications such as Virtual Reality and the potential of deep learning in refining rehabilitation training. This examination aims to provide insights into the evolving landscape, spotlighting the potential of machine learning in rehabilitation robotics and encouraging balanced exploration of current challenges and future opportunities.

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Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

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Individuals with acute neurological or limb-related disorders may be temporarily bedridden and unable to go to the physical therapy departments. The rehabilitation training of these patients in the ward can only be performed manually by therapists because the space in inpatient wards is limited. This paper proposes a bedside cable-driven lower-limb rehabilitation robot based on the sling exercise therapy theory. The robot can actively drive the hip and knee motions at the bedside using flexible cables linking the knee and ankle joints. A human-cable coupling controller was designed to improve the stability of the human-machine coupling system. The controller dynamically adjusts the impedance coefficient of the cable driving force based on the impedance identification of the human lower-limb joints, thus realizing the stable motion of the human body. The experiments with five participants showed that the cable-driven rehabilitation robot effectively improved the maximum flexion of the hip and knee joints, reaching 85° and 90°, respectively. The mean annulus width of the knee joint trajectory was reduced by 63.84%, and the mean oscillation of the ankle joint was decreased by 56.47%, which demonstrated that human joint impedance identification for cable-driven control can effectively stabilize the motion of the human-cable coupling system.

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Spinal cord injury (SCI) is a serious disabling injury, and the main factors causing SCI in patients include car accidents, falls from heights, as well as heavy blows and falls. These factors can all cause spinal cord compression or even complete rupture. After SCI, problems with the movement, balance, and walking ability of the lower limbs are most common, and SCI can cause abnormalities in patient's movement, sensation, and other aspects. Therefore, in the treatment of SCI, it is necessary to strengthen the rehabilitation training (RT) of patients based on data science to improve their motor ability and play a positive role in the recovery of their walking ability. This article used lower limb rehabilitation robot (LLRR) to improve the walking ability of SCI patients and applied them to SCI rehabilitation. The purpose is to improve the limb movement function of patients by imitating and assisting their limb movements, thereby achieving pain relief and muscle strength enhancement and promoting rehabilitation. The experimental results showed that the functional ambulation category (FAC) scale scores of Group A and Group B were 0.79 and 0.81, respectively, in the first 10 weeks of the experiment. After 10 weeks of the experiment, the FAC scores of Group A and Group B were 2.42 and 4.36, respectively. After the experiment, the FAC score of Group B was much higher than that of Group A, indicating that Group B was more effective in improving patients' walking ability compared to Group A. This also indicated that LLRR rehabilitation training can enhance the walking ability of SCI patients.

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Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient's motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation. Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient's desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory. Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively.

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The restricted posture and unrestricted compliance brought by the controller during human-exoskeleton interaction (HEI) can cause patients to lose balance or even fall. In this article, a self-coordinated velocity vector (SCVV) double-layer controller with balance-guiding ability was developed for a lower-limb rehabilitation exoskeleton robot (LLRER). In the outer loop, an adaptive trajectory generator that follows the gait cycle was devised to generate a harmonious hip-knee reference trajectory on the non-time-varying (NTV) phase space. In the inner loop, velocity control was adopted. By searching the minimum L2 norm between the reference phase trajectory and the current configuration, the desired velocity vectors in which encouraged and corrected effects can be self-coordinated according to the L2 norm were obtained. In addition, the controller was simulated using an electromechanical coupling model, and relevant experiments were carried out with a self-developed exoskeleton device. Both simulations and experiments validated the effectiveness of the controller.

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This study presents a model of cooperation between two planar manipulators including an orthosis and a programmable plate in form of a hybrid lower limb rehabilitation robot, which was designed and built at the University of Guilan. The aims of cooperation are to distribute the power required to move between the cooperative manipulators and also reduce the interaction forces between orthosis and leg. The cooperation is performed with two modes using the adjustment of the plate forces, a constant force in the vertical direction (CFV) and variant force proportional to orthosis torque (VFPOT). Kinematic and dynamic analysis of the hybrid lower limb rehabilitation robot and its control are also discussed in this study. The performance and effectiveness of the proposed hybrid robot are demonstrated on a healthy person in real-time. Each walking trial lasted 60 s and repeated 20 times for every mode. The walking speed was considered to be 1.5 km/h and weight compensator was adjusted with a constant weight unloading level of 70%. The results show that the VFPOT mode leads to a 45% reduction in the driving torque of the hip and knee joints compared to orthosis-only. This reduction is expected to reduce interaction force at the connection straps. So, it provides more patient comfort and safety, which can be effective in improving the time and process of rehabilitation.

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Although Cable-driven rehabilitation devices (CDRDs) have several advantages over traditional link-driven devices, including their light weight, ease of reconfiguration, and remote actuation, the majority of existing lower-limb CDRDs are limited to rehabilitation in the sagittal plane. In this work, we proposed a novel three degrees of freedom (DOF) lower limb model which accommodates hip abduction/adduction motion in the frontal plane, as well as knee and hip flexion/extension in the sagittal plane. The proposed model was employed to investigate the feasibility of using bi-planar cable routing to track a bi-planar reference healthy trajectory. Various possible routings of four cable configurations were selected and studied with the 3DOF model. The optimal locations of the hip cuffs were determined using optimization. When compared with the five-cable routing configuration, the four-cable routing produced higher joint forces, which motivated the future study of other potential cable routing configurations and their ability to track bi-planar motion.

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With the popularization of biomechanical simulation technology, aiming at the rehabilitation of ankle joint injury, we imported simplified model of proposed 2-UPS/RR (two identical unconstraint kinematic branches with a universal-prismatic-spherical (UPS) structure and two rotating pair (R)) ankle rehabilitation robot into AnyBody Modeling System. Therefore, a human-machine model was established using the HILL-type muscle model and muscle recruitment criteria. This paper investigated the effects of rehabilitation trajectories on biomechanical response during rehabilitation. Additionally, three main lower limb muscles (soleus, peroneal brevis, and extensor digitorum longus) were examined under different rehabilitation trajectories (plantar dorsiflexion, varus or valgus, and compound movement) in the present study. Based on the biomechanical response of lower limbs, the results showed that different muscles had different sensitivities to the change of rehabilitation trajectories. The correlation coefficient between joint force and plantar dorsiflexion angle reached 0.99 (P < 0.01), indicating that the change of joint force was mainly dominated by plantar dorsiflexion/plantar flexion, but also affected by varus or valgus. Safe rehabilitation training can be achieved by controlling the designed 2-UPS/RR rehabilitation robot. The behavior of muscle force and joint force under different rehabilitation trajectories can meet the needs of rehabilitation and treatment of joint diseases, and provide more reasonable suggestions for early rehabilitation.

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BACKGROUND: During neurological rehabilitation training for patients with lower limb dysfunction, active rehabilitation training based on interactive force recognition can effectively improve participation and efficiency in rehabilitation training. OBJECTIVE: This study proposes an active training strategy for lower-limb rehabilitation robots based on a spring damping model. METHODS: The active training strategy included a kinetic model of the human-machine system, calculated and verified using a pull-pressure force sensor We used a dynamic model of the human-machine system and tensile force sensors to identify the human-machine interaction forces exerted by the patient Finally, the spring damping model is used to convert the active interaction force into the offset angle of each joint, obtaining the active interaction force followed by the active movement of the lower limbsRESULTS:The experimental results showed that the rehabilitation robot could follow the active interaction force of the subject to provide assistance, thus generating the following movement and effectively helping patients improve joint mobility. CONCLUSION: The active flexibility training control strategy based on the virtual spring damping model proposed in this study is feasible, and motion is stable for patients with lower limb dysfunction after stroke Finally, the proposed active training method can be implemented in future work in other rehabilitation equipment and combined virtual reality technology to improve rehabilitation training experience and increase patient participation.

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ObjectiveTo explore the effects of different intensity of wearable lower limb rehabilitation robot-assisted training on walking function, lower limb motor function, balance function and functional independence of stroke patients. MethodsFrom November, 2021 to December, 2022, 60 stroke patients hospitalized in Beijing Bo'ai Hospital were randomly divided into control group (n = 20), observation group 1 (n = 20) and observation group 2 (n = 20). All the groups received routine rehabilitation, while the control group received routine walking training 30 minutes a day, the observation group 1 received wearable lower limb rehabilitation robot-assisted training 30 minutes a day, and the observation group 2 received wearable lower limb rehabilitation robot-assisted training 60 minutes a day, for four weeks. They were assessed with Functional Ambulation Category scale (FAC), Fugl-Meyer Assessment-Lower Extremities (FMA-LE), Berg Balance Scale (BBS) and Rivermead Mobility Index (RMI) before and after treatment. ResultsOne case in the observation group 1 and three cases in the observation group 2 dropped down. The FAC, FMA-LE, BBS and RMI scores improved in all the three groups after treatment (|Z| > 3.448, |t| > 8.102, P < 0.001), and there was no significant difference in all the indexes among the three groups (|H| < 4.643, F = 1.454, P > 0.05); however, the improvement of BBS score was more in the observation group 1 than in the control group (P < 0.05), and the improvement of all the indexes was more in the observation group 2 than in the control group (P < 0.05). ConclusionThe wearable lower limb rehabilitation robot-assisted training may promote the recovery of walking function, lower limb motor function, balance function and functional independence of stroke patients, and high-intensity training seems to be more effective.

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Objective:To investigate the therapeutic effect of orthopedic trauma therapy device combined with URA lower limb rehabilitation trainer in patients who underwent total knee arthroplasty caused by traumatic fracture,and to analyze the effect of that on limb activity and quality of life.Methods:A total of 198 patients with end-stage knee osteoarthritis who were hospitalized in the hospital were randomly divided into a control group(69 cases,referred to as the control group),an orthopedic trauma therapy device group(63 cases,referred to as the therapy device group)and an orthopedic trauma therapy device combined with URA lower limb rehabilitation trainer group(66 cases,referred to as the combination group),and all patients underwent surgery of total knee arthroplasty.The American Knee Society Score(AKSS),Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)score,knee joint range of motion,quality of life,pain score and the evaluation of post-treatment satisfaction were observed and compared among the three groups.Results:The AKSS score and knee joint range of motion of the combination group were respectively higher than those of the control group after therapy,and the differences were significant(t=10.69,t=17.34,P<0.05).The WOMAC score of the combination group was significantly lower than that of the control group(t=26.98,P<0.05)after therapy,and the difference was statistically significant.The satisfaction of the combination group was significantly higher than that of the control group(t=9.93,P<0.05).The social function,physical function,role function and cognitive function of the quality of life in the combination group were significantly higher than those of control group(t=17.48,t=13.20,t=19.57,t=21.74,P<0.05),respectively.The pain score of the combination group was significantly lower than that of the control group(t=32.62,P<0.05),and the difference was statistically significant.Conclusion:The combination of orthopedic trauma therapy device combined with URA lower limb rehabilitation trainer has better clinical value in treating postoperative patients who undergo total knee arthroplasty in postoperatively fast rehabilitation and improving quality of life of them.

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Lower limb rehabilitation robots (LLRRs) have shown promising potential in assisting hemiplegic patients to recover their motor function. During LLRR-aided rehabilitation, the dynamic uncertainties due to human-robot coupling, model uncertainties, and external disturbances, make it challenging to achieve high accuracy and robustness in trajectory tracking. In this study, we design a triple-step controller with linear active disturbance rejection control (TSC-LADRC) for a LLRR, including the steady-state control, feedforward control, and feedback control. The steady-state control and feedforward control are developed to compensate for the gravity and incorporate the reference dynamics information, respectively. Based on the linear active disturbance rejection control, the feedback control is designed to enhance the control performance under dynamic uncertainties. Numerical simulations and experiments are conducted to validate the effectiveness of TSC-LADRC. The results of simulations illustrate that the tracking errors under TSC-LADRC are obviously smaller than those under the triple-step controller without LADRC (TSC), especially with the change of external loads. Moreover, the experiment results of six healthy subjects reveal that the proposed method achieves higher accuracy and lower energy consumption than TSC. Therefore, TSC-LADRC has the potential to assist hemiplegic patients in rehabilitation training.

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The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based gain-tuned compliance control (EGCC) strategy for the lower limb rehabilitation robot. First, the mapping function relationship between the normalized surface electromyography (sEMG) signal and the gain parameter was established and an overall EGCC strategy proposed. Next, the EGCC strategy without sEMG information was simulated and analyzed. The effects of the impedance control parameters on the position correction amount were studied, and the change rules of the robot end trajectory, man-machine contact force, and position correction amount analyzed in different training modes. Then, the sEMG signal acquisition and feature analysis of target muscle groups under different training modes were carried out. Finally, based on the lower limb rehabilitation robot control system, the influence of normalized sEMG threshold on the robot end trajectory and gain parameters under different training modes was experimentally studied. The simulation and experimental results show that the adoption of the EGCC strategy can significantly enhance the compliance of the robot end-effector by detecting the sEMG signal and improve the safety of the robot in different training modes, indicating the EGCC strategy has good application prospects in the rehabilitation robot field.

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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.