Background and Purpose— The amount of task-specific stepping practice provided during rehabilitation poststroke can influence locomotor recovery and reflects one aspect of exercise dose that can affect the efficacy of specific interventions. Emerging data suggest that markedly increasing the intensity and variability of stepping practice may also be critical, although such strategies are discouraged during traditional rehabilitation. The goal of this study was to determine the individual and combined contributions of intensity and variability of stepping practice to improving walking speed and distance in individuals poststroke. Methods— This phase 2, randomized, blinded assessor clinical trial was performed between May 2015 and November 2018. Individuals between 18 and 85 years old with hemiparesis poststroke of >6 months duration were recruited. Of the 152 individuals screened, 97 were randomly assigned to 1 of 3 training groups, with 90 completing >10 sessions. Interventions consisted of either high-intensity stepping (70%–80% heart rate reserve) of variable, difficult stepping tasks (high variable), high-intensity stepping performing only forward walking (high forward), and low-intensity stepping in variable contexts at 30% to 40% heart rate reserve (low variable). Participants received up to 30 sessions over 2 months, with testing at baseline, post-training, and a 3-month follow-up. Primary outcomes included walking speeds and timed distance, with secondary measures of dynamic balance, transfers, spatiotemporal kinematics, and metabolic measures. Results— All walking gains were significantly greater following either high-intensity group versus low-variable training (all P <0.001) with significant correlations with stepping amount and rate ( r =0.48–60; P <0.01). Additional gains in spatiotemporal symmetry were observed with high-intensity training, and balance confidence increased only following high-variable training in individuals with severe impairments. Conclusions— High-intensity stepping training resulted in greater improvements in walking ability and gait symmetry than low-intensity training in individuals with chronic stroke, with potential greater improvements in balance confidence. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT02507466.
Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics.To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient ( ).Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE<10%, >0.94) compared to FFANN (NRMSE<16%, >0.89).A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.
Lower extremity musculoskeletal computational models play an important role in predicting joint forces and muscle activation simultaneously and are valuable for investigating functional outcomes of the implants. However, current computational musculoskeletal models of total knee replacement rarely consider the bearing surface geometry of the implant. Therefore, these models lack detailed information about the contact loading and joint motion which are important factors for evaluating clinical performances. This study extended a rigid multi-body dynamics simulation of a lower extremity musculoskeletal model to incorporate an artificial knee joint, based upon a novel force-dependent kinematics method, and to characterize the in vivo joint contact mechanics during gait. The developed musculoskeletal total knee replacement model integrated the rigid skeleton multi-body dynamics and the flexible contact mechanics of the tibiofemoral and patellofemoral joints. The predicted contact forces and muscle activations are compared against those in vivo measurements obtained from a single patient with good agreements for the medial contact force (root-mean-square error = 215 N, ρ = 0.96) and lateral contact force (root-mean-square error = 179 N, ρ = 0.75). Moreover, the developed model also predicted the motion of the tibiofemoral joint in all degrees of freedom. This new model provides an important step toward the development of a realistic dynamic musculoskeletal total knee replacement model to predict in vivo knee joint motion and loading simultaneously. This could offer a better opportunity to establish a robust virtual modeling platform for future pre-clinical assessment of knee prosthesis designs, surgical procedures and post-operation rehabilitation.
Nearly 20% of patients who have undergone total knee arthroplasty (TKA) report persistent poor knee function. This study explores the idea that, despite similar knee joint biomechanics, the neuro-motor synergies may be different between high-functional and low-functional TKA patients. We hypothesized that (1) high-functional TKA recruit a more complex neuro-motor synergy pattern compared to low-functional TKA and (2) high-functional TKA patients demonstrate more stride-to-stride variability (flexibility) in their synergies. Gait and electromyography (EMG) data were collected during level walking for three groups of participants: (i) high-functional TKA patients (n=13); (ii) low-functional TKA patients (n=13) and (iii) non-operative controls (n=18). Synergies were extracted from EMG data using non-negative matrix factorization. Analysis of variance and Spearman correlation analyses were used to investigate between-group differences in gait and neuro-motor synergies. Results showed that synergy patterns were different among the three groups. Control subjects used 5-6 independent neural commands to execute a gait cycle. High functional TKA patients used 4-5 independent neural commands while low-functional TKA patients relied on only 2-3 independent neural commands to execute a gait cycle. Furthermore, stride-to-stride variability of muscles' response to the neural commands was reduced up to 15% in low-functional TKAs compared to the other two groups.
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