A new metric for characterizing limb loading dynamics in post anterior cruciate ligament reconstruction individuals.

BACKGROUND: Unresolved neuromuscular deficits often persist in post-anterior cruciate ligament reconstruction (ACLR) individuals manifesting as altered impact and active peak force production during running that can contribute to detrimental limb loading. Elevated impact and active peaks are common in pathological populations indicating a stiffer limb loading strategy. Although impact and active peaks are sensitive to changes in limb loading, to our knowledge, there are no established, standardized measures or cutoff criteria to differentiate between healthy and pathological limb loading. However, prior studies have demonstrated that the ratio between traditional biomechanical measures can be used to successfully establish quantifiable and graphical ranges to delineate between healthy and pathological movement. RESEARCH QUESTION: Therefore, this study sought to exploit the impact-to-active peak ratio to generate a new, standardized metric to quantify and characterize limb loading dynamics in healthy controls and post-ACLR individuals during running. METHODS: Twenty-eight post-ACLR individuals and 18 healthy controls performed a running protocol. Impact peak and active peak data were extracted from their strides as they ran at a self-selected speed. A linear regression model was fit to the healthy control data and the models 95 % prediction intervals were used to define a boundary region of healthy limb loading dynamics. RESULTS: The post-ACLR individuals produced a higher impact-to-active peak ratio than the healthy controls indicating that they adopted a stiffer limb loading strategy. The boundary regions derived from the impact and active peak model successfully classified the healthy controls and post-ACLR individual's limb loading dynamics with an accuracy, sensitivity, and specificity of 89 %, 100 %, and 75 %, respectively. SIGNIFICANCE: The ability to effectively evaluate limb loading dynamics using impact and active peaks can provide clinicians with a new, non-invasive metric to quantify and characterize healthy and pathological movement in a clinical setting.

Classification Model for Discriminating Trunk Fatigue During Running.

PURPOSE: Fatigue is often associated with increased injury risk. Many studies have focused on fatigue in the lower extremity muscles brought on by running, yet few have examined the relationship between fatigue of the core musculature and associated changes in running gait. To investigate the relationship between trunk fatigue and running dynamics, this study had two goals: (1) to use machine learning to determine which gait parameters are most associated with trunk fatigue; and (2) to develop a machine learning algorithm that uses those parameters to classify individuals with trunk fatigue. We hypothesized that we could effectively differentiate between the non-fatigued and fatigued states using machine learning models derived from running gait parameters. METHODS: Seventy-two individuals performed a trunk fatigue protocol. Lower extremity running biomechanics were collected pre- and post- the trunk fatigue protocol using an instrumented treadmill and 10-camera motion capture system.The fatiguing protocol involved executing a series of trunk fatiguing exercises until established fatigue criteria were reached. Gait variables extracted from the non-fatigued and fatigued states served as model inputs to aid in the development of the machine learning model that would distinguish between non-fatigued and fatigued running. RESULTS: The machine learning protocol determined three variables - stance time, maximum propulsive GRF and maximum braking GRF - to be the best discriminators between non-fatigued and fatigued running. The SVM with Bagging was the best performing model that discriminated between non-fatigued and fatigued running with an accuracy of 82%, precision of 77%, recall of 90%, and area under the receiver operating curve of 0.91. CONCLUSION: The machine learning model was effective in classifying between non-fatigued and fatigued running using three gait parameters extracted from GRF waveforms. The ability to classify fatigue using these easy to measure GRF derived parameters enhances the ability for the model to be integrated into wearable technology and the clinical setting to aid in the detection of fatigue and potentially injury, as fatigue is often a precursor to injury.Clinical Relevance- This model has the potential to be implemented in a clinical setting to determine the onset of trunk fatigue through basic gait analysis, involving only the ground reaction forces. This model would be aimed toward injury prevention since fatigue is linked to increased risk of injury.

A Metric for Identifying Stress Fractures in Runners.

PURPOSE: Stress fractures are common overuse running injuries. Individuals with stress fractures exhibit running biomechanics characterized by elevated impact peak and loading rate. While elevated impact peak and loading rate are associated with stress fractures, there are few established metrics used to identify the presence of stress fractures in individuals. Here this study aims to exploit the linear relationship between the impact peak and loading rate to establish a metric to help identify individuals with stress fractures. We hypothesize that the ratio between the impact peak and loading rate will serve as a metric to delineate between healthy controls and those with stress fractures. METHODS: Fifteen healthy controls and 11 individuals with stress fractures performed a running protocol. A linear regression model fit to the stress fracture impact peak and loading rate data produced a lower 95% confidence limit boundary that served as the demarcation line between the two groups. RESULTS: Individuals with stress fractures tended to reside above the line with the line accurately classifying 82% of the individuals with stress fractures. CONCLUSION: The analysis supported the hypothesis and demonstrated how the relationship between impact peak and loading rate can help identify the presence of stress fractures in individuals.Clinical Relevance- The relationship between impact peak and loading rate has the potential to serve as clinically useful metric to identify stress fractures during running.

Force and Rate Metrics Provide Return-to-Sport Criterion after ACL Reconstruction.

PURPOSE: Peak vertical ground reaction force and linear loading rate can be valuable metrics for return-to-sport assessment because they represent limb loading dynamics; yet, there is no defined cutoff criterion to differentiate between healthy and altered limb loading. Studies have shown that healthy individuals exhibit strong first-order relationships between gait variables whereas individuals with pathological conditions did not. Thus, this study sought to explore and exploit this first-order relationship to define a region of healthy limb dynamics, which individuals with pathological conditions would reside outside of, to rapidly assess individuals with altered limb loading dynamics for return to sport. We hypothesized that there would be a strong first-order linear relationship between vertical ground reaction force peak force and linear loading rate in healthy controls' limbs, which could be exploited to identify abnormal limb loading dynamics in post-anterior cruciate ligament reconstruction (ACLR) individuals. METHODS: Thirty-one post-ACLR individuals and 31 healthy controls performed a running protocol. A first-order regression analysis modeled the relationship between peak vertical ground reaction forces and linear vertical ground reaction force loading rate in the healthy control limbs to define a region of healthy dynamics to evaluate post-ACLR reconstructed limb dynamics. RESULTS: A first-order regression model aided in the determination of cutoff criteria to define a region of healthy limb dynamics. Ninety percent of the post-ACLR reconstructed limbs exhibited abnormal limb dynamics based on their location outside of the region of healthy dynamics. CONCLUSION: This approach successfully delineated between healthy and abnormal limb loadings dynamics in controls and post-ACLR individuals. The findings demonstrate how force and loading rate-dependent metrics can help develop criteria for individualized post-ACLR return-to-sport assessment.

Classification of Post-Anterior Cruciate Ligament Reconstruction Running Dynamics using Non-Traditional Features.

Despite extensive rehabilitation, nearly half of all post-anterior cruciate ligament reconstruction (ACLR) individuals are unable to perform dynamic tasks at the level they did prior to their injury. This inability can be attributed to unresolved neuromuscular deficits that manifest as altered limb dynamics. While traditional discrete metrics; such as peak vertical ground reaction force (vGRF) and peak knee flexion angle, have been used to successfully differentiate between healthy and pathological running dynamics, recent studies have shown that non-traditional metrics derived from autoregressive (AR) modeling and Smoothed Pseudo Wigner-Ville (SPWV) analysis techniques can also successfully delineate between healthy and pathological populations and could potentially possess greater sensitivity than the traditional metrics. Thus, the objective of this study was to compare the performance of classification models generated from traditional and nontraditional metrics collected from healthy controls and post-ACLR individuals during a running protocol. We hypothesized that the non-traditional metric-based classification model would outperform the traditional metric based model. Thirty-one controls and 31 post-ACLR individuals performed a running protocol from which the traditional metrics - peak vGRF, linear vGRF loading rate and peak knee flexion angle - and nontraditional metrics - dynamic vGRF ratio, AR model coefficients, and a SPWV derived low frequency-high frequency ratio - were extracted from vGRF and knee flexion running waveforms. The results indicated that a fine Gaussian SVM classification model derived from the non-traditional metrics had an accuracy of 87%, specificity of 83% and sensitivity of 61% and it outperformed the classification model derived from traditional metrics. These findings indicate that additional, valuable information can be ascertained from non-traditional metrics that evaluate waveform dynamics. Additionally, it suggests that this or similar models can be used to track the restoration of healthy running dynamics in post-ACLR individuals during rehabilitation.

Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease.

BACKGROUND: Huntington's disease (HD) is a progressive, neurological disorder that results in both cognitive and physical impairments. These impairments affect an individual's gait and, as the disease progresses, it significantly alters one's stability. Previous research found that changes in stride time patterns can help delineate between healthy and pathological gait. Autoregressive (AR) modeling is a statistical technique that models the underlying temporal patterns in data. Here the AR models assessed differences in gait stride time pattern stability between the controls and individuals with HD. Differences in stride time pattern stability were determined based on the AR model coefficients and their placement on a stationarity triangle that provides a visual representation of how the patterns mean, variance and autocorrelation change with time. Thus, individuals who exhibit similar stride time pattern stability will reside in the same region of the stationarity triangle. It was hypothesized that individuals with HD would exhibit a more altered stride time pattern stability than the controls based on the AR model coefficients and their location in the stationarity triangle. METHODS: Sixteen control and twenty individuals with HD performed a five-minute walking protocol. Time series' were constructed from consecutive stride times extracted during the protocol and a second order AR model was fit to the stride time series data. A two-sample t-test was performed on the stride time pattern data to identify differences between the control and HD groups. RESULTS: The individuals with HD exhibited significantly altered stride time pattern stability than the controls based on their AR model coefficients (AR1 p < 0.001; AR2 p < 0.001). CONCLUSIONS: The AR coefficients successfully delineated between the controls and individuals with HD. Individuals with HD resided closer to and within the oscillatory region of the stationarity triangle, which could be reflective of the oscillatory neuronal activity commonly observed in this population. The ability to quantitatively and visually detect differences in stride time behavior highlights the potential of this approach for identifying gait impairment in individuals with HD.