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- Running Mechanics and Gait Analysis PDF
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- Biomechanics and analysis of running gait
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Running Mechanics and Gait Analysis PDF
Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics.
These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction feature selection and extraction , and learning algorithms classification and clustering. Biomechanical gait analysis is commonly used to analyse sport performance and evaluate pathologic gait. Significant advances in motion capture equipment, research methodologies, and data analysis techniques have enabled a plethora of studies that have advanced our understanding of gait biomechanics.
More likely, multiple biomechanical and clinical variables interact with one another and operate as combined risk factors to the point that traditional biomechanical analysis methods e. In response to these shortcomings, advanced multivariate analysis and machine learning methods such as principal component analysis PCA and support vector machine SVM have been used to identify these complex associations [ 2 , 3 ].
However, to build accurate classification models, an adequate number of samples is needed, which grows exponentially with the number of features used in the analysis [ 4 ]. Therefore, to directly meet this need the University of Calgary group Ferber, Osis have developed the infrastructure and established a worldwide and growing network of clinical and research partners all linked through an automated three-dimensional 3D biomechanical gait data collection system: 3D GAIT.
Next, a comprehensive overview of existing methods on the role of big data analytics is presented. We discuss the main components of modern biomechanical gait analysis involving: initial input features, dimensionality reduction using feature selection and feature extraction, and learning algorithms via classification and clustering.
The 3D GAIT system is a deployed turnkey motion capture platform specifically designed for gait analysis using a treadmill. Consequently, the system uses off-the-shelf passive motion capture technology, consisting of between three and six infrared cameras Vicon Motion Systems, Oxford along with spherical retroreflective markers that are pre-configured for ease of placement on the subject.
Additional markers are also placed on the specific anatomical landmarks and are used to define the location of joint centers. These marker data are then transformed using rigid-body kinematics [ 8 , 9 ] into joint angles, which are 3D representations of body movements, between segments, over time. Joint angles from treadmill gait represent a set of non-independent, time-series waveforms, and there are several types of analyses that can be undertaken.
These normalized gait cycles can then be analyzed by: 1 collapsing into a single representative time-series data set by various averaging techniques i. In the latter case, 3D GAIT software generates a feature vector of 74 variables for both left and right sides, representing a substantial number of dimensions in the final data set. These feature vectors and time-series curves are then presented to the user to inform assessment, rehabilitation and training tasks.
After processing, and according to best practices in data science, the final data set is anonymized and packaged for transport. Marker data from the motion capture system, along with biomechanical feature vectors and demographic information i.
These aggregate data, along with critical subject characteristics, allow the potential to statistically model lower limb injury and disease outside of the laboratory setting. Post-hoc analysis also provides the opportunity to develop updated biomechanical models and techniques, which can then inform future modifications to the deployed software.
Despite advances in 3D motion analysis, there are some limitation inherent within any 3D gait analysis methodology. For example, variability in kinematic variables may be attributed to measurement error, skin marker movement, marker re-application errors, and inherent physiological variability during human locomotion. While the first three factors are independent of the patient population itself, it is possible that physiological variability may be different in a clinical population.
Moreover, set-up and operation of 3D gait systems requires calibration by an trained expert, and the time required to operate these systems limits its practicality, especially in a clinical setting. This definition also implies that many traditional analytics may not be able to be applied directly to big data.
Volume refers to vast amounts of data. While traditional biomechanical analysis generally involves only a few variables and low subject numbers, recent advancements in data collection technology generate more data for each subject, and modern biomechanical research is continually involving big volume datasets. Although most of these studies continue to involve only a small cohort of subjects e.
The aforementioned research database can provide the necessary large cohort of subjects for such hypothesis-driven research e. These data would include continuous, discrete, and categorical data and thus sophisticated statistical methods need to be employed.
Velocity refers to the pace at which new data is created and collected. Walking and running gait related-injuries are often chronic in nature and rehabilitation often takes weeks-to-months. In order to monitor the progress of a rehabilitation program, gait data are generally collected at baseline, and some data are collected once a week over several weeks of the program [ 19 , 20 ].
On average, 25 new patients are added each week to the UCalgary research database, and 12—15 new clinic partners are added each year. Veracity refers to noisy, erroneous, or incomplete data and in biomechanics research this term can easily apply as data are often captured through different sensors and systems, as well involving measurement errors associated with kinematic data.
Sources of error can result from many factors such as soft tissue artifact, electrical interference, and improper digitization and placement of retro-reflective markers.
Although in general, there is a large divide between clinical research and clinical practice. Data from standard 3D motion capture systems are generally of high quality. Despite this, there is the possibility of incomplete clinical data, due to missing self-reports and lab exams. Fortunately, big data analytics can handle incomplete data sets when necessary using data science techniques such as k -nearest neighbors to impute the missing values [ 21 ]. Although the potential value associated with these complex and large data sets is very high, the real value of big data analytics in gait biomechanics still remains to be proven, and more sophisticated analytics, which incorporate a priori knowledge, are necessary.
For instance, suitable techniques for extracting useful information can lead to high value outcomes even in situations with low data veracity. Further, multivariate analysis and machine learning methods could potentially be utilized as an automated system for detecting gait changes related to injury [ 3 , 22 ].
However, more research is necessary to advance these ideas. Most investigations of walking and running gait biomechanics involve kinematic data and have focused on determining gait waveform events such as joint angles at touchdown, toe-off, mid-stance, and mid-swing [ 23 ]. Descriptive statistics such as peak angles, excursion, and range of motion are also commonly extracted from the gait waveform [ 23 ].
Consequently, a large proportion of the kinematic data are discarded, whereas they may contain meaningful information related to the between-group differences. In contrast, modern data science methods involve the following main components of initial input features, dimensionality reduction using feature selection and feature extraction, and learning algorithms via classification and clustering Fig. By expanding initial input features, and analysing the entire gait waveform, new insights can be derived from the data to help improve clinical practice.
For example, Phinyomark et al. The results suggest that clinicians should focus on strengthening proximal muscles for female runners and distal muscles for male runners to prevent ITBS injury, and these meaningful joint angles are obtained by examining the entire set of input data. Hence, either a set of representative variables [ 2 , 3 , 24 ] or the entire gait waveforms [ 13 , 14 ] across joints and planes of motion should be employed as the initial set of input features.
Another input feature method involves a position matrix, which is based on the marker position data [ 15 , 16 ]. The size of an initial feature vector based on 3D marker position is usually larger than an initial input vector based on joint angles.
However, more data does not necessarily mean more useful information since it may contain more ambiguous or erroneous data such as soft tissue movement artefact. While similar results obtained from the same statistical group and supervised learning classification approaches were found in a comparison between two previous studies: one with joint angle data [ 2 ] and the other with marker position data [ 25 ], the clinical relevance of marker position data is questionable. Thus, joint kinematic angles are recommended to use as initial input features to improve the clinical relevance of the results.
Due to the fact that some dimensionality reduction and machine learning methods require an adequate number of samples to obtain stable results, the dimensionality of the initial input features used in the analysis should be carefully chosen. For example, Barrett and Kline [ 26 ] recommended that the number of subjects should be at least 50 for a PCA approach. Thus, to minimize the high-dimensionality of the data, big data in terms of big volume i. To analyze a large number of biomechanical gait variables involving many joints and planes of motion, techniques to retain information that is important for class discrimination and discard that which is irrelevant are necessary.
To determine which data should be retained and which can be discarded, dimensionality reduction has been used. Specifically, dimensionality reduction can be defined as the process of reducing data size or the number of initial input features, and this approach is expected to be able to operate effectively in big data analytics.
Although the size of the data in most current studies might be efficiently processed using traditional multivariate and machine learning methods in a single high performance computer, to process larger-scale and more complex data in future studies, it is necessary to re-design and change the way the traditional multivariate and machine learning methods are computed. One way is to use more efficient data analysis methods that can accelerate the computation time or to reduce the memory cost for the analysis.
Another way is to create methods that are capable of analyzing big data by modifying traditional methods that work on a parallel computing environment or even to develop new methods that work naturally on a parallel computing or a cloud computing environment.
It should be noted that these approaches not only reduce the computational cost, but can also possibly improve classification accuracy by reducing the noise and improving the clinical relevance of the results by selecting more interpretable features. There are two types of measures to score features: wrapper and filter. Wrapper methods use a specific classifier with a cross-validation method to provide a score, or a classification rate, for each feature subset.
Although wrapper methods typically provide the best performing feature set for a specific classifier since the characteristics of the selected features match well with the characteristics of the classifier , there are no guarantees that this feature subset will perform best for other classifiers. Moreover, the computational cost of wrapper methods is higher than filter methods and to perform wrapper methods for big data, extensive computational time to search the best feature subset is necessary, thus parallelized implementations of cross-validation may be ideal.
Measures applied in this field include the effect size i. Although mutual information has not been applied in this field yet [ 29 ], this measure offers potential value when initial features consist of both categorical data e.
While filter methods generally provide lower prediction performance than wrapper methods, the selected feature subset is more generalisable and thus more useful for understanding the associations between features. Filter methods can also be used as a pre-processing step [ 13 ] for feature extraction, allowing for more stable results when the dimensionality of initial input is high. Filter methods are also less computationally expensive and often easier to implement than multi-level wrapper functions.
The simplest and most popular search approach is to apply an objective function, such as the filter method, to each feature individually to determine the relevance of the feature related to the target class or the classification variable , and then select the top-ranked or the best features according to these outcome scores. This approach is typically called a univariate feature selection or maximum-relevance MR selection method.
Improvements in classification and interpretation have been observed consistently among previous studies that have applied this approach [ 2 , 3 , 24 ]. The top-ranked features, however, could be correlated among themselves and have different robustness ability.
Therefore, features selected according to their discriminative powers do not guarantee a better feature set. Thus, one solution is to include minimum redundancy criteria i. Another popular search approach in the data science field is sequential feature selection SFS algorithms.
This algorithm has achieved good classification performance to select a subset of discrete variables in several investigations. For example, Fukuchi et al. This algorithm consistently selected the knee flexion excursion angle a decrease in the angle among older runners , which has been usually reported in the literature using classical inferential statistics [ 30 , 31 ]. The results can be used to better understand the greater incidence of injuries among older walkers and runners which might be due to age-related changes in gait patterns [ 32 ].
In addition, Watari et al. The findings of both studies support the use of feature selection of baseline kinematic variables to predict the treatment outcome for patients with PFP and knee OA, which is a significant step towards a method to aid clinicians by providing evidence-informed decisions regarding optimal treatment strategies for patients. Unfortunately, the SFS and related algorithms use an incremental greedy strategy for feature selection and tend to become trapped in local minima, particularly when dimensionality is very high.
To deal with higher-dimensional data in future studies, algorithms need to incorporate randomness into their search procedure to escape local minima. Some potential and well-known population-based metaheuristic methods are genetic algorithm GA [ 34 ], ant colony optimization [ 35 ], particle swarm optimization [ 36 ], and harmony search [ 37 ]. These search techniques have also been developed to work in parallel computing and can be used for big data analytics such as parallel computing version of GA [ 38 ].
Finally, feature selection algorithms can be also embedded within learning algorithms such as decision tree and regularized trees [ 39 ]. Although embedded methods can reduce the cost of exploring larger search spaces, they could still yield poor generalization performance like the wrapper methods.
For example, Eskofier et al.
Biomechanics and analysis of running gait.
Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction feature selection and extraction , and learning algorithms classification and clustering. Biomechanical gait analysis is commonly used to analyse sport performance and evaluate pathologic gait. Significant advances in motion capture equipment, research methodologies, and data analysis techniques have enabled a plethora of studies that have advanced our understanding of gait biomechanics.
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Biomechanics and analysis of running gait
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