Assessing inter- and intra-rater reliability of movement scores and the effects of body-shape using a custom visualisation tool: an exploratory study.

Background: The literature shows conflicting results regarding inter- and intra-rater reliability, even for the same movement screen. The purpose of this study was to assess inter- and intra-rater reliability of movement scores within and between sessions of expert assessors and the effects of body-shape on reliability during a movement screen using a custom online visualisation software.

Methods: Kinematic data from 542 athletes performing seven movement tasks were used to create animations (i.

Comparison of machine learning classifiers for differentiating level and sport using movement data.

The purposes of this study were to determine if 1) recurrent neural networks designed for multivariate, time-series analyses outperform traditional linear and non-linear machine learning classifiers when classifying athletes based on competition level and sport played, and 2) athletes of different sports move differently during non-sport-specific movement screens. Optical-based kinematic data from 542 athletes were used as input data for nine different machine learning algorithms to classify athletes based on competition level and sport played. For the traditional machine learning classifiers, principal component analysis and feature selection were used to reduce the data dimensionality and to determine the best principal components to retain.

A data-driven framework for assessing soldier performance, health, and survivability.

Presented is a framework that uses pattern classification methods to incrementally morph whole-body movement patterns to investigate how personal (sex, military experience, and body mass) and load characteristics affect the survivability tradespace: performance, musculoskeletal health, and susceptibility to enemy action. Sixteen civilians and 12 soldiers performed eight military-based movement patterns under three body-borne loads: ∼5.5 kg, ∼22 kg, and ∼38 kg.

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review.

To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al.

Machine Learning and Deep Neural Network Architectures for 3D Motion Capture Datasets.

Biomechanical movement data are highly correlated multivariate time-series for which a variety of machine learning and deep neural network classification techniques are possible. For image classification, convolutional neural networks have reshaped the field, but have been challenging to apply to 3D movement data with its intrinsic multidimensional nonlinear correlations. Deep neural networks afford the opportunity to reduce feature engineering effort, remove model-based approximations that can introduce systematic errors, and reduce the manual data processing burden which is often a bottleneck in biomechanical data acquisition.

Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data.

Movement screens are frequently used to identify differences in movement patterns such as pathological abnormalities or skill related differences in sport; however, abnormalities are often visually detected by a human assessor resulting in poor reliability. Therefore, our previous research has focused on the development of an objective movement assessment tool to classify elite and novice athletes' kinematic data using machine learning algorithms. Classifying elite and novice athletes can be beneficial to objectively detect differences in movement patterns between the athletes, which can then be used to provide higher quality feedback to athletes and their coaches.

Validation of an IMU Suit for Military-Based Tasks.

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE).

Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks.

Movement screens are used to assess the overall movement quality of an athlete. However, these rely on visual observation of a series of movements and subjective scoring. Data-driven methods to provide objective scoring of these movements are being developed.

Considering movement competency within physical employment standards.

Background: Physical employment standards (PES) ensure that candidates can demonstrate the physical capacity required to perform duties of work. However, movement competency, or an individual's movement strategy, can relate to injury risk and safety, and therefore should be considered in PES.

Objective: Demonstrate the utility of using artificial intelligence (AI) to detect risk-potential of different movement strategies within PES.

Objectively Differentiating Movement Patterns between Elite and Novice Athletes.

Introduction: Movement screens are frequently used to identify abnormal movement patterns that may increase risk of injury or hinder performance. Abnormal patterns are often detected visually based on the observations of a coach or clinician. Quantitative or data-driven methods can increase objectivity, remove issues related to interrater reliability and offer the potential to detect new and important features that may not be observable by the human eye.

Pain catastrophizing moderates changes in spinal control in response to noxiously induced low back pain.

It is generally accepted that spine control and stability are relevant for the prevention and rehabilitation of low back pain (LBP). However, there are conflicting results in the literature in regards to how these variables are modified in the presence of LBP. The aims of the present work were twofold: (1) to use noxious stimulation to induce LBP in healthy individuals to assess the direct effects of pain on control (quantified by the time-dependent behavior of kinematic variance), and (2) to assess whether the relationship between pain and control is moderated by psychological features (i.

The Effects of Experimentally Induced Low Back Pain on Spine Rotational Stiffness and Local Dynamic Stability.

Local dynamic stability, quantified using the maximum finite-time Lyapunov exponent (λ max), and the muscular contributions to spine rotational stiffness can provide pertinent information regarding the neuromuscular control of the spine during movement tasks. The primary goal of the present study was to assess if experimental capsaicin-induced low back pain (LBP) affects spine stability and the neuromuscular control of repetitive trunk movements in a group of healthy participants with no history of LBP. Fourteen healthy males were recruited for this investigation.

Comparing the local dynamic stability of trunk movements between varsity athletes with and without non-specific low back pain.

The local dynamic stability of trunk movements, quantified using the maximum Lyapunov exponent (λmax), can provide important information on the neuromuscular control of spine stability during movement tasks. Although previous research has displayed the promise of this technique, all studies were completed with healthy participants. Therefore the goal of this study was to compare the dynamic stability of spine kinematics and trunk muscle activations, as well as antagonistic muscle co-contraction, between athletes with and without low back pain (LBP).