ARTIFICIAL INTELLIGENCE, DATA ANALYTICS AND SPORTS BIOMECHANICS: A NEW ERA OR A FALSE DAWN?

The adoption of wearable technologies combined with emerging and evolving data science techniques has the potential to do for sports biomechanists what the industrial revolution did for manufacturing. Yet the full capability of these technologies and methods will likely only be realised if sport scientists are embedded in their application and driving the questions being asked

NO DATASET TOO SMALL! ANIMATING 3D MOTION DATA TO ENLARGE 2D VIDEO DATABASES

This study outlines a technique to leverage the wide availability of high resolution three-dimensional (3D) motion capture data for the purpose of synthesising two-dimensional (2D) video camera views, thereby increasing the availability of 2D video image databases for training machine learning models requiring large datasets. We register 3D marker trajectories to generic 3D body-shapes (hulls) and use a 2D pose estimation algorithm to predict joint centre and anatomical landmark keypoints in the synthesised 2D video views – a novel approach that addresses the limited data available in elite sport settings. We use 3D long jump data as an exemplar use case and investigate the influence of; 1) varying anthropometrics, and 2) the 2D camera view, on keypoint estimation accuracy. The results indicated that 2D keypoint determination accuracy is affected by body-shape. Frontal plane camera views result in lower accuracy than sagittal plane camera views

PREDICTING GROUND REACTION FORCES FROM 2D VIDEO: BRIDGING THE LAB TO FIELD NEXUS

The accurate measurement of ground reaction forces (GRFs) is confined to laboratory-based settings. This poses an ongoing problem for sports biomechanists working in the field given the necessity of this variable for input into the commonly employed inverse dynamics modelling approach. In addition, GRFs are also used by the wider sport science community for assessing player “workload” metrics. Non-invasive in-field estimation of GRFs could facilitate the development of an in-field tool which has the potential to revolutionise the side-line management of players from a number of perspectives. The aim of this study was to use a least squares estimator (LSE) matrix to estimate GRFs from 2D video with r\u3e0.8 results found for the vertical and horizontal GRF components. These results provide early support for the efficacy of non-invasive estimation of GRFs in the field determined from 2D video footage in isolation

THE INFLUENCE OF FILTER PARAMETERS ON THE PREDICTION ACCURACY OF THE GROUND REACTION FORCE AND JOINT MOMENTS

Athletes’ movement biomechanics are of high interest to predict injury risk, especially in maximum effort cutting manoeuvres. However, using a standard optical measurement set-up with cameras and force plates influences the athlete’s performance. Therefore, alternative methods, e.g. Neural Networks, have been used to predict kinetic parameters based on easier to measure kinematic parameters. A previous study has evoked the question, whether the filtering processes of the input and output parameters used for training a feedforward neural network affect the prediction accuracy. To answer this question, four different filter combinations have been used during the pre-processing of joint angles, ground reaction force and joint moments of fast cutting manoeuvres, which were used to train a feedforward neural network. The results revealed a dependency

FEATURE SELECTION FOR THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN MOTION ANALYSIS

The application of IMUs and artificial neural networks have shown their potential in estimating joint moments in various motion tasks. In this study, IMU data collected with five sensors during gait was used as input data to estimate hip, knee and ankle joint moments using artificial neural networks. Additionally, the original 30 features of the sensors’ data were reduced to their ten most relevant principal components and also used as input to the neural networks to evaluate the influence of feature selection. The prediction accuracy of the networks was lower for the reduced dataset. Research with a larger dataset needs to be undertaken to further understand the influence of a reduced number of features on the prediction accuracy

CREATING VIRTUAL FORCE PLATFORMS FOR CUTTING MANEUVERS FROM KINEMATIC DATA BASED ON LSTM NEURAL NETWORKS

The precise measurement of ground reaction forces and moments (GRF/M) usually requires stationary equipment and is, therefore, only partly feasible for field measurements. In this work we propose a method to derive GRF/M time series from motion capture marker trajectories for cutting maneuvers (CM) using a long short-term memory (LSTM) neural network. We used a dataset containing 637 CM motion files from 70 participants and trained two-layer LSTM neural networks to predict the GRF/M signals of two force platforms. A five-fold cross-validation resulted in correlation coefficients ranging from 0.870 to 0.977 and normalized root mean square errors from 3.51 to 9.99% between predicted and measured GRF/M. In future, this method can be used not only to simplify lab measurements but also to allow for determining biomechanical parameters during real-world situations

Strategies to optimise machine learning classification performance when using biomechanical features

Building prediction models using biomechanical features is challenging because such models may require large sample sizes. However, collecting biomechanical data on large sample sizes is logistically very challenging. This study aims to investigate if modern machine learning algorithms can help overcome the issue of limited sample sizes on developing prediction models. This was a secondary data analysis two biomechanical datasets – a walking dataset on 2295 participants, and a countermovement jump dataset on 31 participants. The input features were the three-dimensional ground reaction forces (GRFs) of the lower limbs. The outcome was the orthopaedic disease category (healthy, calcaneus, ankle, knee, hip) in the walking dataset, and healthy vs people with patellofemoral pain syndrome in the jump dataset. Different algorithms were compared: multinomial/LASSO regression, XGBoost, various deep learning time-series algorithms with augmented data, and with transfer learning. For the outcome of weighted multiclass area under the receiver operating curve (AUC) in the walking dataset, the three models with the best performance were InceptionTime with x12 augmented data (0.810), XGBoost (0.804), and multinomial logistic regression (0.800). For the jump dataset, the top three models with the highest AUC were the LASSO (1.00), InceptionTime with x8 augmentation (0.750), and transfer learning (0.653). Machine-learning based strategies for managing the challenging issue of limited sample size for biomechanical ML-based problems, could benefit the development of alternative prediction models in healthcare, especially when time-series data are involved