IMU-BASED ACTIVITY RECOGNITION OF THE BASKETBALL JUMP SHOT

The skill and performance of athletes is more and more represented by numbers. Technical devices are utilized to assist and monitor practices and games. In this regard, the objective of this study was to develop an IMU-based algorithm to recognize jump shots in arbitrary basketball motion sequences. For the extraction, a convolutional neural network was trained on the classification task. The leave-one-subject-out cross-validation of the network showed values of over 0.970 for recall and precision and an area under the curve of 0.995 for the receiver operating characteristic curve. The recognition algorithm represents the first step towards future motion analysis incorporated in a tool which may enable the individual player to self-evaluate their shooting mechanics and improve their shooting performance

SYNTHESISING 2D VIDEOS FROM 3D DATA: ENLARGING SPARSE 2D VIDEO DATASETS FOR MACHINE LEARNING APPLICATIONS

This study outlines a technique to repurpose widely available high resolution three-dimensional (3D) motion capture data for training a machine learning model to estimate the ground reaction forces from two-dimensional (2D) pose estimation keypoints. Keypoints describe anatomically related landmarks in 2D image coordinates. The landmarks can be calculated from 3D motion capture data and projected to different image planes, serving to synthesise a near-infinite number of 2D camera views. This highly efficient method of synthesising 2D camera views can be used to enlarge sparse 2D video databases of sporting movements. We show the feasibility of this approach using a sidestepping dataset and evaluate the optimal camera number and location required to estimate 3D ground reaction forces. The method presented and the additional insights gained from this approach can be used to optimise corporeal data capture by sports practitioners

A LOCAL APPROACH TO IDENTIFY THE IMPACT OF SUBJECT SPECIFIC MOVEMENT STRATEGIES ON THE LOCAL FORCES DURING CUTTING MANEUVERS

During multidirectional movements the body is not aligned with the global coordinate system (CS), complicating the interpretation of forces and moments. To overcome these issues, the global ground reaction force (GRF) was transformed into the CS of each segment and the orientation of the segments relative to the global CS were expressed in Euler angles (EA). Principle component analysis (PCA) was used to discriminate the wave forms of the local GRF and the EAs. The first three PC Eigenvectors of the EA and local GRF were correlated to determine the impact of the segment’s orientation on the GRF. An upright position of the shank and thigh segment increased the force acting medially on the knee. This potentially increases the risk of a varus movement, whereas a frontal tilt increased a laterally directed force that potentially stabilized the leg axis

PREDICTION OF JOINT KINETICS BASED ON JOINT KINEMATICS USING ARTIFICIAL NEURAL NETWORKS

The high cost and low portability of measurement systems as well as time-consuming inverse dynamic calculations are a major limitation to motion analysis. Therefore, this study investigates predictions of joint kinetics based on kinematic data using an artificial neural network (ANN) approach. For this purpose, 3D lower limb joint angles and moments of twelve healthy subjects were calculated using inverse dynamics. Kinematic and anthropometric data was used as input parameter to train, validate and test a long short-term memory recurrent ANN to predict joint moments. The ANN predicts joint moments for subjects whose motion patterns are known to the ANN accurately. Although the prediction accuracy for unknown subjects was lower, this study proved the capability of ANNs to predict joint moments based on kinematic and anthropometric data

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

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

JOINT ANGLE ESTIMATION DURING FAST CUTTING MANOEUVRES USING ARTIFICIAL NEURAL NETWORKS

Athletes’ movement biomechanics are of high interest to predict injury risk. However, using a standard optical measurement set-up with cameras and force plates influences the athlete’s performance. Alternative systems such as commercial IMU systems are still jeopardised by measurement discrepancies in the analysis of joint angles. Therefore, this study aims to estimate hip, knee and ankle joint angles from simulated IMU data during the execution and depart contact of a maximum effort 90° cutting manoeuvre using a feed-forward neural network. Simulated accelerations and angular rates of the feet, shanks, thighs and pelvis as input data. The correlation coefficient between the measured and predicted data indicates strong correlations. Hence, the proposed method can be used to predict motion kinematics during a fast change of direction

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 3D GROUND REACTION FORCE FROM 2D VIDEO VIA NEURAL NETWORKS IN SIDESTEPPING TASKS

Sports science practitioners often measure ground reaction forces (GRFs) to assess performance, rehabilitation and injury risk. However, recording of GRFs during dynamic tasks has historically been limited to lab settings. This work aims to use neural networks (NN) to predict three-dimensional (3D) GRF via pose estimation keypoints as inputs, determined from 2D video data. Two different NN were trained on a dataset containing 1474 samples from 14 participants and their prediction accuracy compared with ground truth force data. Results for both NN showed correlation coefficients ranging from 0.936 to 0.954 and normalised root mean square errors from 11.05% to 13.11% for anterior-posterior and vertical GRFs, with poorer results found in the medio-lateral direction. This study demonstrates the feasibility and utility of predicting GRFs from 2D video footage