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

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

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

Разработка конструкции 3-х брусной косилки в условиях СПК «Береговой» Кемеровского района, Кемеровской области

Объектом исследования является конструкция навесной 3-х брусной косилки Цель работы – повышение эффективности заготовки грубых кормов, с разработкой конструкции навесной 3-х брусной косилки. В процессе исследования проводились технологические и конструкторские расчеты В результате исследования предложены дополнительные мероприятия по оптимизации процесса уборки грубых кормов, а также конструкторские решения по повышению эффективности работы косилочных агрегатов.The object of the study is to design a hinged 3-x bronnoy mowers Purpose – improving the efficiency of harvesting forage, with the development of design wall 3 bronnoy mowers. In the process of research was conducted technological and design calculations The study proposed additional measures for optimization of the process of harvesting roughage, and design solutions to improve the efficiency of the mower units

Ground state cooling in a bad cavity

We study the mechanical effects of light on an atom trapped in a harmonic potential when an atomic dipole transition is driven by a laser and it is strongly coupled to a mode of an optical resonator. We investigate the cooling dynamics in the bad cavity limit, focussing on the case in which the effective transition linewidth is smaller than the trap frequency, hence when sideband cooling could be implemented. We show that quantum correlations between the mechanical actions of laser and cavity field can lead to an enhancement of the cooling efficiency with respect to sideband cooling. Such interference effects are found when the resonator losses prevail over spontaneous decay and over the rates of the coherent processes characterizing the dynamics.Comment: 6 pages, 5 figures; J. Mod. Opt. (2007