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

Origin of reversible and irreversible atomic-scale rearrangements in a model two-dimensional network glass

In this contribution, we investigate the fundamental mechanism of plasticity in a model two-dimensional network glass. The glass is generated by using a Monte Carlo bond-switching algorithm and subjected to athermal simple shear deformation, followed by subsequent unloading at selected deformation states. This enables us to investigate the topological origin of reversible and irreversible atomic-scale rearrangements. It is shown that some events that are triggered during loading recover during unloading, while some do not. Thus, two kinds of elementary plastic events are observed, which can be linked to the network topology of the model glass

A molecular dynamics study of the interface temperature in ultrasonic metal welding

In this study, mechanical and thermal behavior of the mating interface during ultrasonic metal welding is investigated using molecular dynamics (MD) simulations. In ultrasonic welding process, the reciprocating motion of the sonotrode together with the application of the external pressure on the mating parts are the sources of friction heat generation, high temperature gradient at the interface and plastic deformations. The rapid process of ultrasonic welding, which takes a few seconds at the longest, involves coupled mechanical and thermal processes. Therefore, MD simulations have been employed to elucidate the nano-mechanics of this complex coupled process within the picosecond timescale. To this end, the atomic scale simulations of the microstructure at and in the vicinity of the mating interface have been carried out. This contribution addresses the interactive effects of the process parameters on the interface temperature evolution and the diffusion behavior of the interface atoms at the atomic scale. The results of this work are compared to the results from macro scale investigations

Universality in the fracture of silica glass

The presence of universality of avalanches characterizing the inelastic response of disordered materials has the potential to bridge the gap from micro- to macroscale. In this study, we explore the statistics and the scaling behavior of avalanches in the fracture of silica glass on the microscale using molecular mechanics. We introduce a robust method for capturing and quantifying the avalanches, allowing us to perform rigorous statistical analysis, revealing universal power laws associated with critical phenomena. The computed exponents suggest that nanoscale fracture of silica belongs to the same universality class as depinning models. Additionally, the influence of an initial crack is explored, observing deviations from mean-field predictions while maintaining criticality. Furthermore, we investigate the strain-dependent probability density function (PDF), its cutoff function, and the interrelation between the critical exponents. Finally, we unveil distinct scaling behavior for small and large avalanches of the crack growth, shedding light on the underlying fracture mechanisms in silica glass.Comment: 11 Pages with 8 Figure

Internal resonances in nonlinear vibrations of a continuous rod with microstructure.

Nonlinear longitudinal vibrations of a periodically heterogeneous rod are considered. Geometrical nonlinearity is described by the Cauchy–Green strain tensor. Physical nonlinearity is modelled expressing the energy of deformation as a series expansion in powers of the strains. The governing macroscopic dynamical equation is obtained by the higher-order asymptotic homogenization method. An asymptotic solution is developed by the method of multiple time scales. The effects of internal resonances and modes coupling are predicted. The specific objective of the paper is to analyse how the presence of the microstructure influences on the processes of mode interactions. It is shown that depending on a scaling relation between the amplitude of the vibrations and the size of the unit cell different scenarios of the modes coupling can be realised

On the vibrations of a composite structure with hexagonal structure of a circular inclusions.

One of the major advantages of homogenization is a possibility of the generalization of the obtained results. Namely, if a solution to the local problem is found, then without principal problems one may solve not only the analyzed problem, by also a series of related static and dynamic problems, including: linear, quasilinear, the eigenvalue problems, etc. The mentioned approach has been applied to the eigenvalue problems regarding the perforated structures and periodically nonhomogenous 2D constructions with a square mesh of inclusions. In this work we have used theory of averaging to solve the vibrations problem regarding stiffly clamped rectangular membrane with periodically located circular inclusions creating a hexagonal mesh. The relations governing eigenvalues (frequencies) and eigenfunctions have been derived. The derivation of analytical formulas governing membrane eigenforms and frequencies consists of three parts. In the first part the local problem regarding a cell (inclusion) of the composite is studied. Second part is focused on finding main terms of the averaged problem. The third part is aimed at an estimation of the first improvement term with respect to the membrane fundamental frequency

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