2,901 research outputs found
Classification of sporting activities using smartphone accelerometers
In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in todayās society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging
sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach
Multi-scale statistics of turbulence motorized by active matter
A number of micro-scale biological flows are characterized by spatio-temporal
chaos. These include dense suspensions of swimming bacteria, microtubule
bundles driven by motor proteins, and dividing and migrating confluent layers
of cells. A characteristic common to all of these systems is that they are
laden with active matter, which transforms free energy in the fluid into
kinetic energy. Because of collective effects, the active matter induces
multi-scale flow motions that bear strong visual resemblance to turbulence. In
this study, multi-scale statistical tools are employed to analyze direct
numerical simulations (DNS) of periodic two- (2D) and three-dimensional (3D)
active flows and compare them to classic turbulent flows. Statistical
descriptions of the flows and their variations with activity levels are
provided in physical and spectral spaces. A scale-dependent intermittency
analysis is performed using wavelets. The results demonstrate fundamental
differences between active and high-Reynolds number turbulence; for instance,
the intermittency is smaller and less energetic in active flows, and the work
of the active stress is spectrally exerted near the integral scales and
dissipated mostly locally by viscosity, with convection playing a minor role in
momentum transport across scales.Comment: Accepted in Journal of Fluid Mechanics (2017
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