Unsteady flow phenomena in human undulatory swimming: a numerical approach

The undulatory underwater sequence is one of the most important phases in competitive swimming. An understanding of the recurrent vortex dynamics around the human body and their generation could therefore be used to improve swimming techniques. In order to produce a dynamic model, we applied human joint kinematics to three-dimensional (3D) body scans of a female swimmer. The flow around this dynamic model was then calculated using computational fluid dynamics with the aid of moving 3D meshes. Evaluation of the numerical results delivered by the various motion cycles identified characteristic vortex structures for each of the cycles, which exhibited increasing intensity and drag influence. At maximum thrust, drag forces appear to be 12 times higher than those of a passive gliding swimmer. As far as we know, this is the first disclosure of vortex rings merging into vortex tubes in the wake after vortex recapturing. All unsteady structures were visualized using a modified Q-criterion also incorporated into our methods. At the very least, our approach is likely to be suited to further studies examining swimmers engaging in undulatory swimming during training or competition

Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

PubMed ID: 2313976

An Efficient Coding Hypothesis Links Sparsity and Selectivity of Neural Responses

To what extent are sensory responses in the brain compatible with first-order principles? The efficient coding hypothesis projects that neurons use as few spikes as possible to faithfully represent natural stimuli. However, many sparsely firing neurons in higher brain areas seem to violate this hypothesis in that they respond more to familiar stimuli than to nonfamiliar stimuli. We reconcile this discrepancy by showing that efficient sensory responses give rise to stimulus selectivity that depends on the stimulus-independent firing threshold and the balance between excitatory and inhibitory inputs. We construct a cost function that enforces minimal firing rates in model neurons by linearly punishing suprathreshold synaptic currents. By contrast, subthreshold currents are punished quadratically, which allows us to optimally reconstruct sensory inputs from elicited responses. We train synaptic currents on many renditions of a particular bird's own song (BOS) and few renditions of conspecific birds' songs (CONs). During training, model neurons develop a response selectivity with complex dependence on the firing threshold. At low thresholds, they fire densely and prefer CON and the reverse BOS (REV) over BOS. However, at high thresholds or when hyperpolarized, they fire sparsely and prefer BOS over REV and over CON. Based on this selectivity reversal, our model suggests that preference for a highly familiar stimulus corresponds to a high-threshold or strong-inhibition regime of an efficient coding strategy. Our findings apply to songbird mirror neurons, and in general, they suggest that the brain may be endowed with simple mechanisms to rapidly change selectivity of neural responses to focus sensory processing on either familiar or nonfamiliar stimuli. In summary, we find support for the efficient coding hypothesis and provide new insights into the interplay between the sparsity and selectivity of neural responses

Goal shot analysis in elite water polo : World Cup final 2018 in Berlin

The subject of this game analysis was the throwing behavior of the world class players under competition match conditions during the final “World Cup Water Polo” tournament 2018 in Berlin. Specifically, we analyzed (a) the situational dependence of concrete environmental conditions (“constraints”) of successful throwing actions as well as (b) the goal throw biomechanics concerning throwing speed. Therefore, player’s and ball positions were recorded by video cameras as well as high-speed cameras. Based on the positions and trajectories parameters such as goal throw speed, Voronoi-cells as well as team centroids were calculated. The area of the Voronoi-cells differs concerning defending and attacking teams as well as between successful and non-successful teams and can be used as an indicator for goal or no goal. Under semi-collective tactical aspects, the comparison of the offensively and defensively controlled field areas (collective space control) between successful and unsuccessful goal throws shows that in the case of successful attacking completions, the attacking team (in the sum of its six players) each controlled significantly more field area in front of the opponent’s goal than the six defensive players together with their goalkeeper. In the case of unsuccessful attacking attempts, this area ratio was exactly reversed, i.e., the defensive team dominated the space.Publikationsfonds ML

Goal Shot Analysis in Elite Water Polo—World Cup Final 2018 in Berlin

The subject of this game analysis was the throwing behavior of the world class players under competition match conditions during the final “World Cup Water Polo” tournament 2018 in Berlin. Specifically, we analyzed (a) the situational dependence of concrete environmental conditions (“constraints”) of successful throwing actions as well as (b) the goal throw biomechanics concerning throwing speed. Therefore, player’s and ball positions were recorded by video cameras as well as high-speed cameras. Based on the positions and trajectories parameters such as goal throw speed, Voronoi-cells as well as team centroids were calculated. The area of the Voronoi-cells differs concerning defending and attacking teams as well as between successful and non-successful teams and can be used as an indicator for goal or no goal. Under semi-collective tactical aspects, the comparison of the offensively and defensively controlled field areas (collective space control) between successful and unsuccessful goal throws shows that in the case of successful attacking completions, the attacking team (in the sum of its six players) each controlled significantly more field area in front of the opponent’s goal than the six defensive players together with their goalkeeper. In the case of unsuccessful attacking attempts, this area ratio was exactly reversed, i.e., the defensive team dominated the space

Goal Shot Analysis in Elite Water Polo—World Cup Final 2018 in Berlin

The subject of this game analysis was the throwing behavior of the world class players under competition match conditions during the final “World Cup Water Polo” tournament 2018 in Berlin. Specifically, we analyzed (a) the situational dependence of concrete environmental conditions (“constraints”) of successful throwing actions as well as (b) the goal throw biomechanics concerning throwing speed. Therefore, player’s and ball positions were recorded by video cameras as well as high-speed cameras. Based on the positions and trajectories parameters such as goal throw speed, Voronoi-cells as well as team centroids were calculated. The area of the Voronoi-cells differs concerning defending and attacking teams as well as between successful and non-successful teams and can be used as an indicator for goal or no goal. Under semi-collective tactical aspects, the comparison of the offensively and defensively controlled field areas (collective space control) between successful and unsuccessful goal throws shows that in the case of successful attacking completions, the attacking team (in the sum of its six players) each controlled significantly more field area in front of the opponent’s goal than the six defensive players together with their goalkeeper. In the case of unsuccessful attacking attempts, this area ratio was exactly reversed, i.e., the defensive team dominated the space

Variables included in the calculation of the virtual pivot point (VPP).

All experimental conditions are shown. Values are mean of all trials and subsequent mean of all participants (N = 11). For shoe walking, mean±s.d. (gray area) is shown. The non-transparent trajectory represents the single support phase, for which the VPP was calculated. A: Horizontal (x) ground reaction forces (GRFs), B: vertical (z) GRFs proportional to body weight (BW). C: Horizontal center of mass (CoM) position normalized to zero at touchdown, D: vertical CoM position. E: Horizontal, CoM-related center of pressure (CoP) position shown for 5% to 95% of contact time due to noisy CoP at the edges. F: Horizontal CoP position normalized to zero at 5% of the contact time, shown for 5% to 95% of contact time.</p

Experimental conditions and exemplary plots of the virtual pivot point (VPP) of one representative participant.

Above: Walking A: barefoot, B: with shoes, C: backwards, D: in handstand, E: with planks, and F: with stilts. The placement of the reflective joint markers of one body side is indicated by the red circles in C. Photo credit: Sandro Schwarzentrub. Below: Colored lines show the ground reaction forces (GRFs) at different measurement times originating at the center of pressure in a coordinate system centered on the center of mass. The GRFs are illustrated from touchdown (black/gray line) to take-off. Red circles with black borders indicate the calculated VPP. For each condition, the first trial is shown.</p

Angular momentum and ankle torque.

All experimental conditions are shown. Values are mean of all trials and subsequent mean of concluded participants. For shoe walking, mean±s.d. (gray area) is shown. The non-transparent trajectory represents the single support phase, for which the VPP was calculated. A: The angular momentum was normalized to body mass (M), mean walking velocity of each condition (V), and mean center of mass height (H). Negative values indicate clockwise rotation (N = 11). B: Ankle torque (for handstand walking wrist torque) normalized to body mass is shown (handstand: N = 9, else: N = 11).</p

Median±’median absolute deviation’ of the virtual pivot point (VPP) variables between participants for each experimental condition.

A: Horizontal (x) and B: vertical (z) position of the VPP, each small dot represents the mean over all trials of one condition for one participant. Only trials with R2>0.6 were considered. R2 represents the spread around the VPP. C: All R2 values are shown (N = 11), D: only participants with R2>0.6 are included (handstand: N = 10, else: N = 11). Each small dot represents one participant.</p