Background: Collisions in rugby union and sevens have a high injury incidence and burden, and are also associated with player and team performance. Understanding the frequency and intensity of these collisions is therefore important for coaches and practitioners to adequately prepare players for competition. The aim of this review is to synthesise the current literature to provide a summary of the collision frequencies and intensities for rugby union and rugby sevens based on video-based analysis and microtechnology. Methods: A systematic search using key words was done on four different databases from 1 January 1990 to 1 September 2021 (PubMed, Scopus, SPORTDiscus and Web of Science). Results: Seventy-three studies were included in the final review, with fifty-eight studies focusing on rugby union, while fifteen studies explored rugby sevens. Of the included studies, four focused on training—three in rugby union and one in sevens, two focused on both training and match-play in rugby union and one in rugby sevens, while the remaining sixty-six studies explored collisions from match-play. The studies included, provincial, national, international, professional, experienced, novice and collegiate players. Most of the studies used video-based analysis (n = 37) to quantify collisions. In rugby union, on average a total of 22.0 (19.0–25.0) scrums, 116.2 (62.7–169.7) rucks, and 156.1 (121.2–191.0) tackles occur per match. In sevens, on average 1.8 (1.7–2.0) scrums, 4.8 (0–11.8) rucks and 14.1 (0–32.8) tackles occur per match. Conclusions: This review showed more studies quantified collisions in matches compared to training. To ensure athletes are adequately prepared for match collision loads, training should be prescribed to meet the match demands. Per minute, rugby sevens players perform more tackles and ball carries into contact than rugby union players and forwards experienced more impacts and tackles than backs. Forwards also perform more very heavy impacts and severe impacts than backs in rugby union. To improve the relationship between matches and training, integrating both video-based analysis and microtechnology is recommended. The frequency and intensity of collisions in training and matches may lead to adaptations for a “collision-fit” player and lend itself to general training principles such as periodisation for optimum collision adaptation. Trial Registration PROSPERO registration number: CRD42020191112.

Many agile legged animals employ lightweight, furry tails to regulate orientation during running, leaping, and turning. Most robots attempting the same tasks either lack a tail or employ one with high inertia, which can induce impractical payload and energy costs. Inspired by nature's solution to this tradeoff, we explore the use of aerodynamic drag tails in reorientation tasks. In this article, we present a model of the aerodynamic drag and from this derive a metric that allows for direct comparison between aerodynamic and inertial tails. Motivated by this model, we construct a tail to maximize this effectiveness while minimizing inertia. We demonstrate the utility of this tail for two dynamic behaviors executed on a quadrupedal robot. First, in aerial reorientation the robot achieves a 90 ^° rotation within one body length of fall at the same performance as an inertial tail but with just 37% of the normalized inertia. Second, the forward acceleration of the robot is improved by 12% despite increasing the system mass by 10% over a tailless version. These results show that aerodynamic drag can provide significant control authority for a robot while decreasing the payload and energy cost.

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.

During high-speed pursuit of prey, the cheetah (Acinonyx jubatus) has been observed to swing its tail while manoeuvring (e.g. turning or braking) but the effect of these complex motions is not well understood. This study demonstrates the potential of the cheetah9s long, furry tail to impart torques and forces on the body as a result of aerodynamic effects, in addition to the well-known inertial effects. The first-order aerodynamic forces on the tail are quantified through wind tunnel testing and it is observed that the fur nearly doubles the effective frontal area of the tail without much mass penalty. Simple dynamic models provide insight into manoeuvrability via simulation of pitch, roll and yaw tail motion primitives. The inertial and quasi-steady state aerodynamic effects of tail actuation are quantified and compared by calculating the angular impulse imparted onto the cheetah9s body and its shown aerodynamic effects contribute to the tail9s angular impulse, especially at the highest forward velocities.