Why apply AI to figure skating? Skaters can always keep pushing, higher, faster, stronger. OOFSkate is all about helping skaters figure out a way to rotate a little bit faster in their jumps or jump a little bit higher. The system helps skaters catch things that perhaps could pass an eye test, but that might allow them to target some high-value areas of opportunity. The artistic side of skating is much harder to evaluate than the technical elements because it's subjective.

Shooting at St. Louis Starbucks kills former Team USA synchronized figure skater, coach Gabrielle "Sam" Linehan, 28, during alleged armed robbery in Tower Grove East neighborhood Tuesday.

That move may have warded off the lawyers, but it didn't slide past Olympic viewers. Disgusted reactions ensued immediately after Mrázková and Mrázek took the ice and NBC's announcers pointed out that, while half of their choreography was set to AC/DC's "Thunderstruck," the other half had been soundtracked by A.I. The ISU's own documentation identifies the track as something called "One Two," created by an A.I. prompted to come up with something resembling "90s style Bon Jovi."

Fleming went on to win the gold medal. Breakthroughs, discoveries, and DIY tips sent six days a week. From figure skating to ice hockey, many of the most popular winter sports stem from a long history of people simply playing around on ice skates . Part of what makes a good skater so fun to watch is the juxtaposition of their clear technical skill and the seeming effortlessness with which they glide across the ice. They make it seem so natural.

-- Marker - free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in keypoint recognition and random fluctuation in keypoint trajectories when analyzing kinematic human poses. The performance of existing deep learning - based models for HPE refinement is considerably limited by inaccurate training datasets in which the keypoints are manually annotated. This paper proposed a novel method to overcome the difficulty through joint angle - based modeling. The key techniques include: (i) A joint angle - based model of human pose, which is robust to describe kinematic human poses; (ii) Approximating temporal variation of joint angles through high order Fourier series to get reliable "ground truth"; (iii) A bidirectional recurrent network is designed as a post - processing module to refine the estimation of well - established HRNet. Trained with the high - quality dataset constructed using our method, the network demonstrates outstanding performance to correct wrongly recognized joints and smooth their spatiotemporal trajectories. Tests show that joint angle - based refinement (JAR) outperforms the state - of - the - art HPE refinement network in challenging cases like figure skating and breaking. Index Terms -- Human pose estimation, k inematic pose, r efinement, j oint angle, F ourier series, r ecurrent neural network . INTRODUCTION omputer vision based human pose estimation (HPE) has been widely adopted as a powerful tool to determine the configuration of human body from images and videos. This technology has found increasing applications across various fields such as human - computer interaction, motion analysis, augmented or virtual reality, and healthcare [1] . The study was financially supported by the National Natural Science Foundation of China (Grant Nos.

Understanding human actions from videos is essential in many domains, including sports. In figure skating, technical judgments are performed by watching skaters' 3D movements, and its part of the judging procedure can be regarded as a Temporal Action Segmentation (TAS) task. TAS tasks in figure skating that automatically assign temporal semantics to video are actively researched. However, there is a lack of datasets and effective methods for TAS tasks requiring 3D pose data. In this study, we first created the FS-Jump3D dataset of complex and dynamic figure skating jumps using optical markerless motion capture. We also propose a new fine-grained figure skating jump TAS dataset annotation method with which TAS models can learn jump procedures. In the experimental results, we validated the usefulness of 3D pose features as input and the fine-grained dataset for the TAS model in figure skating. FS-Jump3D Dataset is available at https://github.com/ryota-skating/FS-Jump3D.

AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are uninterpretable and do not justify their scores, which is important for algorithmic accountability. Indeed, to account for their decisions, instead of scoring subjectively, sports judges use a consistent set of criteria - rubric - on multiple actions in each performance sequence. Therefore, we propose IRIS to perform Interpretable Rubric-Informed Segmentation on action sequences for AQA. We investigated IRIS for scoring videos of figure skating performance. IRIS predicts (1) action segments, (2) technical element score differences of each segment relative to base scores, (3) multiple program component scores, and (4) the summed final score. In a modeling study, we found that IRIS performs better than non-interpretable, state-of-the-art models. In a formative user study, practicing figure skaters agreed with the rubric-informed explanations, found them useful, and trusted AI judgments more. This work highlights the importance of using judgment rubrics to account for AI decisions.

I previous posted the write-up on the first part of my project (Github repo) to predict how skaters would rank in the figure skating world championship from earlier scores that they earned in the season. The main idea is to separate the skater effect, the intrinsic ability of each skater, from the event effect, the influence of an event on a skater's performance, so that a more accurate ranking could be built. Unfortunately, this model does not have a closed-form solution to learn the parameters as opposed to the earlier models. Therefore, gradient descent was used to learn them, which resulted in this neat little animation that tracks how the model residuals, RMSE, as well as predicted ranking gets better and better as gradient descent runs. I also explore different strategies to reduce model overfit (so that it can predict skater ranking more accurately), using familiar methods such as model penalization and early stopping. Lastly, note that this hybrid model is nothing but factorizing the event-skater score matrix into an event-specific vector and skater-specific vector, which can multiply together to approximate the score matrix.

Back in 2006 when I was competing to secure my place on the U.S. Olympic figure skating team, I often found my body working faster than my brain could think. I would spin at nearly 300 rotations per minute, feeling each of my 650-plus muscles engage. I remember wondering how I could overcome the inefficiencies of being human: pain, fear and disorientation. Apparently, I was asking the same question as the ancient Greeks, who envisioned thinking machines capable of out-performing the human brain. While the pursuit of machines with human-like intelligence is an ancient one, it wasn't until recently that artificial intelligence was actually possible.