VCoach: A Customizable Visualization and Analysis System for Video-based Running Coaching

Videos are accessible media for analyzing sports postures and providing feedback to athletes. Existing video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers either manually by a coach or automatically by computing key parameters from poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. Besides, existing sport-specific systems with embedded bespoke pose attributes can hardly generalize to new attributes; directly overlaying two poses might not clearly visualize the key differences that viewers would like to pursue. To address these issues, we analyze and visualize human pose data with customizable viewpoints and attributes in the context of common biomechanics of running poses, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, VCoach, to provide feedback on running poses for amateurs. VCoach provides automatic low-level comparisons of the running poses between a novice and an expert, and visualizes the pose differences as part-based 3D animations on a human model. Meanwhile, it retains the users' controllability and customizability in high-level functionalities, such as navigating the viewpoint for previewing feedback and defining their own pose attributes through our interface. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system

TRAINING CHARACTERISTICS AND POWER PROFILES OF USA CYCLING ROAD CYCLISTS

New advancements in bicycle instrumentation and social media applications have made it possible to obtain quantitative data on training and racing. PURPOSE: To analyze training data (training volume, frequency, distance, speed, and race days) and power profiles of road racers in USA Cycling, in order to compare genders and categories (professional, 1, 2, 3, 4, 5). METHODS: Part 1: Using USAC race results, racers with an active Strava® account were selected. Using data uploaded from on-bike GPS head units, 543 USAC racers’ (279 men, 264 women), 2019 data were documented. Part 2: Subjects with power meter data displayed on Strava® were contacted for demographic information and peak power data (5-s, 1-min, 5-min, 20-min, and 1-h). 92 amateur racers (67 men, 25 women) completed this part of the study. Annual training metrics, power data, and survey results were compared across the categories and genders using ANOVAs. RESULTS: Part 1: In 2019, professional women (N=20) rode 634.7±135.2 hours, 16,581±3,562 km, and completed 304.4±28.5 ride days, 33.2±7.8 races; professional men (N=29) rode 864.7±160.0 hours, 26,103±5,210 km, and completed 310.6±39.3 ride days, 49.9±17.1 races. There were significant gender differences among professionals, for all variables except for ride days (ppppCONCLUSION:Some differences exist for annual training data and power profiles between USAC categories and genders. Knowledge of the training characteristics and power profiles of USAC men and women athletes could be useful to road racers and coaches in designing training programs

The Cowl - v.80 - n.7 - Oct 29, 2015

The Cowl - student newspaper of Providence College. Volume 80 - No. 7 - October 29, 2015. 24 pages

Inform to Perform: Using Domain Analysis to Explore Amateur Athlete Information Resources and Behaviour

Sporting information has been relatively unexamined in library and information science (LIS) literature with most research concentrating on collection management or archival functions. User studies in LIS have covered some aspects of outdoor recreation and hobbies, but only one study has been found explicitly researching amateur athletes. This project builds contributes a definition of sport as an information domain and an exploratory user study of amateur athletes. The research takes a socio-cognitive approach and uses domain analysis linked to serious leisure, information communication chain and information behaviour theories to provide the research context. These foundational theories are used to define sport as an information domain more formally, noting both degrees of specialisation within it and intersections with related disciplines. Four domain analysis approaches are then used to illustrate the potential of the approach for researching different dimensions within the domain. Three of these approaches involve desk research into different aspects of amateur sport information. By discussing the role of documents, computer science and discourses in sport these approaches show that sport is a multi-faceted and interdisciplinary domain with many topics of interest for the information researcher and practitioner. The fourth approach is a user study of athlete information behaviour that collected data on information sources, tasks and attitudes via an online questionnaire