Spartan Daily, December 2, 1980

Volume 75, Issue 63https://scholarworks.sjsu.edu/spartandaily/6698/thumbnail.jp

Spartan Daily, January 26, 1981

Volume 76, Issue 2https://scholarworks.sjsu.edu/spartandaily/6706/thumbnail.jp

Towards Automatic Modelling of Volleyball Players' Behavior for Analysis, Feedback and Hybrid Training

Automatic tagging of video recordings of sports matches and training sessions can be helpful to coaches and players and provide access to structured data at a scale that would be unfeasible if one were to rely on manual tagging. Recognition of different actions forms an essential part of sports video tagging. In this paper, the authors employ machine learning techniques to automatically recognize specific types of volleyball actions (i.e., underhand serve, overhead pass, serve, forearm pass, one hand pass, smash, and block which are manually annotated) during matches and training sessions (uncontrolled, in the wild data) based on motion data captured by inertial measurement unit sensors strapped on the wrists of eight female volleyball players. Analysis of the results suggests that all sensors in the inertial measurement unit (i.e., magnetometer, accelerometer, barometer, and gyroscope) contribute unique information in the classification of volleyball actions types. The authors demonstrate that while the accelerometer feature set provides better results than other sensors, overall (i.e., gyroscope, magnetometer, and barometer) feature fusion of the accelerometer, magnetometer, and gyroscope provides the bests results (unweighted average recall = 67.87%, unweighted average precision = 68.68%, and κ = .727), well above the chance level of 14.28%. Interestingly, it is also demonstrated that the dominant hand (unweighted average recall = 61.45%, unweighted average precision = 65.41%, and κ = .652) provides better results than the nondominant (unweighted average recall = 45.56%, unweighted average precision = 55.45, and κ = .553) hand. Apart from machine learning models, this paper also discusses a modular architecture for a system to automatically supplement video recording by detecting events of interests in volleyball matches and training sessions and to provide tailored and interactive multimodal feedback by utilizing an HTML5/JavaScript application. A proof of concept prototype developed based on this architecture is also described

Spartan Daily, October 12, 1990

Volume 95, Issue 31https://scholarworks.sjsu.edu/spartandaily/8030/thumbnail.jp

Vol. 25 no. 2 Semester 2 (2013)

https://researchonline.nd.edu.au/in_principio2010s/1009/thumbnail.jp

UNLV Lady Rebels 1979-1980

Athletic Staff Former Lady Rebel All Americas Lady Rebels in the Pros Lady Rebel Profiles Last Year\u27s Results and Statistics Las Vegas Profile National Statistics and Rankings NIKE Tournament of Champions Opponents Past Records Schedule Season Outlook Team Picture Team Roster UNLV Facts UNLV Profil

Spartan Daily October 21, 2009

Volume 133, Issue 27https://scholarworks.sjsu.edu/spartandaily/1295/thumbnail.jp

Spartan Daily, November 21, 1986

Volume 87, Issue 59https://scholarworks.sjsu.edu/spartandaily/7516/thumbnail.jp