Video lens

Postma

et al. 2020

MTI

Access to performance data during matches and training sessions is important for coaches and players. Although there are many video tagging systems available which can provide such access, these systems require manual effort. Data from Inertial Measurement Units (IMU) could be used for automatically tagging video recordings in terms of players’ actions. However, the data gathered during volleyball sessions are generally very imbalanced, since for an individual player most time intervals can be classified as “non-actions” rather than “actions”. This makes automatic annotation of video recordings of volleyball matches a challenging machine-learning problem. To address this problem, we evaluated balanced and imbalanced learning methods with our newly proposed ‘super-bagging’ method for volleyball action modelling. All methods are evaluated using six classifiers and four sensors (i.e., accelerometer, magnetometer, gyroscope and barometer). We demonstrate that imbalanced learning provides better unweighted average recall, (UAR = 83.99%) for the non-dominant hand using a naive Bayes classifier than balanced learning, while balanced learning provides better performance (UAR = 84.18%) for the dominant hand using a tree bagger classifier than imbalanced learning. Our super-bagging method provides the best UAR (84.19%). It is also noted that the super-bagging method provides better averaged UAR than balanced and imbalanced methods in 8 out of 10 cases, hence demonstrating the potential of the super-bagging method for IMU’s sensor data. One of the potential applications of these novel models is fatigue and stamina estimation e.g., by keeping track of how many actions a player is performing and when these are being performed.

Section: Introductionmentioning

confidence: 99%

A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors

Postma

et al. 2020

MTI

“…Automatically identifying actions in sport activities is important for multiple reasons, therefore there have been numerous studies to identify actions in sports [1,12,13,15]. Wearable devices such as Inertial Measurement Units (IMUs) [2,14] are becoming increasingly popular for sports related action analysis because of their reasonable price as well as portability [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Dominant and Non-Dominant Hand Movements For Volleyball Action Modelling

Adjunct of the 2019 International Conference on Multimodal Interaction

Naghashi

et al. 2019

In this paper, we assess the use of Inertial Measurement Units (IMU) in recognising different volleyball-specific actions. Analysis of the results suggests that all sensors in the IMU (i.e. magnetometer, accelerometer, barometer and gyroscope) contribute unique information in the classification of volleyball-specific actions. We demonstrate that while the accelerometer feature set provides the best Unweighted Average Recall (UAR) overall, "decision fusion" of the accelerometer with the magnetometer improves UAR slightly from 85.86% to 86.9%. Interestingly, it is also demonstrated that the non-dominant hand provides better UAR than the dominant hand. These results are even more marked with "decision fusion". CCS CONCEPTS • Human-centered computing → Interactive systems and tools; • Interaction paradigms → Web-based interaction.

“…Accessing events of interest in sports recording is of particular interest for both sports fans e.g. a baseball fan wishing to watch all home runs hit by their favorite player during the 2013 baseball season [5], or a coach searching for video recordings related to the intended learning focus for a player or the whole training session [4]. However, these examples require events to be manually tagged which not only requires time and effort but would also split a trainers attention from training to tagging the events for later viewing and analysis.…”

Section: Introductionmentioning

confidence: 99%

A Searching and Automatic Video Tagging Tool for Events of Interest during Volleyball Training Sessions

2019 International Conference on Multimodal Interaction

Tasdemir

et al. 2019

Quick and easy access to performance data during matches and training sessions is important for both players and coaches. While there are many video tagging systems available, these systems require manual effort. This paper proposes a system architecture that automatically supplements video recording by detecting events of interests in volleyball matches and training sessions to provide tailored and interactive multimodal feedback. CCS CONCEPTS• Human-centered computing → Interactive systems and tools; • Interaction paradigms → Web-based interaction.