Enhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis

Abstract

Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the "bump-set-spike" trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.

Keywords: action recognition; digital sports technologies; smart sports; sports telematics; telematics applications; training methods.

Figure 1

SSE System Block Diagram: During the sensing phase, data is collected from sensors such as Inertial Measurement Units (IMUs) and/or pressure-sensitive floor. In the subsequent processing phase, appropriate modules process this data. Finally, users such as players and coaches can view and interact with the processed information.

Figure 2

UI screens. (A) Control panel on the PC monitor to be used for configuring the display floor UI. (B) UI to be displayed on the experimental display floor for player training. The example guides a player on where to step and jump. The red circle shows the player(s) location on the floor. (C) UI for the Bump, Set, Spike game to be displayed on the display floor. Progressively complex visuals give cues to the player for the volleyball actions performed.

Figure 3

Progressively complex visuals used in the Bump Set Spike Training System.

Figure 4

ADR feature extraction model with $m=55$ which provides the result of 74.47% (UAR) for Action Recognition with LDA classifier. The left figure indicates the number of frames present in each cluster (hexagon i.e., neuron) and the right figure indicates the distance between clusters (blue dots i.e., neurons) and darker colour indicates greater distance between clusters. The red lines connect neighbouring neurons.

Figure 5

Action Recognition through ADR: 10-fold stratified cross-validation (with random shuffle) Results without impact detection method and no frames/windows discarding.

Figure 6

Action Recognition through ADR: leave one subject out cross-validation Results without impact detection method and no frames/windows discarding.

Figure 7

Action Recognition through CNN using raw signal: 10-fold cross-validation results with impact detection method.

Figure 8

Action Recognition through CNN using raw signal: leave one out cross-validation results with impact detection method.