SAETA: A Smart Coaching Assistant for Professional Volleyball Training

This paper introduces a smart assistant for professional volleyball training based on machine-learning techniques (SAETA). SAETA addresses two main aspects of elite sports coaching: 1) technical-tactical effort control, which aims at controlling exercise effort and fatigue levels and 2) exercise quality training, which complements the former by analyzing the execution of player movements. SAETA relies on a sensing infrastructure that monitors both players and their environment, and produces real-time data that is analyzed by different modules of a decision engine. Technical-tactical effort control is based on a dynamic programming model, which selects the best activity and rest durations in interval training, with the goal of maximizing effort while preventing fatigue. Exercise quality control consists of two stages. In the first stage, movements are detected by means of a k-nearest neighbors classifier and in the second stage, movement intensity is classified according to recent statistical data from the player being analyzed. These analyses are reported to coaches and players in real-time. SAETA has been developed in close collaboration with the Universidad Católica San Antonio de Murcia volleyball team, which competes in the Spanish women's premier league. Data gathered during training sessions has provided a knowledge base for the algorithms developed, and has been used for the validation of results.

I. Introduction

Recent developments have led to intelligent environments capable of anticipating people’s actions, reacting intelligently and providing support. These environments constitute a new paradigm in information systems known as Ambient Intelligence (AmI) [1]. Because AmI systems rely on decision-making, machine-learning methods are attracting considerable research interest in this field. AmI examples include spaces for education [2], [3], smart homes [4]–[7], health [8]–[11], sports [12], [13], leisure [14]–[16], transportation [17], [18], and so forth. In this paper, we present a novel AmI system for high-level team sport training that is based on supervised learning techniques and stochastic modeling.