SAETA: A Smart Coaching Assistant for Professional Volleyball Training

Abstract

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.

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

“…It may be the case that dominant hand plays a crucially important role in determining the type of action. However, in many applications such as fatigue and stamina estimation [15], researchers are only interested in determining the amount of actions performed regardless of their type. In such cases, the reported results (i.e.…”

“…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].…”

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

“…In sports, heart rate is correlated to the effort of the athlete; its comparison with its thresholds provides performance and fatigue levels in physical activity [73]. For this measurement, [73] which can be used in volleyball, where heart rate sensors are installed on each athlete, monitoring in real time and transmitting to the coaching staff.…”

“…For this measurement, [73] which can be used in volleyball, where heart rate sensors are installed on each athlete, monitoring in real time and transmitting to the coaching staff. However, this measurement type can be used in conjunction with other sensors, executing data fusion in other sports such as athletics (track), cycling, swimming, soccer, and basketball [13,48,73]. …”

Evaluation of Sports Visualization Based on Wearable Devices

“…A. U. Alahakone et al [4] put forward the method of gait research and rehabilitation, and achieved the effect of scientific gait training by using inertial sensor technology to identify toe-off and landing on the heel when exercising on a treadmill. In the research of virtual reality technology in physical education [5][6][7], Lin Zhang et al [8] and Xu Lanjun [9] discussed the application of sports simulation technology and tactics in basketball teaching practice and volleyball teaching, respectively; In the field of campus sports simulation, Liu Heng et al [10] focused on how to stimulate students' independent learning. In the research of Markov process on sport competitive prediction and teaching prediction, Yonggan Wang [11] predicted and analyzed the basketball competition results, while Liu Yaxin studied the application of Markov process on teaching evaluation.…”