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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 57477

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Special Issue Editors

Prof. Dr. Peter Dabnichki

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Guest Editor

STEM College, RMIT University, Melbourne, VIC 3000, Australia
Interests: sport bimechanics; performance analysis; pervasive computing; intelligent systems

Dr. Juliana Exel

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Guest Editor

Centre for Sport Science and University Sports, University of Vienna, 1150 Vienna, Austria
Interests: performance analysis; pervasive computing; sports biomechanics; ecological systems

Special Issue Information

Dear Colleagues,

Vast amounts of data have become central to and an inseparable part of sport performance improvement by getting the edge over competitors, helping to reach peak form at the right moment, avoiding injury and maximizing abilities to name a few. They are collected in different forms and using a variety of sources, from laboratory equipment through to remote devices and wearables. Converting the data into information that yields accurate and reliable data-driven decisions has become the most important challenge to scientists and sport professionals. Sport analytics have become the tool of choice and, nowadays, teams compete not only on the field but also in the backrooms, where sharp minds devise winning analytical approaches to enhance and improve decision making.

This Special Issue will highlight developments in analytics tools, training applications, performance improvements and support for both physical and mental wellbeing of the athletes. Integrated software and hardware developments are given substantive presence.

Prof. Dr. Peter Dabnichki
Dr. Juliana Exel
Guest Editors

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Keywords

performance monitoring
pervasive analysis
instantaneous feedback
data-driven intervention
injury prevention
smart equipment
intelligent systems

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Published Papers (10 papers)

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13 pages, 1783 KiB

Open AccessArticle

Optimizing Sporting Actions Effectiveness: A Machine Learning Approach to Uncover Key Variables in the Men’s Professional Doubles Tennis Serve

by Fernando Vives, Javier Lázaro, José Francisco Guzmán, Rafael Martínez-Gallego and Miguel Crespo

Appl. Sci. 2023, 13(24), 13213; https://doi.org/10.3390/app132413213 - 13 Dec 2023

Cited by 3 | Viewed by 1856

Abstract

This study used a novel machine learning approach to uncover key serve variables that maximize effectiveness in men’s professional doubles tennis. A large dataset of 14,146 serves from 97 Davis Cup doubles matches played between 2010 and 2019 was analyzed using explainable AI techniques. The angle and distance from the bounce to the sidelines of the serves were found to best distinguish the points won with aces from rallies lasting more than three strokes. Optimal serve angle ranges of 5.7–8.7° substantially increased the probability of serving an ace by over 80%, compared to around 30% when serving used more central angles. Lateral bounce distances of 0–28 cm from the sidelines also boosted the ace probability by over 50%. The serve speed was shown to have less influence on serve effectiveness as compared to singles tennis, with velocities above 187 km h⁻¹ only increasing the probability of serving an ace by 10%. These findings have important practical implications for the tactical decision-making and technical training of serves in men’s professional doubles tennis. The data highlight that the angle and placement of serves are more important than velocity for attaining effective serves in doubles. Coaches and players can use this knowledge to pay special attention to the most important variables in the effectiveness of serves, such as the line distance and angle, in order to maximize the performance of the doubles serve. The novel methodology used in this study provides a valid and reliable way to calculate the efficiency of actions in various sport disciplines using tracking data and machine learning approaches. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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11 pages, 1625 KiB

Open AccessArticle

The Effect of Bodyweight Support and Incline Running on Triceps Surae Electromyographic Activity

by Tom Timbert, Nicolas Babault, Spyridon Methenitis, Carole Cometti, Nicolas Amiez and Christos Paizis

Appl. Sci. 2023, 13(17), 9620; https://doi.org/10.3390/app13179620 - 25 Aug 2023

Cited by 1 | Viewed by 2225

Abstract

Body weight support (BWS) and incline running (IR) are commonly used either during rehabilitation or during training separately, with many positive effects on athletes’ performance and rehabilitation. The aim of the present study was to investigate the interaction between bodyweight support and incline running on the electromyographic activity of the triceps surae and compare it to flat running. In eighteen healthy men (age: 20.3 ± 1.2 years, body weight: 70.2 ± 4.8 kg, body height: 179.6 ± 5.4 cm), the changes in electromyographic activity (EMGA) during a 10 min run with BWS (15% or 30% of body weight; in different occasions) and IR at 7%, as well as jumping performance and gait spatiotemporal parameters, were evaluated. A lower Rating of Perceived Exertion and a significant decrease in the size of the Vastus Lateralis (VL) (33.4%), Soleus (SOL) (17%), and Gastrocnemius Lateralis (GL) EMGA (28.5%, p < 0.05) but not in Gastrocnemius Medialis (GM) (10.5%, p > 0.05), was observed during BWS30% at 7% slope compared to flat running. Also, low-frequency fatigue of the quadriceps was induced only after running without BWS on a 7% slope (p = 0.011). No changes were found in jumping performance (p = 0.246) and gait spatiotemporal parameters (p > 0.05) except for flight time (p < 0.006). In conclusion, running with a slope of 7% and 30% of BWS can result in EMG activity comparable to that observed during level running. This method can also be used in prevention and rehabilitation training programs without creating fatigue. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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13 pages, 576 KiB

Open AccessArticle

JudgED: Comparison between Kickboxing Referee Performance at a Novel Serious Game for Judging Improvement and at World Championships

by Dominik Hoelbling, Andre Salmhofer, Cebrail Gencoglu, René Baranyi, Karl Pinter, Serhat Özbay, Süleyman Ulupinar, Abdullah Bora Ozkara and Thomas Grechenig

Appl. Sci. 2023, 13(17), 9549; https://doi.org/10.3390/app13179549 - 23 Aug 2023

Cited by 4 | Viewed by 2521

Abstract

The particular responsibility of referees in combat sports lies in their decision-making to enforce the rules of the sport, which requires considerable experience and a multitude of skills, including perception, categorization, memory processing, and information integration. As a cost-effective alternative to in-tournament training, this research aims to evaluate the novel video-based serious game called “JudgED” to train martial arts referees’ decision-making processes through immediate feedback. The effectiveness of the JudgED game was assessed by (a) measuring decision accuracy and specific reaction time, (b) calculating a theoretical probability of correct scoring, and (c) comparing these results with real competition judging agreement data. A field study was conducted to analyze the performance of 16 kickboxing referees. The study involved two video-based tests in the serious game. The performance data for JudgED were obtained via a procedure that compares the players’ inputs in the serious game with expert-defined decisions. The results were compared to real-competition data gathered through qualitative analysis of kickboxing fights (n = 400 fights/1200 bouts) at the WAKO World Championships 2021. The findings showed an average decision accuracy of 43.011% and an average reaction time of 1.022 s. For further comparison, binominal distribution for the probability of correct final decisions (between 15.3% and 67.2%) in JudgED and Fleiss’ Kappa interrater reliability for JudgED (Ring:

κ

= 0.371; Tatami:

κ

= 0.398; p < 0.001) and tournament decisions (by bout:

κ

= 0.114; by fight

κ

= 0.063; by outcome

κ

= 0.166; p < 0.001) were calculated. The results suggest that more training is required to improve referee decision accuracy, and JudgED bears the potential to work as a suitable supporting system. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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13 pages, 606 KiB

Open AccessArticle

Prevention and Rehabilitation Gaming Support for Ankle Injuries Usable by Semi-Professional Athletes Using Commercial Off-the-Shelf Sensors

by Jonas Galli, René Baranyi, Dominik Hoelbling, Karl Pinter, Christoph Aigner, Werner Hörner and Thomas Grechenig

Appl. Sci. 2023, 13(16), 9193; https://doi.org/10.3390/app13169193 - 12 Aug 2023

Cited by 2 | Viewed by 2682

Abstract

Ankle injuries are amongst the most common musculoskeletal injuries. The necessity of prevention measurements before or an early rehabilitation start after an injury, is essential for (semi-) professional sports like soccer to decrease healing duration. Sensor-supported serious games could complement a therapeutic program to support resilience and motivation during the prevention or rehabilitation process. Therefore, the aim of this study is to develop and evaluate a user-centered prototype of a serious game using a commercial Off-The-Shelf MetaMotion IMU sensor. A semi-structured interview with a soccer club therapist, followed by an online questionnaire containing 48 questions (n = 91), was performed to ensure a user-centered approach. Based on this, a prototype, including five identified functional requirements and seven exercises (comprising: horizontal/vertical in- and eversion, dorsi- and plantarflexion, knee bend and squat, and toe and heel rise), was developed in an iterative process and evaluated by two participants with an acute ankle injury. The questionnaire outcomes showed averages of 3.3 ankle injuries per participant and 40 absence days per incident. Additionally, 85% of the participants reported needing more prevention time for such injuries. The evaluation phase (total training duration: 2 h 52 min) consisted of playing two different game types (1 and 2 degrees of freedom) and three different levels, where an avatar needs to be controlled while running and avoiding obstacles or collecting trophies. Both range of motion (ROM) and scores, which are directly measured by the game, showed significant improvements (ROM: t = 5.71; p < 0.01; Score: t = 3.98; p < 0.01) between the first and last session in both participants (P1: ROM +3.56°; Score +7.00%, P2: ROM +6.59°; Score +9.53%), indicating high effectiveness, despite a short training period (1 and 2 weeks). ROM improvement results and athlete feedback coincide in that the sensor-assisted serious game might be beneficial for ankle prevention and rehabilitation. At the same time, the increased scores indicate substantial motivation over several training sessions. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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12 pages, 911 KiB

Open AccessArticle

Incidence of Injuries in Elite Spanish Male Youth Football Players: A Season-Long Study with Under-10 to Under-18 Athletes

by Jesus Barguerias-Martínez, Mário C. Espada, Abian Perdomo-Alonso, Sergio Gomez-Carrero, Aldo M. Costa, Víctor Hernández-Beltrán and José M. Gamonales

Appl. Sci. 2023, 13(16), 9084; https://doi.org/10.3390/app13169084 - 9 Aug 2023

Cited by 8 | Viewed by 1545

Abstract

The aim of this study was to analyse the injuries sustained by youth football players from a professional team of the Spanish League integrated into an elite academy, considering the sporting context, the month, and the category of the player throughout the 2017–2018 football season. A total of 227 players in under (U) age categories from U-10 to U-18, with two age-groups in each category (A and B), except U-18, with three groups (A, B and, C), were evaluated. Of the 242 cases, 196 injuries were observed. Injury recurrence or different injuries were observed in the same football player during the season, specifically in the older age categories. With regard to the location of injuries, sixteen different parts of the body were associated with injuries, with five of those totalizing the majority of incidence (162 injuries): the ankle (19), the foot (10), the hip (22), the knee (27), and the thigh (74). A negative relationship was observed between the sporting context and the number of injuries sustained (Rho = −0.203; p = 0.002), and a positive relationship between the category and the number of injuries was identified (Rho = 0.488; p < 0.001). Of the total, 118 injuries were sustained during training (62.8%), 70 were sustained in competitive moments (37.2%), and the remaining cases were associated with accidents outside football. The majority of injuries were muscular (101), followed by articular injuries (49), with both combined accounting for 150 of the total injuries. Though no relationship was observed between month and number of injuries (Rho = −0.024; p = 0.707), the months associated with the restart of training routines after interruptions (August, September, and January) were the highest, other than May, in which the highest number of injuries was observed. The findings in this study suggested that it is advisable to carry out a greater number of hours of injury prevention training in U-16 and U-18. Coaches should routinely monitor young players’ development in order to adapt and balance training interventions to individual needs, and they should consider implementing specific injury risk mitigation strategies in youth football based on the long-term development of the football players. Additionally, it is our understanding that it is essential to program, perform, and monitor specific training sessions or even specific training tasks considering the player’s category and long-term sporting development. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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16 pages, 3565 KiB

Open AccessArticle

Smart Boxing Glove “RD α”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning

by Dea Cizmic, Dominik Hoelbling, René Baranyi, Roland Breiteneder and Thomas Grechenig

Appl. Sci. 2023, 13(16), 9073; https://doi.org/10.3390/app13169073 - 8 Aug 2023

Cited by 9 | Viewed by 3215

Abstract

Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD

α

) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study [...] Read more.

Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD

α

) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD

α

system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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13 pages, 19375 KiB

Open AccessArticle

Situational Analysis and Tactical Decision-Making in Elite Handball Players

by Simona Nicolosi, Antonella Quinto, Mario Lipoma and Francesco Sgrò

Appl. Sci. 2023, 13(15), 8920; https://doi.org/10.3390/app13158920 - 2 Aug 2023

Cited by 3 | Viewed by 1961

Abstract

Situational analysis and decision-making represent key elements of elite sports performances, but few studies have investigated which player’s skills related to these aspects are relevant in elite handballers. The aim of this study was to address differences among handballers belonging to two tiers in processing situational probabilities information related to offensive and defensive situations. A total of 38 handballers (male = 22, female = 16, age: 25.6 ± 6.5 years, first-tier = 11, second-tier = 27) saw videos about different offensive and defensive actions. According to the temporal occlusion paradigm, each handballer provided a response about the best action a selected player had to perform according to the game’s context. The time, accuracy, and technical correctness of each player’s response were assessed. MANOVA revealed moderate-to-high skills differences between first- and second-tier players. First-tier players provided higher scores in response time and accuracy; they also obtained higher technical correctness scores in the most complex situation. The members of the first tier seemed to mainly depend on the accuracy of responses, even if the technical correctness also resulted in a predictor in the most complex situation. Playing in the best tier seems to require the development of very good skills related to processing situational probability information; therefore, training these elements seems to be necessary for determining the differences among elite handballers. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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15 pages, 2468 KiB

Open AccessArticle

A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques

by Mauro Mandorino, Antonio Tessitore, Cédric Leduc, Valerio Persichetti, Manuel Morabito and Mathieu Lacome

Appl. Sci. 2023, 13(15), 8808; https://doi.org/10.3390/app13158808 - 30 Jul 2023

Cited by 3 | Viewed by 2405

Abstract

Previous studies have shown that variation in PlayerLoad (PL) could be used to detect fatigue in soccer players. Machine learning techniques (ML) were used to develop a new locomotor efficiency index (LEI) based on the prediction of PL. Sixty-four elite soccer players were monitored during an entire season. GPS systems were employed to collect external load data, which in turn were used to predict PL during training/matches. Random Forest Regression (RF) produced the best performance (mean absolute percentage error = 0.10 ± 0.01) and was included in further analyses. The difference between the PL value predicted by the ML model and the real one was calculated, individualized for each player using a z-score transformation (LEI), and interpreted as a sign of fatigue (negative LEI) or neuromuscular readiness (positive LEI). A linear mixed model was used to analyze how LEI changed according to the period of the season, day of the week, and weekly load. Regarding seasonal variation, the lowest and highest LEI values were recorded at the beginning of the season and in the middle of the season, respectively. On a weekly basis, our results showed lower values on match day − 2, while high weekly training loads were associated with a reduction in LEI. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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26 pages, 5160 KiB

Open AccessReview

Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis

by Victor R. A. Cossich, Dave Carlgren, Robert John Holash and Larry Katz

Appl. Sci. 2023, 13(23), 12965; https://doi.org/10.3390/app132312965 - 4 Dec 2023

Cited by 17 | Viewed by 29980

Abstract

We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity. However, recent advances in technology have ushered in a new era of objective and real-time performance analysis. AI has revolutionized sports analysis by streamlining data collection, processing vast datasets, and automating information synthesis. VR introduces highly realistic training environments, allowing athletes to train and refine their skills in controlled settings. AR overlays digital information onto the real sports environment, providing real-time feedback and facilitating tactical planning. DV techniques convert complex data into visual representations, improving the understanding of performance metrics. In this paper, we explore the potential of these emerging technologies to transform sports performance analysis, offering valuable resources to coaches and athletes. We aim to enhance athletes’ performance, optimize training strategies, and inform decision-making processes. Additionally, we identify challenges and propose solutions for integrating these technologies into current sports analysis practices. This narrative review provides a comprehensive analysis of the historical context and evolution of performance analysis in sports science, highlighting current methods’ merits and limitations. It delves into the transformative potential of AI, VR, AR, and DV, offering insights into how these tools can be integrated into a theoretical model. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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18 pages, 313 KiB

Open AccessPerspective

Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization?

by Juliana Exel and Peter Dabnichki

Appl. Sci. 2024, 14(8), 3361; https://doi.org/10.3390/app14083361 - 16 Apr 2024

Cited by 4 | Viewed by 7673

Abstract

In elite sports, athletic excellence demands meticulous performance preparation and a sound health status. This paper overviews the current propositions and applications of pervasive computing and data analytics and our vision on how they should be used in future frameworks to contribute to the optimal balance of athletes’ performance and health requirements. Two main areas will be discussed. The first area is Sports Performance Optimization, in which we consider interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will also briefly discuss how the betting industry has been relaying and developing sports analytics. The second area is Athlete’s Wellness and Wellbeing, which will discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circadian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the final part of this paper, we argue that a critical issue for managers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes from monitoring tools and athletes’ feedback interventions. This framework has the potential to revolutionize how athletes’ performance and well-being are monitored, assessed, and optimized, contributing to a new era of precision in sports science and medicine. Full article

(This article belongs to the Special Issue Analytics in Sports Sciences: State of the Art and Future Directions)

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