A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

The rapid transformation of our communities and our way of life due to modern technologies has impacted sports as well. Artificial intelligence, computational intelligence, data mining, the Internet of Things (IoT), and machine learning have had a profound effect on the way we do things. These technologies have brought changes to the way we watch, play, compete, and also train sports. What was once simply training is now a combination of smart IoT sensors, cameras, algorithms, and systems just to achieve a new peak: The optimum one. This paper provides a systematic literature review of smart sport training, presenting 109 identified studies. Intelligent data analysis methods are presented, which are currently used in the field of Smart Sport Training (SST). Sport domains in which SST is already used are presented, and phases of training are identified, together with the maturity of SST methods. Finally, future directions of research are proposed in the emerging field of SST.

[1]  Miguel Torres Ruiz,et al.  A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning , 2017, Telematics Informatics.

[2]  Mark Billinghurst,et al.  Getting your game on: Using virtual reality to improve real table tennis skills , 2019, PloS one.

[3]  Asitha Bandaranayake,et al.  Identifying the optimal set of attributes that impose high impact on the end results of a cricket match using machine learning , 2017, 2017 IEEE International Conference on Industrial and Information Systems (ICIIS).

[4]  Zahari Taha,et al.  The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach. , 2018, Human movement science.

[5]  William T. Scherer,et al.  Developing Predictive Athletic Performance Models for Informative Training Regimens , 2019, 2019 Systems and Information Engineering Design Symposium (SIEDS).

[6]  Heming Zhao,et al.  Detecting sports fatigue from speech by support vector machine , 2016, 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN).

[7]  Lifang Wu,et al.  Deep key frame extraction for sport training , 2019, Neurocomputing.

[8]  Wang Puchun,et al.  The Application of Data Mining Algorithm Based on Association Rules in the Analysis of Football Tactics , 2016, 2016 International Conference on Robots & Intelligent System (ICRIS).

[9]  Majid Sarrafzadeh,et al.  Machine Learning-Based Adaptive Wireless Interval Training Guidance System , 2011, Mobile Networks and Applications.

[10]  Liu Tianbiao,et al.  Apriori-based diagnostical analysis of passings in the football game , 2016, 2016 IEEE International Conference on Big Data Analysis (ICBDA).

[11]  Iztok Fister,et al.  Generating eating plans for athletes using the particle swarm optimization , 2016, 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI).

[12]  Dipankar Das,et al.  Strength Training: A Fitness Application for Indoor Based Exercise Recognition and Comfort Analysis , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[13]  Lorena Torres-Ronda,et al.  Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach. , 2017, International journal of sports physiology and performance.

[14]  Yuji Ohgi,et al.  Sensor Data Mining on the Kinematical Characteristics of the Competitive Swimming , 2014 .

[15]  Christos Tjortjis,et al.  Sports Analytics algorithms for performance prediction , 2019, 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA).

[16]  Wolfgang Kastner,et al.  IMU-based smart fitness devices for weight training , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[17]  Björn Eskofier,et al.  Sensor-based stroke detection and stroke type classification in table tennis , 2015, SEMWEB.

[18]  N. Bragazzi,et al.  Data concerning the effect of plyometric training on jump performance in soccer players: A meta-analysis , 2017, Data in brief.

[19]  Andreas Dengel,et al.  Towards a Digital Personal Trainer for Health Clubs - Sport Exercise Recognition Using Personalized Models and Deep Learning , 2018, ICAART.

[20]  Fabrizio Lamberti,et al.  A Movement Analysis System based on Immersive Virtual Reality and Wearable Technology for Sport Training , 2018, ICVR 2018.

[21]  Shunxiang Wu,et al.  The Design and Implementation of Shooting Training and Intelligent Evaluation System , 2012 .

[22]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Kai Kunze,et al.  VRTe do: the way of the virtual hand , 2018, VRST.

[24]  Simon Fong,et al.  Data Mining in Sporting Activities Created by Sports Trackers , 2013, 2013 International Symposium on Computational and Business Intelligence.

[25]  Kristof Van Laerhoven,et al.  Using Wrist-Worn Activity Recognition for Basketball Game Analysis , 2018, iWOAR.

[26]  J. Vehí,et al.  Artificial Intelligence for Diabetes Management and Decision Support: Literature Review , 2018, Journal of medical Internet research.

[27]  Xiwei Zhong.  A Study on Basketball Techniques and Tactics Based on Apriori Algorithm , 2018, Wirel. Pers. Commun..

[28]  Bin Kong,et al.  A Shooting Training and Instructing System Based on Image Analysis , 2006, 2006 IEEE International Conference on Information Acquisition.

[29]  Bo Lang,et al.  Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey , 2019, Applied Sciences.

[30]  J. Friedman.  Stochastic gradient boosting , 2002 .

[31]  Hugo Fuks,et al.  Qualitative activity recognition of weight lifting exercises , 2013, AH.

[32]  Richard J. Duro,et al.  Ambient Intelligence Systems for Personalized Sport Training , 2010, Sensors.

[33]  David Whiteside,et al.  Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning. , 2017, International journal of sports physiology and performance.

[34]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[35]  Leif E. Peterson.  K-nearest neighbor , 2009, Scholarpedia.

[36]  Luca Chittaro,et al.  MOPET: A context-aware and user-adaptive wearable system for fitness training , 2008, Artif. Intell. Medicine.

[37]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[38]  Zhichao Cao,et al.  Key pose recognition toward sports scene using deeply-learned model , 2019, J. Vis. Commun. Image Represent..

[39]  Luca Pappalardo,et al.  Effective injury forecasting in soccer with GPS training data and machine learning , 2017, PloS one.

[40]  Wannes Meert,et al.  Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion , 2018, KDD.

[41]  Arnold Baca,et al.  Fuzzy Logic in Sports: A Review and an Illustrative Case Study in the Field of Strength Training , 2013 .

[42]  John van der Kamp,et al.  Automatic Classification of Strike Techniques Using Limb Trajectory Data , 2018, MLSA@PKDD/ECML.

[43]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[44]  Florian Daiber,et al.  ClimbSense: Automatic Climbing Route Recognition using Wrist-worn Inertia Measurement Units , 2015, CHI.

[45]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[46]  K. Chamari,et al.  Data concerning isometric lower limb strength of dominant versus not-dominant leg in young elite soccer players , 2018, Data in Brief.

[47]  Pao-Ann Hsiung,et al.  Artificial Intelligence of Things in Sports Science: Weight Training as an Example , 2019, Computer.

[48]  Elena Baralis,et al.  Early prediction of the highest workload in incremental cardiopulmonary tests , 2013, ACM Trans. Intell. Syst. Technol..

[49]  Bahadorreza Ofoghi,et al.  Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach , 2013, Inf. Sci..

[50]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[51]  Iztok Fister,et al.  Planning the sports training sessions with the bat algorithm , 2015, Neurocomputing.

[52]  Janez Brest,et al.  Framework for planning the training sessions in triathlon , 2018, GECCO.

[53]  Hao Wu,et al.  Multi-sensor Golf Swing Classification Using Deep CNN , 2017, IIKI.

[54]  Iztok Fister,et al.  BatMiner for Identifying the Characteristics of Athletes in Training , 2018, Computational Intelligence in Sports.

[55]  Aderemi A. Atayero,et al.  Statistical analysis of frequencies of opponents׳ eliminations in Royal Rumble wrestling matches, 1988–2018 , 2018, Data in brief.

[56]  Olgierd Unold,et al.  Machine learning approach to model sport training , 2011, Comput. Hum. Behav..

[57]  Vinh Huy Chau,et al.  A Gravitational-Double Layer Extreme Learning Machine and its Application in Powerlifting Analysis , 2019, IEEE Access.

[58]  Giampietro Alberti,et al.  GPS Data Reflect Players’ Internal Load in Soccer , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[59]  F. J. Gonzalez-Castano,et al.  Ambient intelligence assistant for running sports based on k-NN classifiers , 2010, 3rd International Conference on Human System Interaction.

[60]  Lukás Chrpa,et al.  Automated Training Plan Generation for Athletes , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[61]  Manolis Maragoudakis,et al.  Sports & Nutrition Data Science using Gradient Boosting Machines , 2018, SETN.

[62]  Paul Lukowicz,et al.  Never skip leg day: A novel wearable approach to monitoring gym leg exercises , 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[63]  Guru Venkataramani,et al.  Machine Learning-Based Analysis of Program Binaries: A Comprehensive Study , 2019, IEEE Access.

[64]  Gerhard Tröster,et al.  What Do Sensors Know about Your Running Performance? , 2011, 2011 15th Annual International Symposium on Wearable Computers.

[65]  Peijiang Yuan,et al.  Recognition of Yoga poses through an interactive system with Kinect based on confidence value , 2018, 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM).

[66]  Stefan Kopp,et al.  Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes , 2018, Comput. Graph..

[67]  William J. Knottenbelt,et al.  Deep Learning for Domain-Specific Action Recognition in Tennis , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[68]  Paul Lukowicz,et al.  Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix , 2014, UbiComp.

[69]  Anatoliy Sachenko,et al.  Artificial Intelligence for Sport Activitity Recognition , 2019, 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[70]  Ayman El-Baz,et al.  Athlete-Customized Injury Prediction using Training Load Statistical Records and Machine Learning , 2018, 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[71]  Kok-Leong Ong,et al.  Predictive Modelling of Training Loads and Injury in Australian Football , 2017, Int. J. Comput. Sci. Sport.

[72]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[73]  Correlational data concerning body centre of mass acceleration, muscle activity, and forces exerted during a suspended lunge under different stability conditions in high-standard track and field athletes , 2019, Data in brief.

[74]  Arnold Baca,et al.  Artificial intelligence in sports on the example of weight training. , 2013, Journal of sports science & medicine.

[75]  Jooyoung Park,et al.  LSTM-Guided Coaching Assistant for Table Tennis Practice , 2018, Sensors.

[76]  Elena Mugellini,et al.  Designing an e-Coach to Tailor Training Plans for Road Cyclists , 2019, IHSED.

[77]  Shiqiang Wang,et al.  Detection of Tennis Events from Acoustic Data , 2019, MMSports '19.

[78]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[79]  Ingolf Waßmann,et al.  Train4U - Mobile Sport Diagnostic Expert System for User-Adaptive Training , 2019, Proceedings of the 12th International Symposium on Computer Science in Sport (IACSS 2019).

[80]  John C. Mankins,et al.  Technology readiness and risk assessments: A new approach , 2009 .

[81]  Antonio Krüger,et al.  ClimbAware: Investigating Perception and Acceptance of Wearables in Rock Climbing , 2016, CHI.

[82]  Wang Jihong,et al.  Research on Tennis Technique and Tactics Decision Support Based on Theory of Association Data Mining , 2010, 2010 Second World Congress on Software Engineering.

[83]  Lili Pan,et al.  A Big Data-Based Data Mining Tool for Physical Education and Technical and Tactical Analysis , 2019, Int. J. Emerg. Technol. Learn..

[84]  L. Burattini,et al.  Sport Database: Cardiorespiratory data acquired through wearable sensors while practicing sports , 2019, Data in brief.

[85]  Vladimir Medved,et al.  Data-driven Basketball Web Application for Support in Making Decisions , 2019, icSPORTS.

[86]  J. Kiely,et al.  The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance , 2019, Journal of functional morphology and kinesiology.

[87]  Francisco Javier Ramirez Fernandez,et al.  Player Tracker - a tool to analyze sport players using RFID , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[88]  Dino Pedreschi,et al.  A public data set of spatio-temporal match events in soccer competitions , 2019, Scientific Data.

[89]  Marek R. Ogiela,et al.  How Repetitive are Karate Kicks Performed by Skilled Practitioners? , 2018, ICCAE.

[90]  B. Shiyamala,et al.  A Trainer System for Air Rifle/Pistol Shooting , 2009, 2009 Second International Conference on Machine Vision.

[91]  Francisco Javier González-Castaño,et al.  SAETA: A Smart Coaching Assistant for Professional Volleyball Training , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[92]  Chih-Wei Yi,et al.  Calculate Golf Swing Trajectories from IMU Sensing Data , 2012, 2012 41st International Conference on Parallel Processing Workshops.

[93]  Giampietro Alberti,et al.  Characterization of In-season Elite Football Trainings by GPS Features: The Identity Card of a Short-Term Football Training Cycle , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[94]  Peter Wolf,et al.  When a robot teaches humans: Automated feedback selection accelerates motor learning , 2019, Science Robotics.

[95]  Kostas Karpouzis,et al.  THETIS: Three Dimensional Tennis Shots a Human Action Dataset , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[96]  G. Myer,et al.  A Preventive Model for Muscle Injuries: A Novel Approach based on Learning Algorithms , 2017, Medicine and science in sports and exercise.

[97]  Iztok Fister,et al.  Development of a framework for modeling preference times in triathlon , 2018, Neural Computing and Applications.

[98]  Iztok Fister,et al.  Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review , 2018, Sensors.

[99]  Li Guangjun,et al.  Knowledge Rule Discovery Based on Training Data of Rowing , 2011, 2011 International Conference on Future Computer Science and Education.

[100]  Aman Shrivastava,et al.  Team strategizing using a machine learning approach , 2017, 2017 International Conference on Inventive Computing and Informatics (ICICI).

[101]  K.D. Peterson,et al.  Decision Support System for Mitigating Athletic Injuries , 2019, Int. J. Comput. Sci. Sport.

[102]  A. Flammini,et al.  IMU-based solution for automatic detection and classification of exercises in the fitness scenario , 2017, 2017 IEEE Sensors Applications Symposium (SAS).

[103]  João Rocha,et al.  Smart Coach - A Recommendation System for Young Football Athletes , 2019, ISAmI.

[104]  Julien Henriet,et al.  Artificial Intelligence-Virtual Trainer: An educative system based on artificial intelligence and designed to produce varied and consistent training lessons , 2017 .

[105]  Kazuya Seo,et al.  Development of an Automated Motion Evaluation System from Wearable Sensor Devices for Ski Jumping , 2016 .

[106]  K. Kipp,et al.  Use of Machine Learning to Model Volume Load Effects on Changes in Jump Performance. , 2020, International journal of sports physiology and performance.

[107]  Hirohiko Suwa,et al.  Volleyball Setting Technique Assessment Using a Single Point Sensor , 2019, 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[108]  Zhaoxian Zhou,et al.  Predicting Countermovement Jump Heights by Time Domain, Frequency Domain, and Machine Learning Algorithms , 2017, 2017 10th International Symposium on Computational Intelligence and Design (ISCID).

[109]  Simon Fong,et al.  Towards Automatic Food Prediction During Endurance Sport Competitions , 2014, 2014 International Conference on Soft Computing and Machine Intelligence.

[110]  Feilong Liu,et al.  Fencing Training Decision Support System Based on Bayesian Network , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[111]  Janez Brest,et al.  Post hoc analysis of sport performance with differential evolution , 2018, Neural Computing and Applications.

[112]  Pawel Swiatek,et al.  ADAPTIVE DECISION SUPPORT SYSTEM FOR AUTOMATIC PHYSICAL EFFORT PLAN GENERATION—DATA-DRIVEN APPROACH , 2013, Cybern. Syst..

[113]  Charles J. Geyer,et al.  Practical Markov Chain Monte Carlo , 1992 .

[114]  Sarah Almujahed,et al.  Sports analytics: Designing a volleyball game analysis decision-support tool using big data , 2013, 2013 IEEE Systems and Information Engineering Design Symposium.

[115]  Janez Brest,et al.  Making up for the deficit in a marathon run , 2017, ISMSI '17.

[116]  J. Tompkins,et al.  'They Ought to Enjoy Physical Activity, You Know?': Struggling with Fun in Physical Education , 2001 .

[117]  V. Marozas,et al.  Inertial sensor for objective evaluation of swimmer performance , 2008, 2008 11th International Biennial Baltic Electronics Conference.

[118]  Hua Li,et al.  A Wireless Sensor System for the Training of Hammer Throwers , 2014, 2014 Tenth International Conference on Computational Intelligence and Security.

[119]  Pål Halvorsen,et al.  Predicting Peek Readiness-to-Train of Soccer Players Using Long Short-Term Memory Recurrent Neural Networks , 2019, 2019 International Conference on Content-Based Multimedia Indexing (CBMI).

[120]  Feng Sun,et al.  Sports Athletes’ Performance Prediction Model Based on Machine Learning Algorithm , 2019 .

[121]  Noel E. O'Connor,et al.  Automatic Detection, Extraction, and Analysis of Landing During a Training Session, Using a Wearable Sensor System☆ , 2015 .