A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training
2. Sport Training
- Planning refers to the prescription of the proper exercise units. The cycle of sports training sessions are focused around the competition calendar. It is the phase in which the trainer prepares the exercise schedule for the athlete.
- Realization is the execution phase of the prepared exercises. The roles of the trainer in this phase are: preparing (potential) equipment, conducting a psychophysical evaluation of the athlete before the session, monitoring the intensity of the session, and improving tactics in team based sports. The exercise data needed for further analysis are recorded in this step.
- Control is a comparison of the exercises actually performed by the athlete versus the planned exercises. This can be completed by the use of video analysis and contemporary computational technology. In individual sports, a bio-metric performance analysis can be performed, whereas notational analysis systems are used in team sports.
- Evaluation is the measurement of the athlete’s performance. Two kinds of evaluations exist: (1) The evaluation of the single training load (short-term performance analysis) and the (2) evaluation of the total training cycle load (long-term performance analysis). The evaluation is the comparison between set goals versus achieved results, and the amount of planned versus actually performed exercises.
3. Research Methodology
- RQ: How do smart applications influence the process of sports training?(a) RQ: In which phases of sports training are smart applications utilized?
- RQ: Which sports are the most supported?
- RQ: Which intelligent data analysis methods are the most utilized in smart training applications?
- RQ: How mature are the research ideas of smart training applications in practice?
(“sport”) AND (“training” OR “tracker” OR “logger” OR “diary” OR “trainer”) AND (“data mining” OR “computational intelligence” OR “artificial intelligence” OR “big data” OR “machine learning”).
- The research addressed sport training, sport trainers, or sport trainees.
- The research was peer reviewed.
- The research addressed sport as an athletic activity requiring skill or physical prowess and often of a competitive nature .
- The research used at least one of the intelligent data analysis technologies (e.g., data mining, computational intelligence, big data, and machine learning).
- The research was not in the English language.
- The full text of the research was not available on the digital library or any of the subscription services.
- The research only addressed activity recognition from a leisure perspective (e.g., general health).
- The research was limited to the five scientific databases/search engines: ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and Google Scholar.
- The research had to be available prior to 12.03.2020, when the indexing of potential articles was conducted.
- Google Scholar results were searched until there were at least two consecutive pages of non-relevant results (20), so a total of 270 results were inspected.
- Idea (TRL 0–3).
- Validation (TRL 6–7).
- Production (TRL 8–9).
4. Intelligent Data Methods Used in Studies
- Computational Intelligence methods :
- Data Mining:
- conventional Data Mining methods, i.e., Apriori .
- Machine Learning: Conventional machine learning methods, i.e., Decision Trees (DT) , adaptive boosting , Random Forests (RF) , Gradient Boosting (GB) , K-Nearest Neighbors  (k-NN), Support Vector Machine (SVM) , Artificial Neural Networks  (ANN), hierarchical clustering , and k-means clustering .
- Other methods: Case-Based Reasoning (CBR) , Dynamic Time Warping  (DTW), Bayesian Networks (BN) , Naive Bayes (NB) , Markov chain , generalized additive models , Gaussian process , Linear Regression  (LR), regularized logistic regression , linear discriminant analysis , and spline interpolation .
5. Review of Sports
- Individual—aikido, archery, climbing, jumping, fencing, fitness (gym training), golf, hammer throwing, karate, kickboxing, rowing, running, shooting, skiing, swimming, Tai-chi, tennis, triathlon, weight lifting, and yoga.
- Mixed—badminton, cycling, rowing, ski jumping, table tennis, and tennis.
- Team—basketball, cricket, (American/Australian) football, handball, hockey, soccer, and volleyball.
5.3. Fitness (Gym Training)
5.9. Table Tennis
5.13. Weight Lifting
5.14. Other Sports
- Planning—no research in the domains of basketball, hammer throwing, rowing, table tennis, tennis, climbing, golf, hockey, karate, ski jumping, skiing, and yoga.
- Realization—no research in the domains of triathlon, aikido, archery, and ski jumping.
- Control—no research in the domains of fitness (gym), aikido, archery, hammer throwing, ski jumping, and yoga.
- Evaluation—no research in the domains of fitness (gym), table tennis, aikido, climbing, fencing, golf, hammer throwing, karate, kickboxing, skiing, Tai-chi, and yoga.
- Knowledge transfer into the real-world and validation level research. There are a lot of research papers that propose planning the training sessions for athletes in various sports. However, most of the papers are concluded with the results in a table, where the results generated by the selected method are shown. However, we do not know how some athletes approach these plans and what the long-term consequences or influence on race results are. Therefore, we encourage researchers to also share their insights of these results in the real-world. Most of the research that was presented reached at most the control phase of TRL and, as such, stopped short of the validation phase. If the field is to gain widespread validity such research is needed, and researchers should try to get in contact with professional athletes more and plan their experiments to capture a wider scope of audience in the field researched.
- Cooperation with trainers and athletes: Every athlete is unique and his/her body or mind have different features. How to integrate this component in automatic intelligent solutions still remains a very topical problem. According to our systematic literature review, there are almost no papers that would include the conversation of researchers with athletes and their trainers in the design phase of their experiments.
- Obtaining test datasets and their dissemination: The experiments are based on data that could be real or synthetic (i.e., generated artificially). Although a lot of data are available publicly (for example in cycling , or soccer ), most of the data are still inaccessible, mostly in the domain of individual sports. For that reason, researchers should be encouraged to deposit their data into public repositories, and enable other researchers to access their data.
Conflicts of Interest
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|Database Name||URL||No. Total Results||No. Included Results|
|ACM Digital Library||dl.acm.org||33||20|
|Google Scholar||scholar.google.com||17,900 (270)E||43|
|ACM DL||IEEEX||ScienceD.||Scopus||G. Scholar|
|ACM Digital Library||/||0||0||1||1|
|Reference||Reference no.||[citation number]|
|Year of publication||-|
|Sport||Type||individual, mixed (e.g., tennis in pairs), team|
|Name||name of the sport|
|Research||Methods used||algorithms identified in Section 4|
|Training phases||Planning||0—not addressed, 1—idea, 2—prototype, 3—validation, 4—production|
|Sport Type||No. of Studies||% of Studies (General Studies Excluded from Count.)|
|||SVM||Recognizing the basketball training type automatically by sampling data from a battery powered wireless wearable device equipped with motion sensors, by using the SVM classifier.||99.5% accuracy of activity recognition with the SVM algorithm on the examined data-set.||0||2||1||0|
|||DTW||A VR training system is presented helping users to learn sport gestures in the case of basketball.||A bio mechanical training regimen was developed and amateur users were interviewed.||0||2||1||0|
|||k-NN; RF||Use of a wrist watch wearable device that records movement. A model was built to classify the actions of basketball players.||The Random Forest model was found to be the most accurate.||0||2||2||0|
|||/||Proposal of a web based information system called Basketball Coach Assistant (later BCA) for sports statistics and analysis.||The proposal and prototype were described.||0||1||1||1|
|||Apriori||Identification of commonly used technical actions in basketball games to provide reference for the training of players and coaches, based on Apriori algorithm model generated association rules.||The system’s usage in practice was demonstrated.||0||0||0||2|
|||PSO||Proposal of method for generating eating plans for athletes.||An eating plan generation method proposal, based on Particle Swarm Optimization.||2||0||0||0|
|||BA||Method for sports training sessions, where the training plans are generated using the Bat Algorithm according to data obtained from a sports watch while cycling.||The construction of a training plan generation model based on the previous performance data of an individual athlete.||2||0||0||0|
|||BN||Machine Learning models to assist cycling experts in the decision-making processes for athlete selection and strategic planning in the track cycling omnium.||A Bayesian Belief Network model was constructed to predict the future final standings of cyclists in each cycling event category.||2||0||0||2|
|||LSTM||A Proposal for an artificial coaching system for road cycling athletes, able to follow and tailor their training plans automatically, based on a Machine Learning algorithm.||The virtual coach provided personalized training plans of comparable quality to human experts.||2||2||2||0|
|||k-NN; SVM; BN; DT||Comparison of different algorithms used to determine the activity being conducted in the Fitness Center by using data from a smart wrist wearable device.||Deep Exercise Recognizer model constructed from received measurements and combined individual Machine Learning models.||0||2||0||0|
|||k-NN; SVM; DT||An automatic indoor exercise recognition model for both in gym and home usage scenarios. Classified activities are Biceps curl, Chest fly, Row, Push up, Sit up, Squat and Triceps curl.||Accuracy of 95.3% and 99.4% was achieved for activity recognition and repetition count, respectively.||0||2||0||0|
|||BA||Planning fitness training sessions.||A fitness training session generation method that takes into account muscle groups, intensity and repetition, so that a balance between muscle groups is achieved for best results in training for a triathlon.||2||0||0||0|
|||Adaptive Boosting||The demonstration of a wearable system, based on a fabric force mapping sensor matrix, which can measure the muscle movement during various sporting activities, demonstrated with the case of leg workout exercises.||81.7% accuracy was achieved after 24 different leg workout sessions.||0||2||0||0|
|||Custom Data Mining||Time series (one year of rowing training data) data analysis, to obtain the knowledge rules from the training data of rowing through analysis.||Model that determines if the individual over or under trained||0||0||2||2|
|||Apriori||Analysis of the association between the different training items of (rowing) athletes in different time segments during training in the method of time series data analysis.||Discovery of some rules regarding training and future performance.||0||0||2||2|
|||DTW||Automated feedback implementation, to decrease the number of mistakes made by trainees, on a VR supported rowing simulator.||The learning rate of the requested velocity profile was significantly higher for the experimental group compared with the control group.||0||2||2||0|
|||Simulated annealing||Planning the optimum running speed of an athlete, by estimating the physical effort needed at each part of the competitions and training.||An example of the application of a data-driven approach to the development of an adaptive decision support system for sports training, based on a case of estimating the optimum running speed of an athlete.||2||2||1||1|
|||/||A proposal for a wearable personal training system which supports the user’s outdoor fitness activities with context-aware and user adaptive advice, based on sensed context, a user model, and knowledge elicited from a personal trainer and a Sport Physiologist.||A proposal by a training expert was developed to guide the user towards optimum training.||1||1||1||1|
|||DE||A post hoc analysis of sport performance in a marathon run||A method that determines where an athlete underperformed (lost time) when their results are weaker than expected, based on GPS running data.||0||0||0||2|
|||DE||A post hoc analysis of an athlete’s performance (time trial) based on their heart rate.||25 different strategies identified by the Differential Evolution algorithm on how to reduce time deficits in each kilometer of the course.||0||0||0||2|
|||k-NN||The implementation of an ambient intelligence system applied to the practice of outdoor running sports, with support for personalized real-time feedback for sports practitioners.||The system provided real time feedback (with up to 70% accuracy) based on terrain, temperature and slope.||0||2||2||0|
|||ANN; GB; LR||Fatigue detection and warning system to prevent injuries from occurring during training.||Models, trained on a longitudinal dataset of runners, were able to predict the Rate of Perceived Exertion accurately.||0||2||0||0|
|||GB||Predicting the finishing time of athletes for 800m and 5000m runs based on exercise (wearable) and nutrition (diary) data.||With a small sample, the GBM algorithm proved more accurate than the SVM, LR, RF and DNN algorithms.||0||2||2||2|
|||/||Assessment of the kinematic features of a standardized endurance running test using novel ETHOS (Inertial Movement Units) sensors.||A minimum set of two acceleration sensors attached to the athlete’s foot and hip were sufficient to derive kinematic features that allow for a distinction between experienced and inexperienced runners.||0||2||2||0|
|||BN||The presentation of a system that formulates an optimized interval training method efficiently for each individual by using Data Mining schemes on attributes, conditions, and data gathered from an individual’s exercise sessions.||The users who followed the proposed system training plans burned 29.54% calories compared with the Tabata interval training protocol.||2||2||2||0|
|||SVM; k-NN; Spline interpolation||Demonstration of the feasibility of ambient intelligence technologies applied to outdoor sports practice.||A system which chosen user track to adjust the difficulty of running has achieved 80% success rate in runners maintaining their target heart rate in training.||0||2||0||0|
|||SVM||Proposal for an athlete performance prediction-model and Sports Science training plan based on an SVM built model||SVM predicted the running results of athletes with 90% accuracy.||0||0||1||2|
|||/||The design and implementation of a computer-aided shooting training and instructing system for trainees and coaches.||The device was accurate, and allows for the replacement of parts for shooting training (with live ammunition) with a virtualized component.||0||2||1||0|
|||Custom Data Mining algorithm||Design and implementation of a shooting training and intelligent evaluation system by image acquisition.||A system was developed which can be used on 95 different military-style rifles. The system provided automatic target-scoring and analysis of the shooter’s technique.||0||3||2||2|
|||Fuzzy||Proposal for a shooting trainer system solution, based on the requirements received from the shooters and trainers, which consists of hardware and software components.||The top level architecture of the system was presented.||1||1||1||1|
|||RF; LR; ANN; SVM||Estimate future goal scoring performance and shots attempted for soccer players, based on past performance.||A model to predict goals scored, based on total shots attempted.||0||0||0||1|
|||/||Recording player positions in a match with the use of a wearable device and visualizing them by placing them on a heat map.||A model incorporating sensor data recordings and output on a heat map was developed.||0||0||0||2|
|||SVM; Gaussian Process; ANN||A model for predicting the recovery time after the injury of soccer players, based on the parameters regarding the player and the injury.||No single method was found to be significantly better than the other two. Somewhat accurate predictions can be acquired.||2||0||0||0|
|||K-means clustering||Smart Coach user adaptation model to assist with recommendations in user training.||System was developed, and is going to be tested at two soccer clubs.||1||1||1||1|
|||k-NN; SVM||Developing a Machine Learning predictive system for early injury detection and prediction was based on athletic load data.||A player individualized probability mapping of possible future injuries was presented.||2||2||0||0|
|||Custom Data Mining algorithm||A soccer tactics Data Mining algorithm using an improved association rules mining algorithm.||The proposed algorithm effectively distinguished between different types of soccer tactics.||0||0||0||1|
|||LR, RF, ANN||A Machine Learning model to determine the performance of a soccer player at a particular playing position.||The ANN model achieved 79.01% accuracy.||1||0||0||0|
|||DT||Soccer player injury forecasting by extracting training load data and physical attributes.||A set of rules were proposed for evaluating and interpreting the relation between injury risk and training performance in professional soccer.||2||3||2||0|
|||DT; RF, SVM, GAM, LR, k-NN||Predicting the Rate of Perceived exertion from GPS training data of players in a soccer club.||GPS data was used to uncover the training workload of players in a professional soccer club during a season. The proposed Ordinal predictor was accurate and precise in medium RPE value (i.e. between 4 and 7) but was not consistent in the extreme values (i.e. below 4 and above 7).||0||2||2||0|
|||RF||Detecting the in-season short-term training cycles in an Italian elite soccer team.||The soccer training cycles detected were composed of two kinds of training: high and low intensity training loads performed in the days long before, and close to, the match.||2||3||3||0|
|||Apriori||Algarve Cup 2012 Germany vs. Japan match analysis supported with Data Mining||Identification of general playing patterns key combinations and players in a soccer match that have a positive impact on scoring opportunity.||0||0||0||1|
|||LSTM||Deriving peaks in soccer players’ ability to perform from subjective self-reported wellness data collected using the PMSys system.||LSTM RNN model could predict the performance peaks with an accuracy of at least 90%.||2||0||0||2|
|||/||Evaluation of swimmer training performance by recording the exercise with a wearable sensor and analyzing the data.||Measurements were performed and the measurement of the critical stroke rate was proposed via use of the wearable.||1||2||1||1|
|||Fuzzy||Data Mining the available data in the domain of Swimming and proposing a set of fuzzy rules ‘‘IF (fuzzy conditions) THEN (class) regarding swimmer’s feelings after their training session.||The proposed Machine Learning tool acquired the rules from the data with an almost 70% accuracy rate.||0||1||1||0|
|||DT; ANN||Proposal for a methodology for the automatic identification and classification of swimmers’ kinematics (swimming strokes) information, retrieved from sensors, during interval training of competitive swimming.||The accuracy of the stroke style classification by both the multi-layered Neural Network (NN) and the C4.5 Decision Tree were 91.1%.||0||2||2||0|
|||ANN||To establish neural form models assisting the recruitment process in sport swimming, based on swimmers’ physical attributes and standardized athlete results.||Kohonen’s networks showed that through the use of independent variables, they could group subjects accurately into categories, which after a year, achieved very good, average and very weak performances.||3||0||0||0|
|||SVM; BN; RF; k-NN;||Table Tennis stroke detection and stroke type classification using inertial sensor data, were trained and tested by SVM; Linear Kernel (LIN); Radial Based Function (RBF) kernel; k-NN.||An SVM based algorithm yielded the best results with a classification rate of 96.7%.||0||2||0||0|
|||LSTM||A Deep Learning-based coaching assistant method, for providing useful information in supporting Table Tennis practice on data collected by an Inertial Movement Unit sensor.||Experimental results showed that the presented method can yield results for characterizing high-dimensional time series patterns.||0||2||0||0|
|||/||Investigating the use of virtual reality training to improve Table Tennis skills.||VR training improved participants’ real-world Table Tennis performance significantly compared to a no-training control group in both quantitative and quality of skill assessments.||0||3||3||0|
|||Fuzzy||Use of Artificial Intelligence on sports coaching with the example of tennis coaching.||A tennis coaching prototype that rates each swing based on input data.||0||1||2||0|
|||ANN||Recognizing events of a tennis game from sound recording.||A system that identifies the main events of tennis games based on sound recordings.||0||0||0||2|
|||ANN||The recognition of tennis strokes by using a sensor for data collection and ANN for activity recognition.||A trained ANN model for the recognition of 9 different tennis strokes. Basic recognition achieved, recognition of advanced motion is still a work in progress.||0||2||1||0|
|||Apriori||Associative Rules proposal on tennis strokes mined from the data of a professional female athlete.||Rules about an athlete were discovered, and using these rules in the future was proposed.||0||0||0||2|
|||LSTM||A Deep Neural Network model classification model of action recognition in tennis, from video data.||A 3-layered LSTM network was able to classify fine-grained tennis actions with high accuracy (between 81.23%–88.16%)||0||2||0||0|
|||SVM||An automated method for quantifying shot counts, and for discriminating shot types among elite tennis players using an Inertial Movement Unit sensor and video recording data.||Binned shots (overhead, forehand, or backhand) were classified with an accuracy of 97.4% while a 93.2% accuracy rate was achieved for the classification of all 9 shot types.||0||2||2||0|
|||PSO||Planning the training sessions for triathlon||Training session plan (multiple sessions) generation method for preparing for a triathlon.||2||0||0||0|
|||DT||Decision Tree classification of eating suggestions during a longer endurance race.||A Decision Tree model was built to aid in the decision making process during a race, based on welfare, weather, heart rate and distance.||0||0||0||2|
|||PSO||Presentation of an automatic framework for modeling preference times, based on previous results of athletes for a particular race course with the Particle Swarm Optimization algorithm.||A Particle Swarm Optimization model was developed from which an athlete can determine their preference times for an Ironman triathlon||0||0||1||2|
|||RF; k-NN; |
DT; BN; SVM; LR; Linear Discriminant Analysis
|Predicting the level of the in-exercise loads by the use of Machine Learning methods (linear regression, Linear Discriminant Analysis, k-nearest neighbors, Decision Tree, Random Forest, Gaussian naive Bayes, support-vector machine) for monitoring energy expenditures in athletes.||The k-NN classifier was found to be the best predictor, but the data are not generalizable, and need to be studied further.||0||0||2||0|
|||MC; LR||Improving the team performance of a (specific) volleyball team, through identifying specific skills to improve in training.||Ball blocks were found to be determinable to their team performance, so their use in training was recommended.||2||1||1||3|
|||LSTM||Arms, hands and wrists, standing posture and timing recognition by the use of wearable (Inertial Measure Unit and EMG sensor) and video cameras and classification of disallowed moves so that feedback is provided in training sessions.||The proposed model proved beneficial for beginner players. It was possible to describe the setting action with IMU and EMG sensors and the usage of performance classes.||0||2||0||0|
|||k-NN||SAETA AmI system for professional team sports training presentations. Developing decision-making systems for different aspects of player training, providing automated real time feedback to coaches and athletes.||The system use in practice was presented. The system detection process of player jumps (which has a 93% rate for true positives and 100% accuracy for true negatives) was described in depth.||1||2||2||2|
|||ANN||A prediction of a female power-lifter’s performance, based on their applying their bio-metric data to an ANN model.||A prediction model was built that gives an estimation of a best dead-lift performance.||0||0||2||2|
|||CNN||A training system was developed for Weight Lifting exercises. It contained fatigue and a posture warning system, based on CNN, to warn users about incorrect postures or high fatigue.||A solution was developed that warns users of incorrect movements and fatigue, based on readings from sensors and cameras.||1||2||1||0|
|||SVM||Classification of Weight Lifting exercise by an SVM model currently performed from data worn on a single wrist wearable device.||94,36% accuracy achieved in classifying between 9 different exercises.||0||2||0||0|
|||Markov Chains, SVM, k-NN||Accurate and real time tracking of select/the use of wearable gyroscope and acceleration meter sensors (based on Inertial Measurement Units).||The system can identify Weight Lifting exercises with a delay of 300 ms and count their repetitions with high accuracy.||0||2||0||0|
|||CNN||A prototype of a system for extracting poses from weightlifting sport training videos.||Deep Key Frame Extraction (DKFE) was presented for sport training video analysis.||0||2||2||0|
|||ANN||Artificial Neural Network (ANN) model to automatically evaluate exercises in weight training, by receiving data from sensor placed on Weight Lifting machines.||ANN model automatically differentiated between improper and proper execution of machines and provided feedback.||0||2||2||0|
|||Fuzzy||The Fuzzy logic approach to evaluate if Weight Lifting exercises on machines are being executed correctly.||Fuzzy rules regarding proper training execution on machines were proposed combining time duration, velocity, and displacement of equipment parts.||0||0||1||0|
|||RF||Assessing and providing feedback on Weight Lifting exercises with sensor support and a trained Random Forest model.||Users using the system made less mistakes doing Weight Lifting exercises than when not using the system.||0||2||2||0|
|||CNN||Presentation of the key pose recognition method, based on Deep Learning.||The learned system could classify users lifting weights in one of the four poses with over 95% accuracy.||0||2||0||0|
|||Aikido||CBR||Presentation of an AI-Virtual Trainer educative system on a case of Aikido lessons. The system proposes varied lessons to trainers, via the utilization of case-based reasoning.||The system can propose training tasks based on requested training objectives, without repeating the same exercises.||2||0||0||0|
|||Archery||SVM||Predicting the class of archers based on the chosen performance variables using the SVM model.||Archers were split between low and high potential and a 97.5% classification accuracy rate was found.||2||0||0||2|
|||Badminton||LR||To design a mobile application, which will serve as a virtual trainer and provide the athlete with dietary, exercise and health related advice, based on his profile.||A developed solution for managing stress and health, generating exercise and training schedules, suggesting meals by using the example of badminton.||3||1||3||2|
|||Climbing||/||A system for automatic route recognition on a climbing wall is proposed using a Inertial Measurement Unit sensor.||The system was developed and tested. It was very accurate for ascent-only climbs, but only limited in use when the ascent was combined with a descent.||0||2||0||0|
|||Climbing||/||Wearable device based on the feedback of an online survey of climbers. Verification of the device by a case study in a climbing gym. Investigating best notification channels for real time notifications during a climb.||The most suited notification channel was sound, directly followed by vibrotactile output.||0||1||0||0|
|||Jumping||ANN||Artificial Neural Network (ANN) model to determine the effect of 15 weeks of resistance training on changes in countermovement jump (CMJ) performance in male track and field athletes.||A model was built that predicted performance increase based on weekly volume load.||0||2||2||2|
|||Cricket||Fuzzy, ANN||Developing an Artificial Intelligence based cricket coach, that amateur cricketers can use to practice and gain expertise in cricket, particularly in batting, bowling and fielding.||The system can suggest the best strokes as well as bat movements and can train fielders with regard to every aspect of training.||2||2||1||0|
|||Cricket||SVM||Identification of the optimal set of attributes (of cricket players), which impose the high impact on the results of a cricket match.||Player attributes were determined based on their effect on the end result of a cricket match.||2||0||0||2|
|||Fencing||BN||Development of a decision support system of the Chinese National Fencing Team based on a Bayesian network.||The model could provide effective decision support for coaches.||1||1||1||0|
|||(American / Australian) Football||ANN||Estimating the Rate Perceived Exertion (RPE) based on the GPS data of professional Football players by using Artificial Neural Networks.||The Training Load model, could estimate RPE based on GPS movement data, training and previous RPE measurements.||1||2||2||3|
|||(American / Australian) Football||LR; RF; SVM||Hamstring injury prediction models based on training loads, estimated by GPS, accelerometers and perceived exertion ratings of an Australian football club.||Logistic Regressions were found to be the best performing model for predicting injuries. Poor accuracy was found when data from another football club was applied.||1||2||2||1|
|||Golf||/||A custom algorithm that estimates swing golf trajectories and rates them based on data from a Inertial Measure Unit sensor and a camera.||The model was presented, whose outputs were swing trajectories and features.||0||2||2||0|
|||Golf||CNN||Example of golf swing data classification methods based on Deep Convolutional Neural Network (deep CNN) fed with multi-sensor golf swing signals.||The Deep CNN model outperformed the SVM method.||0||2||2||0|
|||Hammer Throwing||/||Proposal of scientifically described training targets and routes, which in turn require tools that can measure and quantify the characteristics of an effective hammer-throw. The development of a biomechanical feedback device to be used in training of hammer throwers.||The software and hardware architecture of the proposed system was described.||0||1||0||0|
|||Handball||DT||Lower externity muscle injury risk prediction model from handball players’ personal, psychological and neuromuscular data, as well as a comparison between different Decision Tree classifiers.||SmooteBoost ADTree was found to be the best algorithm for predicting injuries.||2||1||2||2|
|||Hockey||SVM; DT||Identifying differences between individuals in a hockey team and proposing a future match result estimator based on the latest training biometric data.||An SVM based model was built based on the training performance. The model allowed for the prediction of future game outcomes of The University of Virginia’s (UVA) varsity field hockey team with 79.8 % accuracy.||0||2||2||3|
|||Karate||DTW||Investigating repetitiveness of karate kicks of skilled karate practitioners||Ranges of body and joint movement were established for standard karate kicks.||0||2||2||0|
|||Karate||/||Kinect based VR training system for Karate katas.||The system was presented briefly.||0||1||0||0|
|||Kickboxing||/||Automated Planning techniques for generating individual training plans, which consist of exercises the athlete has to perform during training, given the athlete’s current performance, period of time, and target performance that should be achieved.||The training plans automatically generated by the proposed approach were more detailed and individualized than plans prepared manually by an expert coach.||2||0||0||0|
|||Kickboxing||k-NN; SVM||Automatic classification by skill level of trajectory data of Kickboxing strike techniques.||73.3% accuracy of classifying strikes by skill level was achieved by k-NN.||0||2||2||0|
|||Ski Jumping||/||Developing a system for the automatic evaluation of ski jumps on the base of Machine Learning algorithms.||A custom built algorithm for evaluating the performance of individual ski jumps, based on recorded sensor data was proposed.||0||0||0||2|
|||Skiing||CNN||AI Coach system to provide personalized athletic training experiences for posture-wise sports activities with the case of skiing.||The AI coach system for pose tracking was used by volunteers and a questionnaire reported that they were satisfied with the system.||0||1||1||0|
|||Tai-chi||RF; SVM||A VR training system of Tai Chi that analyzes and provides feedback to trainee movements.||The VR training system detected mistakes and corrected trainees, but some of the more subtle mistakes went largely undetected.||1||2||1||0|
|||Yoga||Adaptive Boosting||Presentation of a Kinect v2 powered interactive system for Yoga pose recognition, with real time direction and picture guidance about the poses to be executed.||6 different yoga poses were identified with 92% accuracy.||0||2||0||0|
|||RF||End-to-end motion analysis system analyzing joint angle and impact acceleration data among athletes with a back injury and uninjured athletes.||Movements that can contribute to knee and back injury was measured and individual movements of athletes wearing an IMU can be classified by the built model.||2||0||0||2|
|||ANN; k-NN||Use of Artificial Neural Networks and K-Nearest Neighbors to estimate maximum workload in cardiopulmonary tests without completing the whole test.||Shortened test proposal that uses k-NN and ANN to estimate the athlete’s ability without completing the whole test, therefore not fully exhausting the athlete.||0||2||2||0|
|||SVM||Voice-based fatigue detection SVM model, that predicts fatigue levels during a training exercise.||A 91.67% accuracy rate for fatigue was detected. The fatigue was measured with the Rating of Perceived Exertion||0||2||0||0|
|||BA||Discovering the characteristics and habits of athletes in training||Results of the BatMiner algorithm for association rule mining were presented and association rules were proposed to aid in the decision making of trainers||2||1||1||1|
|||/||A general approach to design of a automated personal trainer.||A general approach (algorithm independent) for creating an artificial trainer.||0||1||0||0|
|||NB||Developing a training assistant which helps regular people exercise and monitor their diets.||A generalized app was developed which monitors a person’s exercise activity and food intake plans and suggests exercises and nutrition plans.||2||2||2||2|
|||/||Proposal for a Data Mining tool to guide sports training and provide technical and tactical analysis.||A proposal for a framework for a Data Mining tool is presented to aid coaches and trainees.||0||0||1||1|
|||BN||Presentation of an inductive approach for dynamically modelling sport-related injuries with a Dynamic Bayesian Network (DBN), on data from regularly monitored athletes.||DBN suggested subjectively-reported stress two days prior, internally perceived exertions one day prior to the injury and direct current potential and sympathetic tone the day of, as the most impact towards injury occurrence.||0||2||2||0|
|||/||Proposal for a generalized training framework based on emerging technologies.||A personalized training framework proposal based on genetics and regular training monitoring.||1||1||1||1|
|||K-means clustering, Hierarchical Clustering||Proposal for an integrated Data Mining algorithm based on sports team match data and the generation of individual training regimes to increase their athletes’ stamina.||Proposed algorithm with necessary hardware and software equipment for actual use.||1||1||1||1|
|||k-NN||Textile pressure sensor matrix, that can be integrated into exercise mats to recognize and count exercises done on a exercise mat.||A model for recognition between different standard exercises achieved 82.5% user independent and 89.9% user dependent counting accuracy.||0||2||2||0|
|||/||A general method to develop an expert system for dynamically adapting workout sessions of athletes was presented. A study of identifying relevant parameters that influence sporting performance was conducted.||An empirical study and self monitoring of athletes led to the rule catalog proposed by the researchers.||1||1||1||1|
|||LR; RF, DT||Experiment evaluating performance of football players in countermovement jumps (CMJs) and predicting it by using different Machine Learning methods||A correlation between countermovement jump performance and ability to produce greater force in a short period of time was found.||0||2||1||2|
|||/||Proposal of a model for evaluating fitness level of athletes by measuring their heart rate and GPS location.||Framework that provides a fitness level based on cardiac parameter identification.||0||1||1||2|
|Counter Movement Jumping||/||1||1||1|
|Smart Sport Training Stage|
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Rajšp, A.; Fister, I., Jr. A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training. Appl. Sci. 2020, 10, 3013. https://doi.org/10.3390/app10093013
Rajšp A, Fister I Jr. A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training. Applied Sciences. 2020; 10(9):3013. https://doi.org/10.3390/app10093013Chicago/Turabian Style
Rajšp, Alen, and Iztok Fister, Jr. 2020. "A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training" Applied Sciences 10, no. 9: 3013. https://doi.org/10.3390/app10093013