# A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training
## 3 Research Methodology  
We followed the Systematic Literature Review Guidelines in Software Engineering [9] to conduct this review. The goal of this review was to: (1) Identify how modern smart applications and methods assist athletes and trainers in sports training and (2) how fast the theoretical knowledge is transitioning into practical real-world use cases. Based on the study goals, the following Research Questions (RQ) were formulated:  
1. RQ: How do smart applications influence the process of sports training? 1. RQ: In which phases of sports training are smart applications utilized? 2. RQ: Which sports are the most supported? 3. RQ: Which intelligent data analysis methods are the most utilized in smart training applications? 4. RQ: How mature are the research ideas of smart training applications in practice? The literature search was conducted between **12 March 2020** and **18 March 2020**. The standard search string used to identify literature was:  
(\"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\"). There were some differences in the search strings used between databases, due to different query languages and limitations between scientific paper databases. The databases queried are shown in Table 1 with their corresponding number of results; the results are shown prior to the exclusion of duplicates and prior to their evaluation based on inclusion and exclusion criteria. Figure 1: The four phases of sport training. The abstracts of the studies were all inspected to include/exclude the studies from the review. There were some necessary modifications to search strings. The ScienceDirect database allows a maximum of eight Boolean operators per search field, so the search string was split for the abstract and full text conditions. All the results were inspected on all databases except for Google Scholar, where the results were shown by relevancy, and the search was stopped once there were no more included (relevant) studies on two successful pages \(-20\) results, and this criteria was satisfied after 270 inspected results. All the database results were checked for duplicates, and after removal, 181 results remained. The duplicates between database pairs are seen in Table 2, where it can be seen that all the databases had at least one duplicate when compared with Scopus or Google Scholar. That is because Scopus and Google Scholar merely index documents found on other databases, and do not host them. The selection and exclusion criteria were specified, and limitations were examined with respect to determining as complete and actual a state of the field as possible. **Selection criteria:**  
* 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 process and often of a competitive nature_[10]. * The research used at least one of the intelligent data analysis technologies (e.g., data mining, computational intelligence, big data, and machine learning). **Exclusion criteria:**  
* 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). **Limitations:**  
* 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.