# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 2 Results
### Classification
#### 2.3.2 Classification Methods  
The classification methods can be generally divided into three main branches: first, classic machine learning methods, such as decision trees, k-nearest neighbours (KNN), or naive Bayes (NB) classifiers, which were applied in 78 papers. Second, seven works developed threshold or correlation-based algorithms to classify the motion data into pre-defined groups. Third, dynamic time warping (DTW) clustering approaches were used in seven works. Table A2 in the Appendix A shows a detailed overview of the methods used in each article. In addition to these three main approaches, the authors' own developed methods were applied in seven works. Refs. [73; 86; 87] applied cluster-based approaches. Some authors used hybrid or two-stage approaches. Ref. [32] classified, in their skateboard classification, first grid or air and then the final trick in the second stage. Ref. [86] applied a back-propagation artificial neural network in the first stage, and in the second, fuzzy C-means. Ref. [69] combined a rule to first spot a motion gesture of interest (first stage) with a decision tree for activity classification (second stage). Ref. [82] classified one of four styles in the first stage and continued with a more detailed classification in the second stage. The number of classes varied from binary classification [29; 39; 41; 43; 51; 53; 58; 59; 63; 70; 74; 81; 88; 98; 100; 103; 107; 109] to 28 labels in [35]. Ref. [53] applied binary classification to identify dives or no-dives of goalkeepers as well as multi-class classification to identify motion classes. Ref. [21] also tested an ensemble approach where they combined random forests, a support vector machine (SVM) and KNN with a voting approach. The authors also included personal characteristics in their classification models. Ref. [110] calculated two separated models for forehand and backhand strokes and Ref. [67] proposed a two-layer recognition framework.  
A detailed analysis of the classification methods (see Table 5 and Figure 4) revealed that SVM was the method most frequently used in 42 articles, followed by any type of neural networks (39) and KNN (23). Out of the 39 papers that used any type of neural network (NN), 10 applied an LSTM. Sixteen works used a random forest, 19 a decision tree, and eight models were based on boosting algorithms. Seventeen applied NB classifiers, 11 logistic regression, seven DTW, and six linear discriminant analysis. Nearly half (42;  
\begin{table}
\begin{tabular}{l l r} \hline \hline
**Split Type** & **Article** & **Total Articles** \\ \hline Subject-wise & [20; 23; 25; 26; 27; 29; 32; 34; 36; 38; 39; 45; 46; 47; 53; 56; 57; 60; 62; 63; 66; 71; 73; 79], & \\  & 82,87,100–102,105,106,110] & & 33 \\ \hline Person-dependent & [26; 27; 56; 60; 91; 96] & & 6 \\ \hline Random split/not clear & [19; 21; 22; 24; 28; 29; 31; 33; 35; 37; 39; 44; 48; 55; 58; 59; 61; 64; 72; 74; 79; 81– \\  & 86,88–90,92–95,97–99,102,103,107–109,111] & & 65 \\ \hline Different versions & [29; 35; 39; 43; 71; 79; 82; 91; 102; 104] & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Type of data set split into training, validation and test data set.  
45%) of the works only used one classification method; the others compared more than one where the total range goes from one to eight different classifiers.  
Moreover, Figure 5 shows the number of applied algorithms per sports discipline. Interestingly, in some disciplines such as ski jumping, cross-country skiing, ballet/dancing, or archery, very few different algorithms are used. For the categories _fitness_, _yoga_, _badyweight_, _exercises_, and _badminton_, _basehall_, _cricket_, _golf_, _tennis_, _table tennis_, on the other hand, almost the entire arsenal of machine learning algorithms is used in the literature.