# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 3 Discussion  
The aim of this scoping review was to to present an overview of the methods used in the literature to classify human motion data based on IMU data, to analyse whether and how the time-dependent data structure of IMU sensors is taken into account in the classification step of the ARC, and further to identify research areas that need further detailed investigation, e.g., in the form of a systematic review. Due to the variety of disciplines and classification tasks, an aggregation of the results was not possible. However, in conducting the scoping review, data extraction also proved to be difficult because of the different disciplines involved and sometimes because of inconsistencies in reporting the data analysis process. For example, sample size is reported in a variety of ways, sometimes as total sample (e.g., steps), per subject, or number of trials (e.g., runs). Furthermore, the included features are not always clearly described and the number of features used is not always comprehensible. The different division of the data into training, test, and validation sets is also not always clearly stated which might be due to page limits. However, for direct comparisons and the identification of the best performing classifiers for certain research questions, this information would be needed. The analysis revealed that classification methods have been applied to a variety of disciplines, where tennis, soccer, and basketball were the three most often analysed sports. Human motion data was mainly classified via deep learning, classic machine learning, DTW, or threshold-based approaches. For each of these methods, different pros and cons can be identified and the approaches differ in their algorithmic complexity, model accuracies, and degree of interpretability. While deep learning approaches show a high level of accuracy, they are less easy to interpret than rule-based learners, linear or logistic regression, and decision trees that show high interpretability but less model accuracy [113]. The SVM, the most often applied approach in the analysed works, was the classification method in 45.2% of the papers, and shows a high generalisation capability but has weaknesses with regard to algorithmic complexity and the training time in large data sets, the challenge of correct classification in imbalanced data sets, and the development of classifiers for multi-class problems [114]. DTW, which has been successfully applied in various works in the area of biosignal processing, takes nonlinearities into account [115]. On the other hand, DTW is time consuming due to the calculation of the similarity distance for each pair of sequences [116]. LSTM and HMM are also popular tools for sequential and time series data [14; 117]. In general, time-dependence of the IMU data was, if it was considered, incorporated in different ways: either directly within the methods applied, such as LSTM, or in the feature calculation step by incorporating time-dependency explicitly using rolling windows with an overlap. Due to the difference in data collection, experimental set-up, and classification tasks, it is difficult to compare the classification performances across the works. In general, the analysis revealed that there is no single best-performing method, as analysing the classification methods compared within works revealed the best classifiers varied between the works and also depended on the training data sets/different number of features or validation procedures. Overall, deep learning models, random forests, boosting approaches and, SVMs mostly showed higher classification accuracy than decision trees, KNN, and NB in direct comparison. However, more complex models, such as LSTM, do not always achieve higher classification accuracy, as [48] found that the one-dimensional CNN performed best, compared to the SVM, multilayer perceptron, LSTM, and other deep convolutional neural networks.