# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 3 Discussion
In this context, it also has to been mentioned that nearly half of the works, 45%, only described one classification method and thus lacked a comparison of different classification methods and approaches. Identifying the best working method is therefore not possible in these cases. Future work should concentrate on comparative works and, e.g., compare machine learning approaches with the researchers' own developed threshold-classification approaches, or methods where the time series structure of the IMU data is considered with methods where it is not considered. In most of the works, the data was either split random/record-wise into training, validation, or test data, or the split was not clear. However, the use of a record-wise split has been criticised, especially for clinical outcome predictions [118]. Thirty-three papers validated the models via a subject-wise split and some even tried different split, and thus validation, versions to analyse whether the models perform well and are generalisable to new subjects. Further, in around \(1/3\) of the papers, feature reduction techniques were applied, although the number of reduced features was not always stated. When analysing the four most frequently analysed sports disciplines that were analysed in more than five papers (tennis, soccer, and basketball), it can be shown that in soccer, \(4/6\) (\(66.67\%\)) of the papers applied a neural network approach [21; 44; 53; 75] or an SVM [21; 53; 75; 92]. While in basketball, \(50\%\) of the works applied an SVM [19; 60; 94], no clear pattern can be identified for the classification methods when classifying tennis activities. Furthermore, when it comes to analysing the methods for incorporating time-dependence in the three sports disciplines, no sport-specific trend can be found. Future work could thus systematically compare and analyse classification methods in one specific sports discipline to investigate which of the different classification approaches performs best in certain circumstances in the form of a systematic review. These systematic reviews could also include quality assessments of the included works to analyse whether the lack of clarity is correlated with the journal citation index. A further question could be to investigate the trade-off between model complexity, real-time processing requirements, and model accuracy. The generalisability of the developed models to different populations (in the sports domain, e.g., amateur, recreational, or professional athletes) could be investigated in future research and transfer learning approaches could be tested. Future research could also systematically analyse the effect of feature selection/dimension reduction for specific sports disciplines. In addition, few articles mentioned the challenge of balance/imbalanced data sets and over- and undersampling of data, and data augmentation techniques were rarely used. Therefore, analysing the impact of unbalanced data sets and applying different resampling or data augmentation methods, such as the synthetic minority oversampling technique [119] or adaptive synthetic [120] etc., could be an interesting research question for future work. Hybrid, stacking, and ensemble methods were also rarely used and could be explored in future work. One of the papers included in this review used additional simulated data. The use and application of different data augmentation techniques for time series classification (see, e.g., [121]) to simulate human motion data could also be a future research question. Future work should investigate the relationship between the choice of discipline and how it affects data requirements and algorithm performance. It is plausible to assume that the dynamics of a sport's movement patterns raise the required signal quality in terms of sample rate and resolution. Likewise, it is unknown how the volume (i.e., the number of recorded motion primitives) of the collected sensor data affects classification performance.