# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 1 Introduction  
Human motion analysis is applied in various application areas such as athletic performance, medical diagnostics, surveillance, human-machine user interface, or video-conferencing, etc. [1]. Depending on the activity of interest, different sensor set-ups are used to capture and digitise human motion. Ref. [2] differentiate the systems into direct and indirect ones. Indirect systems are vision-based and are represented, for example, by video or optoelectronic techniques. A popular example of a direct system widely used in sports are inertial measurement units (IMU). They typically measure acceleration and angular velocity in three dimensions. The result of a measurement are multivariate time series. In general, the analysis of human motion data can be split into five steps, which are described as activity recognition chain (ARC) [3]. Figure 1 visualises a typical ARC. As shown, the first step is the collection of the raw IMU sensor data. The second step consists of the pre-processing, such as filtering or removal of outliers. Step three is the segmentation phase, in which the raw time series is divided into segments, so-called motion or action primitives. These simple, atomic movements can be combined sequentially to form a complex behavior [4]. In the action hierarchy of [5], action primitives are the lowest unit in the hierarchy from which actions are formed. Step four of the ARC is the calculationof features for each segment, which could be time or frequency domain features, and step five is the classification phase. As for the classification step of the ARC, there are different methods that can be roughly divided into traditional and deep learning models according to [6]. There are already various reviews about the application of machine and deep learning techniques for human motion analysis. Ref. [7] have already analysed the application of machine learning algorithms in the field of sport-specific movement recognition in a systematic review for the period of January 2000 to May 2018. The authors analysed in-field automatic sport-specific movement detection and recognition with IMU and vision-based data. Ref. [8] published a survey on activity detection and classification based on wearable sensors, where they focused on the sensor specifications, such as sensor type, number of sensors used, sensor location, type of activities detected, processing type etc. Ref. [6] concentrated in their survey on the analysis of accelerometer data for human activity recognition. In addition, there exist various surveys for the analysis of deep learning and machine learning methods for general activity detection based on sensor data (see e.g., ref. [9] or [10]). In general, time series classification is a challenging task [11], and several classification algorithms have been developed for both univariate and multivariate data [11; 12; 13]. The methods used for time series classification consider the temporal structure differently. E.g., long short-term memory models (LSTM), which are a type of recurrent neural network, learn the order dependencies in sequential data [14]. In contrast, another common deep neural network architecture for time series classification, the multilayer perceptron (MLP), processes time series elements independently from each other [11]. The aim of this work is to identify the classification methods used for human motion analysis in sports in the form of a scoping review and to analyse whether and how the time series structure of the data is considered. Compared to [7], we focus exclusively on classification tasks and IMU data, do not only include machine or deep learning algorithms, and also include in-lab studies. We mainly discuss the fifth step of the ARC and investigate the methodological and statistical specifications of human motion data classification in sports across all disciplines.