# User Assessment of a Customized Taekwondo Athlete Performance Cyber-Physical System
## 3 Study on the System's Acceptability
### System Data Collection  
Data collection plays a vital role in any research study, serving as the fundamental basis for acquiring precise and dependable results. The data collection sessions were conducted at the training location of the involved Taekwondo athletes. This choice was made to ensure that the system was utilized in a real environment, thereby maximizing the accuracy of the obtained data. Additionally, the utmost consideration was given to minimize any disruption to the athletes' training routine. The collected data consist of the values of the Cartesian coordinates of the athletes' joints, as well as the linear acceleration values of the accelerometer and the angular rotation rate of the gyroscope of the movements performed. So, the input to the deep learning module consists of a set of information on the position of the X, Y, and Z axes (three-dimensional coordinates) of each of the human athletes' joints, such as knees, ankles, elbows, and wrists. These data are provided by the 3D camera's depth sensor and developed motion sensors. They are collected by the SPERTA application, developed for the system, which stores them in a database and sends them to the deep learning module to count and identify the athletes' movements. In Figure 7, a graphical representation of the data that are provided to the deep learning network is presented. The set of frames, the X, Y, and Z coordinates, represents the movement of the right-hand joint during _jirugui_ movement throughout a sequence of 80 samples. However, to classify the athlete's movements, data from other joints are considered. To determine which deep learning methodology would be most suitable for the data typology, preliminary tests were carried out using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short-term memory networks (LSTM), as each of the neural networks has different features regarding the method of analysis and data output. To carry out the tests, each of the models were composed of a layer of 100 units and then a dropout rate of 0.5. Below is a brief description of the features of each of the neural networks studied:  
* Convolutional neural networks (CNNs) are feedforward neural networks that use filters and pooling layers and do not have recurrent connections. They are suitable for working with images and videos, for example for facial recognition, medical analysis, and image classification [37]. * Recurrent neural networks (RNNs) are a type of artificial neural network used to analyze time series data; they use recurrent connections to generate output. They are suitable for speech recognition, natural language processing, and tasks such as text summarization, machine translation, and speech analysis. RNNs, due to their architecture, can suffer from problems such as gradient disappearance and explosion [38]. * Long-short-term memory (LSTM) networks are a type of RNN with greater memory power to remember the outputs of each node for a longer period, thus combating the disappearance of gradients or the long-term dependency problem of RNNs. LSTM networks have gates that update and regulate cell states, such as the forget gate, which determines what information in the cell state should be forgotten, helping RNNs remember the critical inputs needed to generate the correct output [39]. After carrying out tests with the described neural networks, the results obtained allowed us to determine that the LSTM model obtained better results. Having been applied  
Figure 7: Data from right-hand joint during _jirugui_ movement along a sequence of 80 samples. in the study, the LSTM model was composed of a first LSTM layer with 100 units, then a dropout of 0.5 rate, followed by action layer with the ReLU function, and, at the end, a Softmax layer with the probabilities per class. This model, due to its analysis features, is better suited to the data under analysis, which is made up of a set of values (frames) over a period of time. The number of frames provided to the deep learning module may vary.