# New Perspectives in the Development of the Artificial Sport Trainer
## 3 Illustrative Example
### Digital Twin in Cycling  
The aim of the DT in cycling is to advise the real cyclist what to do during the realization of the training session in a particular situation. Indeed, it monitors the sensors incorporated into a wearable wireless device which measures the performances of the athlete and compares these achievements with those prescribed by the real trainer in the training plans. Based on the history of realized sport training sessions, the DT advises the athlete in which PWR intensity zones he/she must perform the current workout in order to satisfy the demands of the training plan. The proposed DT for cycling is based on the simplified cognitive model of a human (Figure 5), where signals from sensors are perceived by the perception component and transmitted to a \"brain\" component. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Zone** & **PWR Zone Name** & **\% of FTP** \\ \hline
1 & Active recovery & \(<\)55 \\
2 & Aerobic endurance & 56–75 \\
3 & Tempo & 76–90 \\
4 & Lactate threshold & 91–105 \\
5 & VO\({}_{2}\)max & 106–120 \\
6 & Anaerobic capacity & 121–150 \\
7 & Sprint power & \(>\)150 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Power-based training zones according to [PERSON] [37]. The component consists of STM and LTM, where the former is dedicated for processing information from the sensors, the latter to goal-oriented processing. The goal-oriented processing makes decisions according to the current training plan (the goal) and a cyclist's context obtained from the AST+. The cyclist's context is represented as a Long Short-Term Memory (LSTM) network, which incorporates a history of the cyclist's realized training activities, and it is suitable for predicting the cyclist's future behavior. The predicted behavior serves the cyclist as advice on how to react in a specific situation. Typically, the other load indicators that can be measured during a training session besides the power meter, are as follows: heart rate, speed, duration, cadence, altitude, and weather. The HR is still valuable information of intensity load. Speed is a very interesting load indicator, but it is not the most accurate, due to its dependence on wind, weather, and altitude. The duration is an especially powerful load indicator for endurance cyclists, and has a direct relation with the calories' consumption, in the following sense: The longer the training, the larger the calories' consumption. The cadence load indicator measures the frequency of turning the wheel, and depends on the gear used for a specific situation, such as riding a straight road, climbing a hill, or sprinting. Thus, cycling trainers advise that the cyclists perform optimally when they ride the cycles in the lower gear with the higher cadence. Altitude allows the cyclist to monitor climbing the terrain during the sport training. This measure is obtained today using a Global Positioning System (GPS) that is part of the wearable monitoring device. The weather load indicator is part of the external factors that affect the performance of the realized sport training dramatically. Typically, the weather forecast consists of the following indicators: rain, sun, wind, humidity, and temperature. As a DT model, the Recurrent Neural Networks (RNN) are considered that are appropriate for manipulating the sequential dependence among attributes in time-series datasets [35]. Indeed, the sport activities present a kind of time-series data. Unfortunately, the RNNs are sensitive on two problems, i.e., vanishing and exploding gradients, causing inherent instability of a back-propagation algorithm. The back-propagation algorithm works when gradients of the cost function with respect to the weights of the perceptrons consisting in the neural network are decreased by training with the input-output samples [35]. Thus, the vanishing of the gradient arises as a result of successive multiplication  
Figure 5: The proposed DT in cycling (The cyclist’s icon was taken from [38]) which is licensed under Attribution 4.0 International (CC BY 4.0).