## 4 Experiments and Results  
The purpose of the experiments was to show that the proposed digital twin could be applied in practice for advising an athlete on how to achieve the training plan goals online during the implementation of the interval training session. In line with this, more tests were conducted, and the results are illustrated in the next section.  
The digital twin algorithms were implemented in the Python programming language, while EasyTrain was implemented in Ruby due to its incorporated language extension capabilities [29]. Thus, data from the sensors were captured in one-second interval rates that ensure an athlete's training demonstrates real-time performance. The digital twin was run on a mobile device that is based on the Raspberry Pi computer equipped with GPS and WiFi modules and ANT+ USB (Table 2).  
The GPS module allows access to positioning services, the WiFi module connection of the computer to the Internet, while the ANT+ USB serves as a hub for communication with IoT devices (HR monitors, power meters, etc.).  
All kinds of athletes perform the same interval training sessions as illustrated in Algorithm 4, from which it can be seen that the intensity of the prescribed training session is moderate, because the average heart rate in the speed interval \(\overline{HR}_{spe}=150\) bpm belongs to the moderate HR zone (Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Component** \\ \hline
1 & Raspberry Pi 4v Model B with Raspberry Operating System \\
2 & 4\({}^{\prime\prime}\) LCD touch screen \\
3 & USB ANT+ stick \\
4 & Adafruit’s Ultimate GPS HAT module \\
5 & Kingston SDHC 32 GB Canvas \\
6 & Garmin HRM PRO Chest strap with ANT+ Transmitter \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the hardware equipment.  
The efficiency of the interval sport training implementation was estimated using a proprietary developed measure considering the speed and recovery interval separately. The success rate for the speed interval (\(SR_{spe}\)) is calculated according to a following equation:  
\[SR_{spe}=\frac{1}{repeats}\sum_{n=1}^{repeats}\left(\frac{TRIMP_{spe,n}}{TRIMP_{spe, n}}\right), \tag{8}\]  
where the variable \(\overline{TRIMP_{spe,n}}\) is calculated from the training plan in Algorithm 4, and variable \(TRIMP_{spe,n}\) can be measured using the mobile device. Obviously, the variable \(n\) refers to an observed interval.  
The efficiency of implementing the recovery interval \(SR_{rec}\) is evaluated in a similar way, in other words:  
\[SR_{rec}=\frac{1}{repeats}\sum_{n=1}^{repeats}\left(\frac{TRIMP_{rec,n}}{TRIMP _{rec,n}}\right), \tag{9}\]  
where we operate with variables referring to recovery instead of a speed interval.  
In summary, the average efficiency of the interval training implementation \(\overline{SR}\) is highlighted by the following equation:  
\[\overline{SR}=1,0-\frac{1}{2}\Big{(}(1,0-SR_{spe})-(1,0-SR_{rec})\Big{)}, \tag{10}\]  
which denotes the success rate as the average difference between the success rates achieved in both intervals. It holds that the higher the value of the average success rate, the better the efficiency of the implementation.