# A Multiprotocol Wireless Sensor Network for High Performance Sport Applications
## 3 Results  
The proposed WSN system presents two main features that make it more widely exploitable in several sport applications with respect to other commercially available systems:  
* High flexibility in number and type of employable sensors;
* Possibility to cover a wide sensor area by using second level Slave nodes;
* OS independent user interface represented by a simple web browser.  
The acquired data can be visualized and analyzed on purpose-made apps that are specific to each sport application. In this section, we present applications of the proposed system in different configurations showing acquired data and a brief description of the specific app for the data analysis.  
Figure 7 depicts the app developed for the e-kayak system equipped with a master node that hosts GPS and IMU and two slave nodes with force sensors on footrest and paddle, respectively. The screen shows all data acquired during a K1 training session: At the top left graph, the data of roll speed (red), yaw speed (yellow), and pitch speed (green, hidden) are shown. The bottom left graph shows the forces on paddle (red) and foot rest (green), while the graphs on the right side of the page depict (from top to bottom) the acceleration on the three axis, the boat's speed, and the traveled distance. All these measurements were taken in synchronous mode, so an increase in the stroke rate and force (e.g., at about 220 s) corresponds to an increase of the roll speed as well as the boat's acceleration and speed.  
Figure 6: (a) Master node in a waterproof case; (b) slave node placed in the paddle shaft.  
Figure 8 shows data from a measure of a session of K2 training for which the system was equipped with four slave nodes: Two for the paddles and two for the footrests. In particular, it depicts a detail of an acquisition of the forces applied by the two athletes on their paddles. One can note a rather good synchronization between the athletes and a good symmetry, for both of them, of the force applied on the left paddle (positive side of the waveform) with respect to the right one (negative side of the waveform). On the other hand, there is a remarkable difference in the force applied by the two kayakers.  
Figures 9 and 10 show some screenshots of preliminary measurements of a session of starting race. In this case, the system was equipped only with the master node to acquire data from GPS and triaxial accelerometer and gyroscope. In particular, Figure 9a shows the screenshot of the analysis app where the measures of the time lap, speed in the selected sectors, lap distance, and the average, top, and bottom speed are reported for each lap. Figure 9b shows the curves of the  
Figure 8: Force on the paddles measured on K2.  
Figure 7: Example of screenshot of the application for e-kayak session analysis.  
lap in a different color, with respect to the position on the track. Finally, Figure 10 shows a screenshot from the same app where the user can, graphically on the map, select the positions of the markers to identify the different sectors on the track. Moreover, the user can analyze the different trajectories together with the speed (the curve at the bottom of the screen) for each lap.  
Figure 11 shows an application of the system for measures of swimming training. In this case, the system was equipped with a master node only. This has been equipped with two differential pressure measurement devices (measuring the difference in pressure between hands' palm and back) for the evaluation of the forces applied by the swimmer with her arms.  
The system is placed on the back of the swimmer, as shown in Figure 10(a). Thanks to the triaxial accelerometer-gyroscope unit, it is possible to assess the effectiveness of each stroke (see Figure 10(b)) [33].  
Figure 10: Screenshot of the application for starting session analysis.  
Figure 9: Examples of screenshot of the application for starting session analysis. (a) Analysis app; (b) kart’s speed.