Introduction
The KickMetric project, carried out as part of our fourth year of studies at Unilasalle Amiens Engineering School, aims to design a sports performance tracking system specifically tailored to football players. This system includes a performance sensor, a server for data storage and processing, and a mobile application for visualizing the results.
Objectives :
Measurement of Heart Rate: Integrate a heart rate sensor to monitor the player's heart rate during exercise.
Acceleration Measurement: Incorporate an accelerometer to record the player's movements and assess their dynamism on the field.
Measurement of Flexion during Strike: Design a mechanism or specific sensors to evaluate the player's bending and technique during ball striking.
Temperature Measurement: Integrate a temperature sensor to monitor the player's body heat during physical activity.
Real-time Data Transmission: Ensure real-time collection and transmission of measured data to a dedicated application, allowing users to visualize and analyze the information instantly.
User-friendly Interface: Develop an intuitive user interface in the application to allow users to easily track and interpret the collected data.
LoRa :LoRa (Long Range) is a long-range wireless communication technology that is particularly suited for IoT applications.
In our project, we used LoRa to establish a wireless connection between our sports performance sensor device (SODAQ Explorer) and our LoRaWAN network based on The Things Network. The SODAQ Explorer have a LoRa module to collect data such as heart rate, acceleration, and temperature during physical activity.
In the initial phase, we established LoRa communication between the SODAQ board and The Things Network (TTN), configuring the LoRa module on the SODAQ board to establish a LoRaWAN connection with TTN. This entailed registering the LoRaWAN device with TTN and developing Arduino code to transmit sensor data to TTN.
II. Configuration of the Virtual Server and Installation of Node-RED:The second step involved configuring a virtual server with VMware and installing Node-RED on this server. We created a virtual machine with a Linux operating system and installed Node-RED for data processing. This step set up the necessary software infrastructure to receive and process sports performance data.
In the third step, we established the transfer of data from TTN to Node-RED and created an SQL database on a UwAmp server. We configured flows in Node-RED to receive data from TTN and store it in the SQL database. This step centralized the collected data and organized it in a structured manner for further analysis.
Unfortunately, despite our efforts, we encountered challenges in converting the data on The Things Network (TTN) for all metrics except temperature. While temperature data conversion was successfully achieved, difficulties arose in accurately processing and converting other metrics such as heart rate, acceleration, and flexion during the strike.
IV. Development of the Android Application:Due to time constraints, the Android application development in Android Studio remains incomplete.
ConclusionIn summary, despite facing challenges and time limitations, we've made important progress in developing the KickMetric project. From establishing LoRa communication to setting up the virtual server and implementing Node-RED, we've laid the groundwork for collecting and processing sports performance data. Though we weren't able to finish the Android app in Android Studio due to time constraints, we've shown our ability to integrate various technologies and address technical issues. This work represents a significant step forward in creating a comprehensive performance tracking solution for footballers, and it paves the way for future developments to realize our vision for KickMetric.
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