Monitoring cyclist biometric signals using a wireless body sensor network based on LILYGO TTGO
Introduction: This article is the result of research project 22883.25-PD at the National Technological Institute of Mexico, conducted at the Instituto Tecnológico Superior de Irapuato in 2025. A cyclist's posture, pedaling technique, muscle strength, and revolutions per minute are crucial factors for the acquisition and processing of signals in dynamic analysis.
Problem: Traditional methods of acquiring biometric data are often direct and uncomfortable for the cyclist, causing inconvenience and stress that can negatively impact performance.
Objective: To analyze the development of a low-cost, compact system for monitoring a cyclist's pedaling performance using wireless communication and sensors such as the AD8832, AD8232, and MPU6050.
Methodology: The WBSN (Wireless Body Sensor Network) biometric monitoring system efficiently captures and processes analog data transmitted via Bluetooth during three pedaling stages (slow, normal, and fast). This process is complemented with imaging of joint articulation nodes.
Results: The biometric analysis enables real-time detection of postural deviations, oxygenation deficiencies, and variations in force applied across the three pedaling stages. Data are acquired wirelessly and in real time.
Conclusions: The system successfully implements the LilyGo Ttgo board to transmit biometric data—such as muscle activity, heart rate, and body posture—in real time, using the Bluetooth v2.4 protocol of the WBSN network.
Originality: The integration of new, compact, and low-cost technologies proves effective in monitoring analog biometric signals in cyclists, with the capability of retransmission via wireless networks.
Limitations: The system's working range could be extended by incorporating a radio frequency (RF) communication protocol.
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