An IoT-Enabled Wearable System for Tennis Stroke Analysis Using IMU Sensors

The ability to make quick decisions and execute fast movements is essential in lawn tennis and is difficult to assess accurately through visual observation and subjective coaching. Current racquet-mounted and wearable sensing systems can collect stroke information, but many require racquet modifications, are cumbersome during play, or are primarily designed for stroke classification without providing real-time visualization and feedback. Therefore, a lightweight, non-invasive, and cost-effective wearable platform for continuous on-court monitoring is still needed. This paper presents a proof-of-concept Internet of Things (IoT)-based wearable system integrated into a conventional tennis wristband for real-time stroke data collection, processing, and visualization. The system is built around a Nordic nRF52832 System-on-Chip (SoC) and an ICM-20948 inertial measurement unit (IMU) mounted on a custom-designed printed circuit board (PCB) powered by a rechargeable lithium-ion battery. Motion data are validated, noise-filtered, and calibrated before being transmitted to an Android application and cloud backend using Bluetooth Low Energy (BLE). On-court testing demonstrated the successful acquisition and real-time visualization of synchronized linear acceleration, angular velocity, and effective jerk signals during tennis stroke execution. The testing was conducted during five 60-minute on-court sessions involving players aged 13–40 years, and the ball–racquet impact was identified using jerk magnitude peaks exceeding 4,000 meter2/second4. These findings demonstrate the technical feasibility of a low-cost, non-invasive wearable IoT platform for real-time tennis stroke monitoring and provide a foundation for future machine learning-based stroke classification and performance analysis.
Keywords: Lawn tennis; Internet of Things (IoT); Inertial Measurement Un