The modern world offers various methods to track athletes’ performance in sports. Due to its small size and lightweight, a wearable sensor device with a built-in Inertial Measuring Unit (IMU) is currently in demand. These sensor devices are integrated into sports equipment like tennis racquets, baseball bats, and gloves to monitor and improve athlete performance during training and tournaments. Despite this trend, there is a lack of research in Brunei Darussalam on using wearable sensor devices to study and analyze tennis player’s strokes. Therefore, we developed a cost-effective prototype wearable sensor device for tennis players in Brunei Darussalam. We used Adafruit BNO005 as the IMU sensor and Arduino Nano 33 IoT as the microcontroller to develop our prototype. This prototype was attached to the tennis player’s wrist and collected data. We collected only beginners’ and intermediate tennis players’ forehand and backhand strokes. The collected data was accessed on both PCs and smartphones through software like Arduino IDE and Arduino Science Journal and analyzed their tennis strokes.

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Cost-Effective Wearable Sensors to Study and Analyze Tennis Strokes

  • Siti Nabilah Hj Md Salihin,
  • Ravi Kumar Patchmuthu

摘要

The modern world offers various methods to track athletes’ performance in sports. Due to its small size and lightweight, a wearable sensor device with a built-in Inertial Measuring Unit (IMU) is currently in demand. These sensor devices are integrated into sports equipment like tennis racquets, baseball bats, and gloves to monitor and improve athlete performance during training and tournaments. Despite this trend, there is a lack of research in Brunei Darussalam on using wearable sensor devices to study and analyze tennis player’s strokes. Therefore, we developed a cost-effective prototype wearable sensor device for tennis players in Brunei Darussalam. We used Adafruit BNO005 as the IMU sensor and Arduino Nano 33 IoT as the microcontroller to develop our prototype. This prototype was attached to the tennis player’s wrist and collected data. We collected only beginners’ and intermediate tennis players’ forehand and backhand strokes. The collected data was accessed on both PCs and smartphones through software like Arduino IDE and Arduino Science Journal and analyzed their tennis strokes.