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Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification.

Hyunsung Kim1, Jaehee Kim1, Young-Seok Kim2,3

  • 1Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang 37673, Korea.

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Summary

This study introduces an energy-optimized electronic performance tracking system (EPTS) for football players. Advanced methods reduce Global Navigation Satellite System (GNSS) use, extending device life by over 1.3 times.

Keywords:
electronic performance and tracking systemenergy-efficient sensor controlon-device DCNN processingsports wearable device

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Area of Science:

  • Sports Science
  • Wearable Technology
  • Energy Optimization

Background:

  • Analyzing athletic movements is crucial for football player performance evaluation.
  • Existing electronic performance tracking systems (EPTS) face limitations due to high energy consumption, particularly from Global Navigation Satellite System (GNSS) sensors.
  • Extending the operational lifetime of wearable EPTS devices is essential for practical, long-term use.

Purpose of the Study:

  • To develop an energy-optimized EPTS device for football players.
  • To enhance the energy efficiency of wearable sensors without compromising data quality.
  • To extend the operational lifetime of the EPTS device through intelligent power management.

Main Methods:

  • Development of a compact, battery-operated wearable EPTS device incorporating GNSS and Inertial Measurement Unit (IMU) sensors.
  • Implementation of advanced optimization techniques to dynamically reduce GNSS sampling rates during static player movements.
  • Utilization of a novel deep convolution neural network (DCNN) with compression techniques for on-device activity classification and sensor control.

Main Results:

  • The proposed DCNN-assisted sensing control strategy reduced active power consumption by 28%.
  • The energy optimization methods successfully extended the EPTS device's operational lifetime by more than 1.3 times.
  • Accurate classification of football activities and on-device processing were achieved despite memory limitations.

Conclusions:

  • The developed energy-optimized EPTS significantly enhances the usability of wearable technology in sports analytics.
  • Intelligent power management strategies, particularly dynamic GNSS sampling based on activity recognition, are effective for extending device lifetime.
  • The integration of compressed DCNN models enables sophisticated on-device processing for real-time performance analysis in resource-constrained wearable systems.