Abstract
This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times.
Highlights
Since the German national football (The term football in this work refers to soccer or association football.) team dominated the World Cup in 2014 with various IT technologies [1], the use of an electronic performance tracking system (EPTS) has been gaining huge popularity in the football industry and has been standardized by FIFA to be used even at the international matches [2].Basically, an EPTS device can be attached to the body of football players during the match time, sensing a number of data related to the athletic and strategic performances
We introduce an global average pooling layer (GAP) rather than utilizing the computation-intensive fully-connected layers [43], which can provide enough recognition accuracy to be used for categorizing the football activities
As we removed only the redundant sensing operations, which is supported by the accurate recognition of football activities with the proposed deep convolution neural network (DCNN) architecture, note that the measured trajectory in Figure 8b uses the fewest sampling points compared to the other approaches shown in Figure 4b and Figure 8a
Summary
Since the German national football (The term football in this work refers to soccer or association football.) team dominated the World Cup in 2014 with various IT technologies [1], the use of an electronic performance tracking system (EPTS) has been gaining huge popularity in the football industry and has been standardized by FIFA to be used even at the international matches [2]. The EPTS device may have a positioning system assisted by numerous calibration sensors [3,4,5], collecting quantitative data such as total distance covered, peak/average speed, or other physiological data. These on-site measurements can be used for analyzing the physical workloads of each player, providing valuable insights to optimize the performance [6,7]. Experimental results show that the proposed idea significantly reduces the number of GNSS accesses while supporting similar distance and speed measurement errors compared to the baseline operations, saving the overall energy consumption of the EPTS device by 28%.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
