Paper Info
Title
Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification.
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.
Year
DOI
Venue
2020
10.3390/s20216004
SENSORS
Keywords
DocType
Volume
electronic performance and tracking system,sports wearable device,energy-efficient sensor control,on-device DCNN processing
Journal
20
Issue
ISSN
Citations 
21
1424-8220
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Hyunsung Kim100.34
Jae-Hee Kim2365.91
Seok Young Kim322.38
Mijung Kim41088.34
Young Joo Lee520336.75