Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification
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 per...
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doaj-aca0460f1e7945b7b8b5090f602444232020-11-25T03:41:09ZengMDPI AGSensors1424-82202020-10-01206004600410.3390/s20216004Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity ClassificationHyunsung Kim0Jaehee Kim1Young-Seok Kim2Mijung Kim3Youngjoo Lee4Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang 37673, KoreaDepartment of Electrical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang 37673, KoreaInstitute of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang 37673, KoreaSports AIX Graduate Program, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang 37673, KoreaDepartment of Electrical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang 37673, KoreaThis 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.https://www.mdpi.com/1424-8220/20/21/6004electronic performance and tracking systemsports wearable deviceenergy-efficient sensor controlon-device DCNN processing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hyunsung Kim Jaehee Kim Young-Seok Kim Mijung Kim Youngjoo Lee |
spellingShingle |
Hyunsung Kim Jaehee Kim Young-Seok Kim Mijung Kim Youngjoo Lee Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification Sensors electronic performance and tracking system sports wearable device energy-efficient sensor control on-device DCNN processing |
author_facet |
Hyunsung Kim Jaehee Kim Young-Seok Kim Mijung Kim Youngjoo Lee |
author_sort |
Hyunsung Kim |
title |
Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification |
title_short |
Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification |
title_full |
Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification |
title_fullStr |
Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification |
title_full_unstemmed |
Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification |
title_sort |
energy-efficient wearable epts device using on-device dcnn processing for football activity classification |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-10-01 |
description |
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. |
topic |
electronic performance and tracking system sports wearable device energy-efficient sensor control on-device DCNN processing |
url |
https://www.mdpi.com/1424-8220/20/21/6004 |
work_keys_str_mv |
AT hyunsungkim energyefficientwearableeptsdeviceusingondevicedcnnprocessingforfootballactivityclassification AT jaeheekim energyefficientwearableeptsdeviceusingondevicedcnnprocessingforfootballactivityclassification AT youngseokkim energyefficientwearableeptsdeviceusingondevicedcnnprocessingforfootballactivityclassification AT mijungkim energyefficientwearableeptsdeviceusingondevicedcnnprocessingforfootballactivityclassification AT youngjoolee energyefficientwearableeptsdeviceusingondevicedcnnprocessingforfootballactivityclassification |
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