Condor: Mobile Golf Swing Tracking via Sensor Fusion using Conditional Generative Adversarial Networks
Authors: 
Hong Jia, 
Jun Liu, Yuezhong Wu, Tomasz Bednarz, 
Lina Yao, 
Wen HuAuthors Info & Claims
Pages 31 - 42
Abstract
This paper explores the possibility of incorporating sensor-rich and ubiquitously deployed mobile devices into sports analytics, particularly to the game of golf. We develop a novel solution to track a player's swing in threedimensional (3D) space using inexpensive tools such as depth sensors and Inertial Measurement Units (IMUs). Existing solutions based on these devices cannot produce consistent and accurate swing-tracking. This is due to commonly known issues with occlusion and low sampling rates generated by depth sensors and complex IMU noise models. To overcome these limitations, we introduce Condor, a tailored deep neural networks to make use of sensor fusion to combine the advantages of these two sensor modalities, where IMUs are not affected by occlusion and can support high sampling rates and depth sensors produce more accurate motion measurements than those produced by IMU. Condor could be implemented with edge devices such as a smart wristband and a smartphone, which are ubiquitously available, for accurate golf swing analytics (e.g. tracking, analysis and assessment) in the wild. Our comprehensive experiment shows that proposed method outperforms other solutions and reaches 6.57 cm error in subject-dependent model and less than 10 cm error for unknow-subjects via a tailored conditional Generative Adversarial Networks (cGAN).
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Information
Published In
February 2021
201 pages
- General Chairs:
- Polly Huang,
- Marco Zuniga,
- Tpc Co-chairss:
- Guoliang Xing,
- Chiara Petrioli
Sponsors
- EWSN: International Conference on Embedded Wireless Systems and Networks
- Huawei: Huawei
- CHIIoT: CHIIoT Workshop on Computer Human Interaction in IoT Applications
- sigbed: ACM Special Interest Group on Embedded Systems
- sigmobile: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Publisher
Junction Publishing
United States
Publication History
Published: 28 April 2021
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- Research-article
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EWSN '21: Proceedings of the 2021 International Conference on Embedded Wireless Systems and Networks
February 17, 2021
Delft, The Netherlands
Acceptance Rates
EWSN '21 Paper Acceptance Rate 14 of 44 submissions, 32%;
Overall Acceptance Rate 81 of 195 submissions, 42%
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