Cited By
View all- Chen DLuo QChen XWang XZhou C(2024)MagDotProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314237:4(1-25)Online publication date: 12-Jan-2024
Real-Time Tracking of Smartwatch Orientation and Location by Multitask Learning
Pages 780 - 781
Abstract
In this demo, we present RTAT, a real-time arm tracking system that tracks both orientation and location of a smartwatch simultaneously by a multitask learning neural network. We incorporate an attention layer and design a dedicated loss for the multitask neural network to learn the dynamic relationships among Inertial Measurement Unit (IMU) sensors. RTAT supports real-time tracking by performing deep learning inference on a smartphone. Finally, to train RTAT, we develop an easy-to-use labeled data collection system that uses a low-cost virtual reality system to measure the ground truth orientation and location of the smartwatch. Extensive experiments show RTAT outperforms significantly the state-of-the-art solutions in inference accuracy and latency.
References
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Scott Sun, Dennis Melamed, and Kris Kitani. Idol: Inertial deep orientation-estimation and localization. arXiv preprint arXiv:2102.04024, 2021.
[2]
Martin Brossard, Silvere Bonnabel, and Axel Barrau. Denoising imu gyroscopes with deep learning for open-loop attitude estimation. IEEE Robotics and Automation Letters, 5(3):4796--4803, 2020.
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Changhao Chen, Xiaoxuan Lu, Andrew Markham, and Niki Trigoni. Ionet: Learning to cure the curse of drift in inertial odometry. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
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Mahdi Abolfazli Esfahani, Han Wang, Keyu Wu, and Shenghai Yuan. Orinet: Robust 3-d orientation estimation with a single particular imu. IEEE Robotics and Automation Letters, 5(2):399--406, 2019.
[5]
Miaomiao Liu, Sikai Yang, Wyssanie Chomsin, and Wan Du. Real-time tracking of smartwatch orientation and location by multitask learning. In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, 2022.
Index Terms
- Real-Time Tracking of Smartwatch Orientation and Location by Multitask Learning
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Published In
November 2022
1280 pages
ISBN:9781450398862
DOI:10.1145/3560905
- General Chairs:
- Jeremy Gummeson,
- Sunghoon Ivan Lee,
- Program Chairs:
- Jie Gao,
- Guoliang Xing
Copyright © 2022 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
- SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
- SIGCOMM: ACM Special Interest Group on Data Communication
- SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
- SIGOPS: ACM Special Interest Group on Operating Systems
- SIGBED: ACM Special Interest Group on Embedded Systems
- SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 January 2023
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Author Tags
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- Demonstration
Funding Sources
- UC National Laboratory Fees Research Program
- NSF (National Science Foundation)
Conference
SenSys '22: The 20th ACM Conference on Embedded Networked Sensor Systems
November 6 - 9, 2022
Massachusetts, Boston
Acceptance Rates
SenSys '22 Paper Acceptance Rate 52 of 187 submissions, 28%;
Overall Acceptance Rate 174 of 867 submissions, 20%
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Cited By
View all- Chen DLuo QChen XWang XZhou C(2024)MagDotProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314237:4(1-25)Online publication date: 12-Jan-2024
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