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research-article

TagSleep3D: RF-based 3D Sleep Posture Skeleton Recognition

Authors:

Xiaojiang ChenAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 1

Article No.: 13, Pages 1 - 28

https://doi.org/10.1145/3643512

Published: 06 March 2024 Publication History

Abstract

Sleep posture plays a crucial role in maintaining good morpheus quality and overall health. As a result, long-term monitoring of 3D sleep postures is significant for sleep analysis and chronic disease prevention. To recognize sleep postures, traditional methods either use cameras to record image data or require the user to wear wearable devices or sleep on pressure mattresses. However, these methods could raise privacy concerns and cause discomfort during sleep. Accordingly, the RF (Radio Frequency) based method has emerged as a promising alternative. Despite most of these methods achieving high precision in classifying sleep postures, they struggle to retrieve 3D sleep postures due to difficulties in capturing 3D positions of static body joints. In this work, we propose TagSleep3D to resolve all the above issues. Specifically, inspired by the concept of RFID tag sheets, we explore the possibility of recognizing 3D sleep posture by deploying an RFID tag array under the bedsheet. When a user sleeps in bed, the signals of some tags could be blocked or reflected by the sleep posture, which can produce a body imprint. We then propose a novel deep learning model composed of the attention mechanism, convolutional neural network, and together with two data augmentation methods to retrieve the 3D sleep postures by analyzing these body imprints. We evaluate TagSleep3D with 43 users and we totally collect 27,300 sleep posture samples. Our extensive experiments demonstrate that TagSleep3D can recognize each joint on the human skeleton with a median MPJPE (Mean Per Joint Position Error) of 4.76 cm for seen users and 7.58 cm for unseen users.

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Cited By

Liu YGan MZeng HLiu LDong YCao ZGanesan DLane NShi W(2024)Hydra: Accurate Multi-Modal Leaf Wetness Sensing with mm-Wave and Camera FusionProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690662(800-814)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1145/3636534.3690662

Index Terms

TagSleep3D: RF-based 3D Sleep Posture Skeleton Recognition
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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Sleep sound-activities including snore, cough and somniloquy are closely related to sleep quality, sleep disorder and even illnesses. To obtain the information of these activities, current solutions either require the user to wear various sensors/devices,...
Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review
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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 1

March 2024

1182 pages

EISSN:2474-9567

DOI:10.1145/3651875

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Copyright © 2024 ACM.

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Published: 06 March 2024

Published in IMWUT Volume 8, Issue 1

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National Natural Science Foundation of China
Key Research and Development Program of Shaanxi
Shaanxi Qinchuangyuan Program
NSFC A3 Foresight Program

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Liu YGan MZeng HLiu LDong YCao ZGanesan DLane NShi W(2024)Hydra: Accurate Multi-Modal Leaf Wetness Sensing with mm-Wave and Camera FusionProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690662(800-814)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1145/3636534.3690662

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