Fine-grained vital signs estimation using commercial Wi-Fi devices

Jiacheng Shang; Jie Wu

doi:10.1145/2987354.2987360

Fine-grained vital signs estimation using commercial Wi-Fi devices

Jiacheng Shang
, Jie Wu

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Scopus citations

Abstract

Due to the importance of vital signs, breathing rate and heart rate have been widely used in heath care. In the past few years, various systems and approaches have been proposed to detect and monitor breath and heart beat. In this paper, we show that Wi-Fi signals can also be used to recognize and count breath and heart beats with different postures. The intuition is that human activities have different impacts on time-series of Channel State Information (CSI) values, which can be utilized to recognize macro and micro human activities. In this paper, we design a Wi-Fi signal based breath and heart beats recognition system called Wi-Health. WiHeath consists of two Commercial Off-The-Shelf (COTS) Wi-Fi devices, a sender (Wi-Fi router) and a receiver (laptop). The sender sends 802.11n packets to the receiver, while the receiver continuously receives these packets and records in real-time CSI value. The receiver analyzes the collected CSI values and determines if the human is alive and the number of heart beats and breaths.

Original language	English
Title of host publication	Proceedings of the 8th Wireless of the Students, by the Students, and for the Students Workshop, S3
Publisher	Association for Computing Machinery
Pages	30-32
Number of pages	3
ISBN (Electronic)	9781450342551
DOIs	https://doi.org/10.1145/2987354.2987360
State	Published - 3 Oct 2016
Event	8th Wireless of the Students, by the Students, and for the Students Workshop, S3 - New York, United States Duration: 3 Oct 2016 → 7 Oct 2016

Publication series

Name	Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
Volume	03-07-October-2016

Conference

Conference	8th Wireless of the Students, by the Students, and for the Students Workshop, S3
Country/Territory	United States
City	New York
Period	3/10/16 → 7/10/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Human activity recognition
Signal processing

Access to Document

10.1145/2987354.2987360

Cite this

Shang, J., & Wu, J. (2016). Fine-grained vital signs estimation using commercial Wi-Fi devices. In Proceedings of the 8th Wireless of the Students, by the Students, and for the Students Workshop, S3 (pp. 30-32). Article 2987360 (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM; Vol. 03-07-October-2016). Association for Computing Machinery. https://doi.org/10.1145/2987354.2987360

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title = "Fine-grained vital signs estimation using commercial Wi-Fi devices",

abstract = "Due to the importance of vital signs, breathing rate and heart rate have been widely used in heath care. In the past few years, various systems and approaches have been proposed to detect and monitor breath and heart beat. In this paper, we show that Wi-Fi signals can also be used to recognize and count breath and heart beats with different postures. The intuition is that human activities have different impacts on time-series of Channel State Information (CSI) values, which can be utilized to recognize macro and micro human activities. In this paper, we design a Wi-Fi signal based breath and heart beats recognition system called Wi-Health. WiHeath consists of two Commercial Off-The-Shelf (COTS) Wi-Fi devices, a sender (Wi-Fi router) and a receiver (laptop). The sender sends 802.11n packets to the receiver, while the receiver continuously receives these packets and records in real-time CSI value. The receiver analyzes the collected CSI values and determines if the human is alive and the number of heart beats and breaths.",

keywords = "Human activity recognition, Signal processing",

author = "Jiacheng Shang and Jie Wu",

note = "Publisher Copyright: {\textcopyright} 2016, Association for Computing Machinery. All rights reserved.; 8th Wireless of the Students, by the Students, and for the Students Workshop, S3 ; Conference date: 03-10-2016 Through 07-10-2016",

year = "2016",

month = oct,

day = "3",

doi = "10.1145/2987354.2987360",

language = "English",

series = "Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM",

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}

Shang, J & Wu, J 2016, Fine-grained vital signs estimation using commercial Wi-Fi devices. in Proceedings of the 8th Wireless of the Students, by the Students, and for the Students Workshop, S3., 2987360, Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, vol. 03-07-October-2016, Association for Computing Machinery, pp. 30-32, 8th Wireless of the Students, by the Students, and for the Students Workshop, S3, New York, United States, 3/10/16. https://doi.org/10.1145/2987354.2987360

Fine-grained vital signs estimation using commercial Wi-Fi devices. / Shang, Jiacheng; Wu, Jie.
Proceedings of the 8th Wireless of the Students, by the Students, and for the Students Workshop, S3. Association for Computing Machinery, 2016. p. 30-32 2987360 (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM; Vol. 03-07-October-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fine-grained vital signs estimation using commercial Wi-Fi devices

AU - Shang, Jiacheng

AU - Wu, Jie

PY - 2016/10/3

Y1 - 2016/10/3

N2 - Due to the importance of vital signs, breathing rate and heart rate have been widely used in heath care. In the past few years, various systems and approaches have been proposed to detect and monitor breath and heart beat. In this paper, we show that Wi-Fi signals can also be used to recognize and count breath and heart beats with different postures. The intuition is that human activities have different impacts on time-series of Channel State Information (CSI) values, which can be utilized to recognize macro and micro human activities. In this paper, we design a Wi-Fi signal based breath and heart beats recognition system called Wi-Health. WiHeath consists of two Commercial Off-The-Shelf (COTS) Wi-Fi devices, a sender (Wi-Fi router) and a receiver (laptop). The sender sends 802.11n packets to the receiver, while the receiver continuously receives these packets and records in real-time CSI value. The receiver analyzes the collected CSI values and determines if the human is alive and the number of heart beats and breaths.

AB - Due to the importance of vital signs, breathing rate and heart rate have been widely used in heath care. In the past few years, various systems and approaches have been proposed to detect and monitor breath and heart beat. In this paper, we show that Wi-Fi signals can also be used to recognize and count breath and heart beats with different postures. The intuition is that human activities have different impacts on time-series of Channel State Information (CSI) values, which can be utilized to recognize macro and micro human activities. In this paper, we design a Wi-Fi signal based breath and heart beats recognition system called Wi-Health. WiHeath consists of two Commercial Off-The-Shelf (COTS) Wi-Fi devices, a sender (Wi-Fi router) and a receiver (laptop). The sender sends 802.11n packets to the receiver, while the receiver continuously receives these packets and records in real-time CSI value. The receiver analyzes the collected CSI values and determines if the human is alive and the number of heart beats and breaths.

KW - Human activity recognition

KW - Signal processing

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M3 - Conference contribution

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T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

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BT - Proceedings of the 8th Wireless of the Students, by the Students, and for the Students Workshop, S3

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Y2 - 3 October 2016 through 7 October 2016

ER -

Shang J, Wu J. Fine-grained vital signs estimation using commercial Wi-Fi devices. In Proceedings of the 8th Wireless of the Students, by the Students, and for the Students Workshop, S3. Association for Computing Machinery. 2016. p. 30-32. 2987360. (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM). doi: 10.1145/2987354.2987360

Fine-grained vital signs estimation using commercial Wi-Fi devices

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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