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Respiration and Activity Detection based on Passive Radio Sensing in Home Environments

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Respiration and Activity Detection based on Passive Radio Sensing in Home Environments. / Chen, Qingchao; Liu, Yang; Tan, Bo; Woodbridge, Karl; Chetty, Kevin.

julkaisussa: IEEE Access, Vuosikerta 8, 13.01.2020, s. 12426 - 12437.

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Harvard

Chen, Q, Liu, Y, Tan, B, Woodbridge, K & Chetty, K 2020, 'Respiration and Activity Detection based on Passive Radio Sensing in Home Environments', IEEE Access, Vuosikerta. 8, Sivut 12426 - 12437. https://doi.org/10.1109/ACCESS.2020.2966126

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Vancouver

Author

Chen, Qingchao ; Liu, Yang ; Tan, Bo ; Woodbridge, Karl ; Chetty, Kevin. / Respiration and Activity Detection based on Passive Radio Sensing in Home Environments. Julkaisussa: IEEE Access. 2020 ; Vuosikerta 8. Sivut 12426 - 12437.

Bibtex - Lataa

@article{bfeeb0592d8d431f96e4a292e8169ae2,
title = "Respiration and Activity Detection based on Passive Radio Sensing in Home Environments",
abstract = "The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling.",
keywords = "Machine learning, Deep Transfer Networks, Opportunistic Wireless Networks, Signs-of-life Detection, Human Activity Monitoring, Micro-Doppler Signature, Phase-Sensitive detection",
author = "Qingchao Chen and Yang Liu and Bo Tan and Karl Woodbridge and Kevin Chetty",
year = "2020",
month = "1",
day = "13",
doi = "10.1109/ACCESS.2020.2966126",
language = "English",
volume = "8",
pages = "12426 -- 12437",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Respiration and Activity Detection based on Passive Radio Sensing in Home Environments

AU - Chen, Qingchao

AU - Liu, Yang

AU - Tan, Bo

AU - Woodbridge, Karl

AU - Chetty, Kevin

PY - 2020/1/13

Y1 - 2020/1/13

N2 - The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling.

AB - The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling.

KW - Machine learning

KW - Deep Transfer Networks

KW - Opportunistic Wireless Networks

KW - Signs-of-life Detection

KW - Human Activity Monitoring

KW - Micro-Doppler Signature

KW - Phase-Sensitive detection

U2 - 10.1109/ACCESS.2020.2966126

DO - 10.1109/ACCESS.2020.2966126

M3 - Article

VL - 8

SP - 12426

EP - 12437

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -