A Smart Wearable Fall Detection System for Firefighters Using V-RNN

Xiaoqing Chai; Boon-Giin Lee; Matthew Pike; Renjie Wu; Wan-Young Chung

doi:10.1007/978-3-030-98404-5_12

A Smart Wearable Fall Detection System for Firefighters Using V-RNN

Xiaoqing Chai, Boon-Giin Lee, Matthew Pike, Renjie Wu, Wan-Young Chung

School of Computer Science

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

2 Citations (Scopus)

Abstract

Falling is one of the leading causes of death among firefighters in China. Fall detection systems (FDS) have yet to be deployed in firefighting applications in China, negatively impacting the safety of firefighters in the fireground. Despite many studies exploring FDSs, few have explored the application of multiple sensors, or applications outside of geriatric healthcare. This study proposes a smart wearable FDS for detecting firefighter falls by incorporating motion sensors in nine different positions on the firefighter’s personal protective clothing (PPC). The firefighter’s fall activities are detected by a proposed RNN model combined with a Boyer-Moore Voting (BMV) Algorithm (V-RNN) and a fall alert can be issued at an early phase. The results indicated that the proposed FDS with optimized parameters achieves 97.86% and 98.20% in sensitivity and accuracy, respectively.

Original language	English
Title of host publication	Intelligent human computer interaction
Subtitle of host publication	13th International Conference, IHCI 2021, Kent, OH, USA, December 20-22, 2021, Revised selected papers
Editors	Jong-Hoon Kim, Madhusudan Singh, Javed Khan, Uma Shanker Tiwary, Marigankar Sur, Dhananjay Singh
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	128-137
Number of pages	10
Volume	13184
ISBN (Electronic)	9783030984045
ISBN (Print)	9783030984038
DOIs	https://doi.org/10.1007/978-3-030-98404-5_12
Publication status	Published - 2022
Event	IHCI: International Conference on Intelligent Human Computer Interaction - Kent, OH, Kent, United States Duration: 20 Dec 2021 → 22 Dec 2021 Conference number: 13

Publication series

Name	Lecture notes in computer science
Volume	13184
ISSN (Print)	1611-3349

Conference

Conference	IHCI: International Conference on Intelligent Human Computer Interaction
Abbreviated title	IHCI
Country/Territory	United States
City	Kent
Period	20/12/21 → 22/12/21

Keywords

Fall Detection System (FDS)
Personal Protective Clothing (PPC)
Inertial Measurement Unit (IMU)
Recurrent Neural Network (RNN)

Access to Document

10.1007/978-3-030-98404-5_12

Cite this

Chai, X., Lee, B.-G., Pike, M., Wu, R., & Chung, W.-Y. (2022). A Smart Wearable Fall Detection System for Firefighters Using V-RNN. In J.-H. Kim, M. Singh, J. Khan, U. S. Tiwary, M. Sur, & D. Singh (Eds.), Intelligent human computer interaction: 13th International Conference, IHCI 2021, Kent, OH, USA, December 20-22, 2021, Revised selected papers (Vol. 13184, pp. 128-137). (Lecture notes in computer science; Vol. 13184). Springer. https://doi.org/10.1007/978-3-030-98404-5_12

Chai, Xiaoqing ; Lee, Boon-Giin ; Pike, Matthew et al. / A Smart Wearable Fall Detection System for Firefighters Using V-RNN. Intelligent human computer interaction: 13th International Conference, IHCI 2021, Kent, OH, USA, December 20-22, 2021, Revised selected papers. editor / Jong-Hoon Kim ; Madhusudan Singh ; Javed Khan ; Uma Shanker Tiwary ; Marigankar Sur ; Dhananjay Singh. Vol. 13184 Cham, Switzerland : Springer, 2022. pp. 128-137 (Lecture notes in computer science).

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title = "A Smart Wearable Fall Detection System for Firefighters Using V-RNN",

abstract = "Falling is one of the leading causes of death among firefighters in China. Fall detection systems (FDS) have yet to be deployed in firefighting applications in China, negatively impacting the safety of firefighters in the fireground. Despite many studies exploring FDSs, few have explored the application of multiple sensors, or applications outside of geriatric healthcare. This study proposes a smart wearable FDS for detecting firefighter falls by incorporating motion sensors in nine different positions on the firefighter{\textquoteright}s personal protective clothing (PPC). The firefighter{\textquoteright}s fall activities are detected by a proposed RNN model combined with a Boyer-Moore Voting (BMV) Algorithm (V-RNN) and a fall alert can be issued at an early phase. The results indicated that the proposed FDS with optimized parameters achieves 97.86% and 98.20% in sensitivity and accuracy, respectively.",

keywords = "Fall Detection System (FDS), Personal Protective Clothing (PPC), Inertial Measurement Unit (IMU), Recurrent Neural Network (RNN)",

author = "Xiaoqing Chai and Boon-Giin Lee and Matthew Pike and Renjie Wu and Wan-Young Chung",

note = "This research was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ21F020024. This research was also funded by a National Research Foundation of Korea (NRF) grant (2019K1A3A1A05088484) funded by the Korean Government (MIST).; IHCI: International Conference on Intelligent Human Computer Interaction, IHCI ; Conference date: 20-12-2021 Through 22-12-2021",

year = "2022",

doi = "10.1007/978-3-030-98404-5_12",

language = "English",

isbn = "9783030984038",

volume = "13184",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "128--137",

editor = "Jong-Hoon Kim and Madhusudan Singh and Javed Khan and Tiwary, {Uma Shanker} and Marigankar Sur and Dhananjay Singh",

booktitle = "Intelligent human computer interaction",

}

Chai, X , Lee, B-G , Pike, M, Wu, R & Chung, W-Y 2022, A Smart Wearable Fall Detection System for Firefighters Using V-RNN. in J-H Kim, M Singh, J Khan, US Tiwary, M Sur & D Singh (eds), Intelligent human computer interaction: 13th International Conference, IHCI 2021, Kent, OH, USA, December 20-22, 2021, Revised selected papers. vol. 13184, Lecture notes in computer science, vol. 13184, Springer, Cham, Switzerland, pp. 128-137, IHCI: International Conference on Intelligent Human Computer Interaction, Kent, United States, 20/12/21. https://doi.org/10.1007/978-3-030-98404-5_12

A Smart Wearable Fall Detection System for Firefighters Using V-RNN. / Chai, Xiaoqing ; Lee, Boon-Giin ; Pike, Matthew et al.
Intelligent human computer interaction: 13th International Conference, IHCI 2021, Kent, OH, USA, December 20-22, 2021, Revised selected papers. ed. / Jong-Hoon Kim; Madhusudan Singh; Javed Khan; Uma Shanker Tiwary; Marigankar Sur; Dhananjay Singh. Vol. 13184 Cham, Switzerland: Springer, 2022. p. 128-137 (Lecture notes in computer science; Vol. 13184).

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

TY - CHAP

T1 - A Smart Wearable Fall Detection System for Firefighters Using V-RNN

AU - Chai, Xiaoqing

AU - Lee, Boon-Giin

AU - Pike, Matthew

AU - Wu, Renjie

AU - Chung, Wan-Young

N1 - Conference code: 13

PY - 2022

Y1 - 2022

N2 - Falling is one of the leading causes of death among firefighters in China. Fall detection systems (FDS) have yet to be deployed in firefighting applications in China, negatively impacting the safety of firefighters in the fireground. Despite many studies exploring FDSs, few have explored the application of multiple sensors, or applications outside of geriatric healthcare. This study proposes a smart wearable FDS for detecting firefighter falls by incorporating motion sensors in nine different positions on the firefighter’s personal protective clothing (PPC). The firefighter’s fall activities are detected by a proposed RNN model combined with a Boyer-Moore Voting (BMV) Algorithm (V-RNN) and a fall alert can be issued at an early phase. The results indicated that the proposed FDS with optimized parameters achieves 97.86% and 98.20% in sensitivity and accuracy, respectively.

AB - Falling is one of the leading causes of death among firefighters in China. Fall detection systems (FDS) have yet to be deployed in firefighting applications in China, negatively impacting the safety of firefighters in the fireground. Despite many studies exploring FDSs, few have explored the application of multiple sensors, or applications outside of geriatric healthcare. This study proposes a smart wearable FDS for detecting firefighter falls by incorporating motion sensors in nine different positions on the firefighter’s personal protective clothing (PPC). The firefighter’s fall activities are detected by a proposed RNN model combined with a Boyer-Moore Voting (BMV) Algorithm (V-RNN) and a fall alert can be issued at an early phase. The results indicated that the proposed FDS with optimized parameters achieves 97.86% and 98.20% in sensitivity and accuracy, respectively.

KW - Fall Detection System (FDS)

KW - Personal Protective Clothing (PPC)

KW - Inertial Measurement Unit (IMU)

KW - Recurrent Neural Network (RNN)

UR - https://doi.org/10.1007/978-3-030-98404-5_12

U2 - 10.1007/978-3-030-98404-5_12

DO - 10.1007/978-3-030-98404-5_12

M3 - Book Chapter

SN - 9783030984038

VL - 13184

T3 - Lecture notes in computer science

SP - 128

EP - 137

BT - Intelligent human computer interaction

A2 - Kim, Jong-Hoon

A2 - Singh, Madhusudan

A2 - Khan, Javed

A2 - Tiwary, Uma Shanker

A2 - Sur, Marigankar

A2 - Singh, Dhananjay

PB - Springer

CY - Cham, Switzerland

T2 - IHCI: International Conference on Intelligent Human Computer Interaction

Y2 - 20 December 2021 through 22 December 2021

ER -

Chai X , Lee BG , Pike M, Wu R, Chung WY. A Smart Wearable Fall Detection System for Firefighters Using V-RNN. In Kim JH, Singh M, Khan J, Tiwary US, Sur M, Singh D, editors, Intelligent human computer interaction: 13th International Conference, IHCI 2021, Kent, OH, USA, December 20-22, 2021, Revised selected papers. Vol. 13184. Cham, Switzerland: Springer. 2022. p. 128-137. (Lecture notes in computer science). doi: 10.1007/978-3-030-98404-5_12

A Smart Wearable Fall Detection System for Firefighters Using V-RNN

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Keywords

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