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

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

2 Citations (Scopus)


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 languageEnglish
Title of host publicationIntelligent human computer interaction
Subtitle of host publication13th International Conference, IHCI 2021, Kent, OH, USA, December 20-22, 2021, Revised selected papers
EditorsJong-Hoon Kim, Madhusudan Singh, Javed Khan, Uma Shanker Tiwary, Marigankar Sur, Dhananjay Singh
Place of PublicationCham, Switzerland
Number of pages10
ISBN (Electronic)9783030984045
ISBN (Print)9783030984038
Publication statusPublished - 2022
EventIHCI: International Conference on Intelligent Human Computer Interaction - Kent, OH, Kent, United States
Duration: 20 Dec 202122 Dec 2021
Conference number: 13

Publication series

NameLecture notes in computer science
ISSN (Print)1611-3349


ConferenceIHCI: International Conference on Intelligent Human Computer Interaction
Abbreviated titleIHCI
Country/TerritoryUnited States


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


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