Thermal Vision Based Fall Detection via Logical and Data driven Processes

Joseph Rafferty; Javier Medina Quero; Susan Quinn; Catherine Saunders; Idongesit Ekerete; CD Nugent; Jonathan Synnott; Matias Garcia-Constantino

doi:10.1109/BCD.2019.8884820

Thermal Vision Based Fall Detection via Logical and Data driven Processes

Joseph Rafferty, Javier Medina Quero, Susan Quinn, Catherine Saunders, Idongesit Ekerete, CD Nugent, Jonathan Synnott, Matias Garcia-Constantino

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

5 Citations (Scopus)

Abstract

Inadvertent falls can cause serious, and potentially fatal injuries, to at risk individuals. One such community of at-risk individuals is the elderly population where age related complications, such as osteoporosis and dementia, can further increase the incidence and negative impact of such falls. Notably, falls within that community has been identified as the leading cause of injury related preventable death, hospitalization and reduction to quality of life. In such cases, rapid detection of, and reaction, to fall events has shown to be critical to reduce the negative effects of falls within this community. Currently, a range of fall detection solutions exist, however, they have several deficiencies related to the core approach that has been adopted. This study has developed an ensemble of thermal vision-based, big data facilitated, solutions which aim to address some of these deficiencies. An evaluation of these logical and data-driven processes has occurred with the promising results presented within this manuscript. Finally, opportunities future work and real-world evaluation have occurred and are underway.

Original language	English
Title of host publication	2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD)
Publisher	IEEE
Pages	35-40
Number of pages	6
ISBN (Electronic)	978-1-7281-0886-5, 978-1-7281-0885-8
ISBN (Print)	978-1-7281-0887-2
DOIs	https://doi.org/10.1109/BCD.2019.8884820
Publication status	Published (in print/issue) - 31 Oct 2019
Event	2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD). - Honolulu, United States Duration: 29 May 2019 → 31 May 2019 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8884820

Conference

Conference	2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD).
Country/Territory	United States
City	Honolulu
Period	29/05/19 → 31/05/19
Internet address	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8884820

Keywords

Ambient Assistive Living
Assisted Ageing
Big Data
Computer Vision
Convolutional Neural Network
Fall Detection
Healthcare
Internet of Things
Pervasive Computing
Thermal Vision

UN SDGs

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

Access to Document

10.1109/BCD.2019.8884820

Cite this

@inproceedings{182d4de777c343819eb7763472b654db,

title = "Thermal Vision Based Fall Detection via Logical and Data driven Processes",

abstract = "Inadvertent falls can cause serious, and potentially fatal injuries, to at risk individuals. One such community of at-risk individuals is the elderly population where age related complications, such as osteoporosis and dementia, can further increase the incidence and negative impact of such falls. Notably, falls within that community has been identified as the leading cause of injury related preventable death, hospitalization and reduction to quality of life. In such cases, rapid detection of, and reaction, to fall events has shown to be critical to reduce the negative effects of falls within this community. Currently, a range of fall detection solutions exist, however, they have several deficiencies related to the core approach that has been adopted. This study has developed an ensemble of thermal vision-based, big data facilitated, solutions which aim to address some of these deficiencies. An evaluation of these logical and data-driven processes has occurred with the promising results presented within this manuscript. Finally, opportunities future work and real-world evaluation have occurred and are underway.",

keywords = "Ambient Assistive Living, Assisted Ageing, Big Data, Computer Vision, Convolutional Neural Network, Fall Detection, Healthcare, Internet of Things, Pervasive Computing, Thermal Vision",

author = "Joseph Rafferty and Quero, \{Javier Medina\} and Susan Quinn and Catherine Saunders and Idongesit Ekerete and CD Nugent and Jonathan Synnott and Matias Garcia-Constantino",

year = "2019",

month = oct,

day = "31",

doi = "10.1109/BCD.2019.8884820",

language = "English",

isbn = "978-1-7281-0887-2",

pages = "35--40",

booktitle = "2019 IEEE International Conference on Big Data, Cloud Computing, Data Science \& Engineering (BCD)",

publisher = "IEEE",

address = "United States",

note = "2019 IEEE International Conference on Big Data, Cloud Computing, Data Science \& Engineering (BCD). ; Conference date: 29-05-2019 Through 31-05-2019",

url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=8884820",

}

Rafferty, J, Quero, JM, Quinn, S, Saunders, C , Ekerete, I , Nugent, CD , Synnott, J & Garcia-Constantino, M 2019, Thermal Vision Based Fall Detection via Logical and Data driven Processes. in 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD). IEEE, pp. 35-40, 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD)., Honolulu, United States, 29/05/19. https://doi.org/10.1109/BCD.2019.8884820

TY - GEN

T1 - Thermal Vision Based Fall Detection via Logical and Data driven Processes

AU - Rafferty, Joseph

AU - Quero, Javier Medina

AU - Quinn, Susan

AU - Saunders, Catherine

AU - Ekerete, Idongesit

AU - Nugent, CD

AU - Synnott, Jonathan

AU - Garcia-Constantino, Matias

PY - 2019/10/31

Y1 - 2019/10/31

N2 - Inadvertent falls can cause serious, and potentially fatal injuries, to at risk individuals. One such community of at-risk individuals is the elderly population where age related complications, such as osteoporosis and dementia, can further increase the incidence and negative impact of such falls. Notably, falls within that community has been identified as the leading cause of injury related preventable death, hospitalization and reduction to quality of life. In such cases, rapid detection of, and reaction, to fall events has shown to be critical to reduce the negative effects of falls within this community. Currently, a range of fall detection solutions exist, however, they have several deficiencies related to the core approach that has been adopted. This study has developed an ensemble of thermal vision-based, big data facilitated, solutions which aim to address some of these deficiencies. An evaluation of these logical and data-driven processes has occurred with the promising results presented within this manuscript. Finally, opportunities future work and real-world evaluation have occurred and are underway.

AB - Inadvertent falls can cause serious, and potentially fatal injuries, to at risk individuals. One such community of at-risk individuals is the elderly population where age related complications, such as osteoporosis and dementia, can further increase the incidence and negative impact of such falls. Notably, falls within that community has been identified as the leading cause of injury related preventable death, hospitalization and reduction to quality of life. In such cases, rapid detection of, and reaction, to fall events has shown to be critical to reduce the negative effects of falls within this community. Currently, a range of fall detection solutions exist, however, they have several deficiencies related to the core approach that has been adopted. This study has developed an ensemble of thermal vision-based, big data facilitated, solutions which aim to address some of these deficiencies. An evaluation of these logical and data-driven processes has occurred with the promising results presented within this manuscript. Finally, opportunities future work and real-world evaluation have occurred and are underway.

KW - Ambient Assistive Living

KW - Assisted Ageing

KW - Big Data

KW - Computer Vision

KW - Convolutional Neural Network

KW - Fall Detection

KW - Healthcare

KW - Internet of Things

KW - Pervasive Computing

KW - Thermal Vision

UR - https://www.scopus.com/pages/publications/85074977480

U2 - 10.1109/BCD.2019.8884820

DO - 10.1109/BCD.2019.8884820

M3 - Conference contribution

SN - 978-1-7281-0887-2

SP - 35

EP - 40

BT - 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD)

PB - IEEE

T2 - 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD).

Y2 - 29 May 2019 through 31 May 2019

ER -

Thermal Vision Based Fall Detection via Logical and Data driven Processes

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Unobtrusive sensing solutions for home-based monitoring

Cite this

Thermal Vision Based Fall Detection via Logical and Data driven Processes

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Student theses

Unobtrusive sensing solutions for home-based monitoring

Cite this