Intelligent fall detection with wearable IoT

Farhad Ahamed; Seyed Shahrestani; Hon Cheung

doi:10.1007/978-3-030-22354-0_35

Intelligent fall detection with wearable IoT

Farhad Ahamed, Seyed Shahrestani, Hon Cheung

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

8 Citations (Scopus)

Abstract

Falls of older adults is a significant concern for themselves and caregivers as most of the times a fall leads to serious physical injuries. In the age of the Internet of things (IoT), connected smart homes and monitoring services have opened up opportunities for quality of life for the older adults. Detecting falls with wearable IoT devices can provide peace of mind for older adults and caregivers. Accelerometer based fall detection is investigated in this paper. Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) based Deep Learning network is applied to detect fall. LSTM network provides good accuracy based on the experiment. This experiment provides a promising indication that IoT-based fall monitoring can assure post-fall procedures to older adults and caregivers and this can increase the safety level and well-being of the older adults.

Original language	English
Title of host publication	Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), 3-5 July 2019, Sydney, N.S.W.
Publisher	Springer Nature
Pages	391-401
Number of pages	11
ISBN (Print)	9783030223533
DOIs	https://doi.org/10.1007/978-3-030-22354-0_35
Publication status	Published - 2020
Event	International Conference on Complex_Intelligent_and Software Intensive Systems - Duration: 3 Jul 2019 → …

Publication series

Name
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Complex_Intelligent_and Software Intensive Systems
Period	3/07/19 → …

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Internet of things
falls (accidents) in old age
intelligent agents (computer software)
older people
wearable technology

Access to Document

10.1007/978-3-030-22354-0_35

Cite this

Ahamed, F., Shahrestani, S., & Cheung, H. (2020). Intelligent fall detection with wearable IoT. In Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), 3-5 July 2019, Sydney, N.S.W. (pp. 391-401). Springer Nature. https://doi.org/10.1007/978-3-030-22354-0_35

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title = "Intelligent fall detection with wearable IoT",

abstract = "Falls of older adults is a significant concern for themselves and caregivers as most of the times a fall leads to serious physical injuries. In the age of the Internet of things (IoT), connected smart homes and monitoring services have opened up opportunities for quality of life for the older adults. Detecting falls with wearable IoT devices can provide peace of mind for older adults and caregivers. Accelerometer based fall detection is investigated in this paper. Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) based Deep Learning network is applied to detect fall. LSTM network provides good accuracy based on the experiment. This experiment provides a promising indication that IoT-based fall monitoring can assure post-fall procedures to older adults and caregivers and this can increase the safety level and well-being of the older adults.",

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author = "Farhad Ahamed and Seyed Shahrestani and Hon Cheung",

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doi = "10.1007/978-3-030-22354-0_35",

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Ahamed, F , Shahrestani, S & Cheung, H 2020, Intelligent fall detection with wearable IoT. in Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), 3-5 July 2019, Sydney, N.S.W.. Springer Nature, pp. 391-401, International Conference on Complex_Intelligent_and Software Intensive Systems, 3/07/19. https://doi.org/10.1007/978-3-030-22354-0_35

Intelligent fall detection with wearable IoT. / Ahamed, Farhad ; Shahrestani, Seyed; Cheung, Hon.
Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), 3-5 July 2019, Sydney, N.S.W.. Springer Nature, 2020. p. 391-401.

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

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AU - Ahamed, Farhad

AU - Shahrestani, Seyed

AU - Cheung, Hon

PY - 2020

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N2 - Falls of older adults is a significant concern for themselves and caregivers as most of the times a fall leads to serious physical injuries. In the age of the Internet of things (IoT), connected smart homes and monitoring services have opened up opportunities for quality of life for the older adults. Detecting falls with wearable IoT devices can provide peace of mind for older adults and caregivers. Accelerometer based fall detection is investigated in this paper. Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) based Deep Learning network is applied to detect fall. LSTM network provides good accuracy based on the experiment. This experiment provides a promising indication that IoT-based fall monitoring can assure post-fall procedures to older adults and caregivers and this can increase the safety level and well-being of the older adults.

AB - Falls of older adults is a significant concern for themselves and caregivers as most of the times a fall leads to serious physical injuries. In the age of the Internet of things (IoT), connected smart homes and monitoring services have opened up opportunities for quality of life for the older adults. Detecting falls with wearable IoT devices can provide peace of mind for older adults and caregivers. Accelerometer based fall detection is investigated in this paper. Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) based Deep Learning network is applied to detect fall. LSTM network provides good accuracy based on the experiment. This experiment provides a promising indication that IoT-based fall monitoring can assure post-fall procedures to older adults and caregivers and this can increase the safety level and well-being of the older adults.

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KW - intelligent agents (computer software)

KW - older people

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Intelligent fall detection with wearable IoT

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