Fall detection and human activity classification using wearable sensors and compressed sensing

Oussama Kerdjidj , Naeem Ramzan, Khalida Ghanem, Abbes Amira, Fatima Chouireb

Research output: Contribution to journalArticle

Abstract

The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different activities of daily living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8% of accuracy.
LanguageEnglish
Pages1-13
Number of pages13
JournalJournal of Ambient Intelligence and Humanized Computing
Early online date31 Jan 2019
StateE-pub ahead of print - 31 Jan 2019

Fingerprint

Compressed sensing
Accelerometers
Bone
Gyroscopes
Energy utilization
Health
Wearable sensors

Keywords

  • Fall detection
  • Human activity
  • Wearable sensors
  • Compressed sensing
  • Classification

Cite this

@article{1225d64dfae646f1bd7018a5133cb97a,
title = "Fall detection and human activity classification using wearable sensors and compressed sensing",
abstract = "The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different activities of daily living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8\{%} of accuracy.",
keywords = "Fall detection, Human activity, Wearable sensors, Compressed sensing, Classification",
author = "Oussama Kerdjidj and Naeem Ramzan and Khalida Ghanem and Abbes Amira and Fatima Chouireb",
year = "2019",
month = "1",
day = "31",
language = "English",
pages = "1--13",
journal = "Journal of Ambient Intelligence and Humanized Computing",
issn = "1868-5137",
publisher = "Springer International Publishing AG",

}

Fall detection and human activity classification using wearable sensors and compressed sensing. / Kerdjidj , Oussama; Ramzan, Naeem; Ghanem, Khalida ; Amira, Abbes; Chouireb, Fatima .

In: Journal of Ambient Intelligence and Humanized Computing, 31.01.2019, p. 1-13.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Fall detection and human activity classification using wearable sensors and compressed sensing

AU - Kerdjidj ,Oussama

AU - Ramzan,Naeem

AU - Ghanem,Khalida

AU - Amira,Abbes

AU - Chouireb,Fatima

PY - 2019/1/31

Y1 - 2019/1/31

N2 - The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different activities of daily living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8% of accuracy.

AB - The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different activities of daily living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8% of accuracy.

KW - Fall detection

KW - Human activity

KW - Wearable sensors

KW - Compressed sensing

KW - Classification

M3 - Article

SP - 1

EP - 13

JO - Journal of Ambient Intelligence and Humanized Computing

T2 - Journal of Ambient Intelligence and Humanized Computing

JF - Journal of Ambient Intelligence and Humanized Computing

SN - 1868-5137

ER -