Abstract
Falling accidents cause severe damage to human health, especially to the elderly. Nowadays, smartphones are ubiquitous and widely used around the world, in which rich sensors are embedded. Thus, a smartphone based falling detection system, SmartFDS is proposed in this paper, which can recognize the associated individual's falling behavior and send out an emergent message containing the location for immediate help. Generally, SmartFDS includes offline training phase and online activity recognition phase. Specifically, in training phase, utilizing the embedded accelerometer sensor, falling data for various situations are collected to extract the desired features that can appropriately characterize the falling behavior. Considering that the raw data are intrinsically prone to noise and error, those data are preprocessed by weighted smoothing. Then, 6 time-domain features are elaborated to determine the discriminative features for characterizing falling, and two features, maximum and vertical velocity are selected. Then, 4 different classification algorithms are investigated, and SVM is selected. The prototype of SmartFDS is implemented on Android phones, and can detect falling in real time accurately.
Original language | English |
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Title of host publication | Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 402-405 |
Number of pages | 4 |
ISBN (Electronic) | 9781509058808 |
DOIs | |
Publication status | Published - 2017 May 1 |
Event | 9th IEEE International Conference on Internet of Things, 12th IEEE International Conference on Green Computing and Communications, 9th IEEE International Conference on Cyber, Physical, and Social Computing and 2016 IEEE International Conference on Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016 - Chengdu, China Duration: 2016 Dec 16 → 2016 Dec 19 |
Other
Other | 9th IEEE International Conference on Internet of Things, 12th IEEE International Conference on Green Computing and Communications, 9th IEEE International Conference on Cyber, Physical, and Social Computing and 2016 IEEE International Conference on Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016 |
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Country | China |
City | Chengdu |
Period | 16/12/16 → 16/12/19 |
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Keywords
- Classification
- Falling detection
- Feature selection
- Smartphone
ASJC Scopus subject areas
- Computer Networks and Communications
- Safety, Risk, Reliability and Quality
- Communication
Cite this
SmartFDS : Design and Implementation of Falling Detection System on Smartphones. / Li, Xiao; Wang, Yufeng; Zhou, Qicai; Jin, Qun.
Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 402-405 7917123.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - SmartFDS
T2 - Design and Implementation of Falling Detection System on Smartphones
AU - Li, Xiao
AU - Wang, Yufeng
AU - Zhou, Qicai
AU - Jin, Qun
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Falling accidents cause severe damage to human health, especially to the elderly. Nowadays, smartphones are ubiquitous and widely used around the world, in which rich sensors are embedded. Thus, a smartphone based falling detection system, SmartFDS is proposed in this paper, which can recognize the associated individual's falling behavior and send out an emergent message containing the location for immediate help. Generally, SmartFDS includes offline training phase and online activity recognition phase. Specifically, in training phase, utilizing the embedded accelerometer sensor, falling data for various situations are collected to extract the desired features that can appropriately characterize the falling behavior. Considering that the raw data are intrinsically prone to noise and error, those data are preprocessed by weighted smoothing. Then, 6 time-domain features are elaborated to determine the discriminative features for characterizing falling, and two features, maximum and vertical velocity are selected. Then, 4 different classification algorithms are investigated, and SVM is selected. The prototype of SmartFDS is implemented on Android phones, and can detect falling in real time accurately.
AB - Falling accidents cause severe damage to human health, especially to the elderly. Nowadays, smartphones are ubiquitous and widely used around the world, in which rich sensors are embedded. Thus, a smartphone based falling detection system, SmartFDS is proposed in this paper, which can recognize the associated individual's falling behavior and send out an emergent message containing the location for immediate help. Generally, SmartFDS includes offline training phase and online activity recognition phase. Specifically, in training phase, utilizing the embedded accelerometer sensor, falling data for various situations are collected to extract the desired features that can appropriately characterize the falling behavior. Considering that the raw data are intrinsically prone to noise and error, those data are preprocessed by weighted smoothing. Then, 6 time-domain features are elaborated to determine the discriminative features for characterizing falling, and two features, maximum and vertical velocity are selected. Then, 4 different classification algorithms are investigated, and SVM is selected. The prototype of SmartFDS is implemented on Android phones, and can detect falling in real time accurately.
KW - Classification
KW - Falling detection
KW - Feature selection
KW - Smartphone
UR - http://www.scopus.com/inward/record.url?scp=85020195911&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020195911&partnerID=8YFLogxK
U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.95
DO - 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.95
M3 - Conference contribution
AN - SCOPUS:85020195911
SP - 402
EP - 405
BT - Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -