TY - GEN
T1 - Exploring Favorable Positions of Wearable Smart Sensors to Falls Detection
T2 - 3rd International Conference on Smart Grid and Smart Cities, ICSGSC 2019
AU - Kong, Anthony
AU - Tang, Jeff K.T.
AU - Ng, Wai Yan
AU - Li, Jacky K.L.
N1 - Funding Information:
The work described in this paper was partially supported by a grant from the Katie Shu Sui Pui Charitable Trust - Research and Publication Fund (KS 2018/2.12).
Funding Information:
ACKNOWLEDGEMENT The work described in this paper was partially supported by a grant from the Katie Shu Sui Pui Charitable Trust – Research and Publication Fund (KS 2018/2.12).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - With the ageing of the global population, fall detection of elderly become a prominent public health problem. Health service, academia and industry are desirous to develop a robust system for automatic falls detection in elderly's daily life, especially for the elderly who living alone. In this paper, we develop an optimize falls detection algorithm that initially investigates the problems of existing systems, and then collect numerous activities of daily living (ADLs) and fall events dataset by a self-defined method. The algorithm is deployed within a sensor system that uses a triaxial accelerometer and a triaxial gyroscope sensors to detect accidental falls. The aim of this paper is to explore the favorable body location for single-sensor wearable falls detection device to detect the falls for elderly. The developed sensor system is used to experiment on seven different locations of human body. The result is detailed evaluated by measuring sensitivity and specificity of algorithm applied in each experimented sensor locations.
AB - With the ageing of the global population, fall detection of elderly become a prominent public health problem. Health service, academia and industry are desirous to develop a robust system for automatic falls detection in elderly's daily life, especially for the elderly who living alone. In this paper, we develop an optimize falls detection algorithm that initially investigates the problems of existing systems, and then collect numerous activities of daily living (ADLs) and fall events dataset by a self-defined method. The algorithm is deployed within a sensor system that uses a triaxial accelerometer and a triaxial gyroscope sensors to detect accidental falls. The aim of this paper is to explore the favorable body location for single-sensor wearable falls detection device to detect the falls for elderly. The developed sensor system is used to experiment on seven different locations of human body. The result is detailed evaluated by measuring sensitivity and specificity of algorithm applied in each experimented sensor locations.
KW - assisted living
KW - pattern matching
KW - wearable sensor
UR - http://www.scopus.com/inward/record.url?scp=85076098569&partnerID=8YFLogxK
U2 - 10.1109/ICSGSC.2019.00-12
DO - 10.1109/ICSGSC.2019.00-12
M3 - Conference contribution
AN - SCOPUS:85076098569
T3 - Proceedings - 2019 3rd International Conference on Smart Grid and Smart Cities, ICSGSC 2019
SP - 92
EP - 100
BT - Proceedings - 2019 3rd International Conference on Smart Grid and Smart Cities, ICSGSC 2019
Y2 - 25 June 2019 through 28 June 2019
ER -
