TY - GEN
T1 - Device-free non-invasive front-door event classification algorithm for forget event detection using binary sensors in the smart house
AU - Gochoo, Munkhjargal
AU - Tan, Tan Hsu
AU - Jean, Fu Rong
AU - Huang, Shih Chia
AU - Kuo, Sy Yen
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - Many elderly persons prefer to stay alone in a single-resident house for seeking an independent life and reducing the cost of health care. However, the independent life cannot be maintained if the resident develops dementia. Thus, an early detection of dementia is essential for the elderly to extend their independent lifetime. One of the early symptoms of dementia is forgetting something when the person leaves the house. In this study, we introduce a novel front-door events (exit, enter, visitor, other, and brief-return-and-exit (BRE)) and their classification scheme that validated by using open datasets (n = 10) collected from ten single-resident testbeds by anonymous binary sensors. BRE events occur when four consecutive events (exit-enter-exit-enter) happen in some certain time intervals (t1, t2, and t3), and some of them may be the forget events. Each testbed had one older adult (aged 73 years and over) during the experimental period (µ = 583.1 ± 297.3 days). The algorithm automatically classifies the resident's front-door events and ignores visitor's entrance and exit events. The experimental results reveal the significance of the ti parameters for the number of BRE events. Since BRE events may include forget events, the proposed algorithm could be a useful tool for the forget event detection.
AB - Many elderly persons prefer to stay alone in a single-resident house for seeking an independent life and reducing the cost of health care. However, the independent life cannot be maintained if the resident develops dementia. Thus, an early detection of dementia is essential for the elderly to extend their independent lifetime. One of the early symptoms of dementia is forgetting something when the person leaves the house. In this study, we introduce a novel front-door events (exit, enter, visitor, other, and brief-return-and-exit (BRE)) and their classification scheme that validated by using open datasets (n = 10) collected from ten single-resident testbeds by anonymous binary sensors. BRE events occur when four consecutive events (exit-enter-exit-enter) happen in some certain time intervals (t1, t2, and t3), and some of them may be the forget events. Each testbed had one older adult (aged 73 years and over) during the experimental period (µ = 583.1 ± 297.3 days). The algorithm automatically classifies the resident's front-door events and ignores visitor's entrance and exit events. The experimental results reveal the significance of the ti parameters for the number of BRE events. Since BRE events may include forget events, the proposed algorithm could be a useful tool for the forget event detection.
KW - Binary sensors
KW - Device-free
KW - Forget event detection
KW - Front-door events
KW - Non-invasive
UR - http://www.scopus.com/inward/record.url?scp=85044412725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044412725&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8122638
DO - 10.1109/SMC.2017.8122638
M3 - Conference contribution
AN - SCOPUS:85044412725
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 405
EP - 409
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -