Embedded fall and activity monitoring for a wearable ambient assisted living solution for older adults.
With the rapidly increasing over 60 and over 80 age groups in society, greater emphasis will be put on technology to detect emergency situations, such as falls, in order to promote independent living. This paper describes the development and deployment of fall-detection, activity classification and energy expenditure algorithms, deployed in a tele-monitoring system. These algorithms were successfully tested in an end-user trial involving 9 elderly volunteers using the system for 28 days.
Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland. alan.bourke@ul.ie
Alan K
AK
Bourke
Sandra
S
Prescher
Friedrich
F
Koehler
Victor
V
Cionca
Carlos
C
Tavares
Sergi
S
Gomis
Virginia
V
Garcia
John
J
Nelson
D000058:Accidental Falls; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D004734:Energy Metabolism; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000295:instrumentation; D017216:Telemedicine / Q000295:instrumentation / Q000379:methods