TY - GEN
T1 - A wearable device for fall detection elderly people using tri dimensional accelerometer
AU - Kurniawan, A.
AU - Hermawan, A. R.
AU - Purnama, I. K.E.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/20
Y1 - 2017/1/20
N2 - A fall detection device is needed to provide information to paramedics or family members when an elderly is falling. Helping for elderly falling can avoid fatal injuries or loss of life. In order for the falling device comfortably taken by the elderly, we proposed a wearable device that lightweight, using battery for power supply, and a low-energy consumption. Our proposed device consists of: 3-dimensional accelerometer as a sensor, a microcontroller and a communication device. The sensor provides accelerations of elderly body movements. Then, the microcontroller identifies position body and a falling from three-axis accelerations. We use parameter threshold in our proposed fall detection as a method that has success detect 75% in fall forward and 95% in fall backward. The proposed device also has a 100% success in providing information on normal activities, such as: standing or sitting, supine, face down, left and right, while the success rate for the e-health device by cooking hack is 92%.
AB - A fall detection device is needed to provide information to paramedics or family members when an elderly is falling. Helping for elderly falling can avoid fatal injuries or loss of life. In order for the falling device comfortably taken by the elderly, we proposed a wearable device that lightweight, using battery for power supply, and a low-energy consumption. Our proposed device consists of: 3-dimensional accelerometer as a sensor, a microcontroller and a communication device. The sensor provides accelerations of elderly body movements. Then, the microcontroller identifies position body and a falling from three-axis accelerations. We use parameter threshold in our proposed fall detection as a method that has success detect 75% in fall forward and 95% in fall backward. The proposed device also has a 100% success in providing information on normal activities, such as: standing or sitting, supine, face down, left and right, while the success rate for the e-health device by cooking hack is 92%.
KW - 3-D Accelerometer
KW - Fall Detection
KW - Wearable Device
UR - http://www.scopus.com/inward/record.url?scp=85016740703&partnerID=8YFLogxK
U2 - 10.1109/ISITIA.2016.7828740
DO - 10.1109/ISITIA.2016.7828740
M3 - Conference contribution
AN - SCOPUS:85016740703
T3 - Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016: Recent Trends in Intelligent Computational Technologies for Sustainable Energy
SP - 671
EP - 674
BT - Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016
Y2 - 28 July 2016 through 30 July 2016
ER -
