TY - GEN
T1 - Fall Prediction Based on Head-Mounted IMU Sensor System
AU - Lin, Fang Yi
AU - Lee, Po Ting
AU - Ho, Yuan Hao
AU - Sung, Pi-Shan
AU - Chen, Peng-Ting
AU - Lin, Chih Lung
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the Ministry of Science and Technology of the Republic of China, Taiwan, under Projects of MOST 109-2224-E-006-001-for its funding support.
Funding Information:
This work was supported by the Ministry of Science and Technology of the Republic of China, Taiwan, under Projects of MOST 109-2224-E-006-001- for its funding support.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Owing to the danger of falls, many fall detection systems have been proposed. However, fall detection cannot avoid the injuries and discover the falls in advance. This work proposes the algorithm of the fall prediction, which alarms before the impact from the fall. In order to identify the forecast of the fall, this algorithm, based on the preset threshold, analyzes the inertial information from the accelerometer and the gyroscope. In the experiment, 5 subjects carried out the simulated falls and the daily activities, which were predicted correctly by the proposed algorithm with the sensitivity of 81.4% and accuracy of 79.4%. Moreover, the time interval between the alarm and the impact was 0.287 seconds in average from the experiment.
AB - Owing to the danger of falls, many fall detection systems have been proposed. However, fall detection cannot avoid the injuries and discover the falls in advance. This work proposes the algorithm of the fall prediction, which alarms before the impact from the fall. In order to identify the forecast of the fall, this algorithm, based on the preset threshold, analyzes the inertial information from the accelerometer and the gyroscope. In the experiment, 5 subjects carried out the simulated falls and the daily activities, which were predicted correctly by the proposed algorithm with the sensitivity of 81.4% and accuracy of 79.4%. Moreover, the time interval between the alarm and the impact was 0.287 seconds in average from the experiment.
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U2 - 10.1109/GCCE56475.2022.10014374
DO - 10.1109/GCCE56475.2022.10014374
M3 - Conference contribution
AN - SCOPUS:85147245473
T3 - GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics
SP - 341
EP - 343
BT - GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE Global Conference on Consumer Electronics, GCCE 2022
Y2 - 18 October 2022 through 21 October 2022
ER -