TY - GEN
T1 - Lightweight real-time fall detection using bidirectional recurrent neural network
AU - Kim, Sangyeon
AU - Lee, Gawon
AU - Kim, Jihie
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/5
Y1 - 2020/12/5
N2 - As the world's population is aging, the home care systems for elderly people have been getting high attention. According to the National Council on Aging, every 11 seconds, an older adult is treated in the emergency room for a fall, and every 19 minutes, an older adult dies from a fall. The number of single households is also increasing with an aging society. In a single household, there is no one to help the elderly when they fall. This could lead to serious problems such as disability or death. In this paper, we propose a lightweight real-time system for fall detection, distinguished from other activities of daily living (ADL). The entire system is divided into a preprocessing and prediction part. With the system, falls and ADLs can be distinguished with more than 92% accuracy which is higher than the existing approach even without any additional resampling method.
AB - As the world's population is aging, the home care systems for elderly people have been getting high attention. According to the National Council on Aging, every 11 seconds, an older adult is treated in the emergency room for a fall, and every 19 minutes, an older adult dies from a fall. The number of single households is also increasing with an aging society. In a single household, there is no one to help the elderly when they fall. This could lead to serious problems such as disability or death. In this paper, we propose a lightweight real-time system for fall detection, distinguished from other activities of daily living (ADL). The entire system is divided into a preprocessing and prediction part. With the system, falls and ADLs can be distinguished with more than 92% accuracy which is higher than the existing approach even without any additional resampling method.
KW - Bidirectional Recurrent Neural Network
KW - Butterworth Loss-pass Filter
KW - Fall Detection
KW - Human Activity Recognition
KW - MobiAct dataset
KW - Real-time Fall Detection
UR - http://www.scopus.com/inward/record.url?scp=85100373351&partnerID=8YFLogxK
U2 - 10.1109/SCISISIS50064.2020.9322735
DO - 10.1109/SCISISIS50064.2020.9322735
M3 - Conference contribution
AN - SCOPUS:85100373351
T3 - 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
BT - 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
Y2 - 5 December 2020 through 8 December 2020
ER -