TY - GEN
T1 - Adaptive time method for fall detection in elderly
AU - Mohammed, Safa Hussein
AU - Fan, Yangyu
AU - Lv, Guoyun
AU - Liu, Shiya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Falls are the largest risk to the health of elderly people. Different researchers have studied threshold algorithms and machine learning for fall detection; however, these methods do not efficiently detect the possibility of falls in the elderly. Recently, sensors have been developed that can observe human joints to determine how prone a person is to fall. However, the use of a single sensor exhibits several drawbacks such as low accuracy, limited information, and a high false alarm rate. Therefore, multiple sensors at the waist, thigh, and ankle were the most comfortable positions for elderly people wearing sensors. In this paper, a hybrid approach to dimension reduction and discrete wavelet transform (DWT) is proposed to extract features from the dataset. A unique approach employing k Nearest Neighbor (KNN) and Support Vector Machine (SVM) was investigated to accurately detect if a person is prone to fall or not. The results after comparative analysis with current methods show a significant increase in accuracy of 94%.
AB - Falls are the largest risk to the health of elderly people. Different researchers have studied threshold algorithms and machine learning for fall detection; however, these methods do not efficiently detect the possibility of falls in the elderly. Recently, sensors have been developed that can observe human joints to determine how prone a person is to fall. However, the use of a single sensor exhibits several drawbacks such as low accuracy, limited information, and a high false alarm rate. Therefore, multiple sensors at the waist, thigh, and ankle were the most comfortable positions for elderly people wearing sensors. In this paper, a hybrid approach to dimension reduction and discrete wavelet transform (DWT) is proposed to extract features from the dataset. A unique approach employing k Nearest Neighbor (KNN) and Support Vector Machine (SVM) was investigated to accurately detect if a person is prone to fall or not. The results after comparative analysis with current methods show a significant increase in accuracy of 94%.
KW - discrete wavelet transform (DWT)
KW - IMU sensor
KW - Principal Component Analysis (PCA)
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85189944254&partnerID=8YFLogxK
U2 - 10.1109/ICRAIE59459.2023.10468387
DO - 10.1109/ICRAIE59459.2023.10468387
M3 - 会议稿件
AN - SCOPUS:85189944254
T3 - 8th International Conference on Recent Advances and Innovations in Engineering: Empowering Computing, Analytics, and Engineering Through Digital Innovation, ICRAIE 2023
BT - 8th International Conference on Recent Advances and Innovations in Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023
Y2 - 2 December 2023 through 3 December 2023
ER -
