Title :

A Fall Posture Classification and Recognition Method Based on Wavelet Packet Transform and Support Vector Machine

Author(s) :

Zhang, Q. Y. [Auteur]
Tao, J. [Auteur]
Sun, Q. L. [Auteur]
Zeng, Xianyi [Auteur]
École nationale supérieure des arts et industries textiles [ENSAIT]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Dehmer, M. [Auteur]
Zhou, Q. [Auteur]

Journal title :

Applied Sciences

Abbreviated title :

Appl. Sci.-Basel

Volume number :

11

Pages :

-

Publication date :

2021-06-18

ISSN :

2076-3417

English keyword(s) :

random forest
support vector machine
wavelet packet transform
recognition
classification
falling posture

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

An accidental fall seriously threatens the health and safety of the elderly. The injuries caused by a fall have a lot to do with the different postures during the fall. Therefore, recognizing the posture of falling is ...
Show more >An accidental fall seriously threatens the health and safety of the elderly. The injuries caused by a fall have a lot to do with the different postures during the fall. Therefore, recognizing the posture of falling is essential for the rescue and care of the elderly. In this paper, a novel method was proposed to improve the classification and recognition accuracy of fall postures. Firstly, the wavelet packet transform was used to extract multiple features from sample data. Secondly, random forest was used to evaluate the importance of the extracted features and obtain effective features through screening. Finally, the support vector machine classifier based on the linear kernel function was used to realize the falling posture recognition. The experiment results on “Simulated Falls and Daily Living Activities Data Set” show that the proposed method can distinguish different types of fall postures and achieve 99% classification accuracy.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T11:48:11Z
2024-03-21T09:11:40Z