Titre :

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

Auteur(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]

Titre de la revue :

Applied Sciences

Nom court de la revue :

Appl. Sci.-Basel

Numéro :

11

Pagination :

-

Date de publication :

2021-06-18

ISSN :

2076-3417

Mot(s)-clé(s) en anglais :

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

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Date de dépôt :

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