Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network

被引：0
|
作者：
Qu, Yuanying [1 ]
Wang, Xinheng [1 ]
机构：
[1] Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou, Peoples R China
来源：
2022 HUMAN-CENTERED COGNITIVE SYSTEMS, HCCS | 2022年
基金：
中国国家自然科学基金;
关键词：
acoustic signal processing; feature extraction; pose estimation; convolutional neural networks; multiple signal classification; deep learning; GROUND REACTION FORCE; GAIT; EXTRACTION; SYSTEM;
D O I：
10.1109/HCCS55241.2022.10090383
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Research on activities of daily living (ADL) continues to attract scientists due to the prospection. Walking is a unique biological characteristic which is an indispensable activity. The related research has potential applications in the vast fields or scenes confronted in daily life. Examples include human-computer interaction, behaviour assessment, emergency search and rescue, and healthcare. This paper proposes walking posture classification based on acoustic analysis and the lightweight convolutional neural network. The findings indicate that the classification accuracy can reach 93%.
引用
收藏
页码：39 / 44
页数：6
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