Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network

Yuanying Qu; Xinheng Wang

doi:10.1109/HCCS55241.2022.10090383

Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network

Yuanying Qu, Xinheng Wang

Department of Mechatronics and Robotics

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

Research on activities of daily living (ADL) con-Tinues 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 %.

Original language	English
Title of host publication	Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665450416
DOIs	https://doi.org/10.1109/HCCS55241.2022.10090383
Publication status	Published - 2022
Event	2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022 - Shanghai, China Duration: 17 Dec 2022 → 18 Dec 2022

Publication series

Name	Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022

Conference

Conference	2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022
Country/Territory	China
City	Shanghai
Period	17/12/22 → 18/12/22

Keywords

acoustic signal processing
convolutional neural networks
deep learning
feature extraction
multiple signal classification
pose estimation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/HCCS55241.2022.10090383

Cite this

Qu, Y., & Wang, X. (2022). Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network. In Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022 (Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HCCS55241.2022.10090383

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title = "Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network",

abstract = "Research on activities of daily living (ADL) con-Tinues 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 %.",

keywords = "acoustic signal processing, convolutional neural networks, deep learning, feature extraction, multiple signal classification, pose estimation",

author = "Yuanying Qu and Xinheng Wang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022 ; Conference date: 17-12-2022 Through 18-12-2022",

year = "2022",

doi = "10.1109/HCCS55241.2022.10090383",

language = "English",

series = "Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022",

}

Qu, Y & Wang, X 2022, Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network. in Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022. Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022, Shanghai, China, 17/12/22. https://doi.org/10.1109/HCCS55241.2022.10090383

Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network. / Qu, Yuanying; Wang, Xinheng.
Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network

AU - Qu, Yuanying

AU - Wang, Xinheng

PY - 2022

Y1 - 2022

N2 - Research on activities of daily living (ADL) con-Tinues 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 %.

AB - Research on activities of daily living (ADL) con-Tinues 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 %.

KW - acoustic signal processing

KW - convolutional neural networks

KW - deep learning

KW - feature extraction

KW - multiple signal classification

KW - pose estimation

UR - http://www.scopus.com/inward/record.url?scp=85153673192&partnerID=8YFLogxK

U2 - 10.1109/HCCS55241.2022.10090383

DO - 10.1109/HCCS55241.2022.10090383

M3 - Conference Proceeding

AN - SCOPUS:85153673192

T3 - Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022

BT - Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022

Y2 - 17 December 2022 through 18 December 2022

ER -

Qu Y, Wang X. Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network. In Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022). doi: 10.1109/HCCS55241.2022.10090383

Walking Posture Classification via Acoustic Analysis and Convolutional Neural Network

Abstract

Publication series

Conference

Keywords

UN SDGs

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