Automated human physical function measurement using constrained high dispersal network with SVM-linear

Dan Meng; Guitao Cao; Xinyu Song; Weiting Chen; Wenming Cao

doi:10.1109/BIBM.2016.7822747

Automated human physical function measurement using constrained high dispersal network with SVM-linear

Dan Meng
, Guitao Cao^*
, Xinyu Song
, Weiting Chen
, Wenming Cao

^*Corresponding author for this work

Software Engineering Institute

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

1 Scopus citations

Abstract

Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and coped with Support Vector Machines (SVM) to measure human physical function. The proposed method can catch the most representative features of depth images belonging to different actions and statuses. We analyze the representation efficiency of hand-crafted features (HOG features, and LBP features), deep learning features (CNN features, and PCANet features) and our proposed deep learning features separately in order to validate the efficiency and accuracy of our proposed method. The results show superior performance of 85.19% on 3840 samples (three actions, each with four different statuses, and every status contains sixteen sequences) when the proposed deep features combined with SVM.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1520-1526
Number of pages	7
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822747
State	Published - 17 Jan 2017
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Keywords

Deep learning
High dispersal
Local normalization
Multi-scale feature
PCA lter
Physical function measurement
SVM

UN SDGs

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

Access to Document

10.1109/BIBM.2016.7822747

Cite this

Meng, D., Cao, G., Song, X., Chen, W., & Cao, W. (2017). Automated human physical function measurement using constrained high dispersal network with SVM-linear. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1520-1526). Article 7822747 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822747

Meng, Dan ; Cao, Guitao ; Song, Xinyu et al. / Automated human physical function measurement using constrained high dispersal network with SVM-linear. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1520-1526 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{0b564ed9a0594c53b13cc12eacdf8890,

title = "Automated human physical function measurement using constrained high dispersal network with SVM-linear",

abstract = "Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and coped with Support Vector Machines (SVM) to measure human physical function. The proposed method can catch the most representative features of depth images belonging to different actions and statuses. We analyze the representation efficiency of hand-crafted features (HOG features, and LBP features), deep learning features (CNN features, and PCANet features) and our proposed deep learning features separately in order to validate the efficiency and accuracy of our proposed method. The results show superior performance of 85.19\% on 3840 samples (three actions, each with four different statuses, and every status contains sixteen sequences) when the proposed deep features combined with SVM.",

keywords = "Deep learning, High dispersal, Local normalization, Multi-scale feature, PCA lter, Physical function measurement, SVM",

author = "Dan Meng and Guitao Cao and Xinyu Song and Weiting Chen and Wenming Cao",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822747",

language = "英语",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

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

pages = "1520--1526",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, \{Xiaohua Tony\} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

address = "美国",

}

Meng, D, Cao, G, Song, X, Chen, W & Cao, W 2017, Automated human physical function measurement using constrained high dispersal network with SVM-linear. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822747, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1520-1526, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822747

Automated human physical function measurement using constrained high dispersal network with SVM-linear. / Meng, Dan; Cao, Guitao; Song, Xinyu et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1520-1526 7822747 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

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

TY - GEN

T1 - Automated human physical function measurement using constrained high dispersal network with SVM-linear

AU - Meng, Dan

AU - Cao, Guitao

AU - Song, Xinyu

AU - Chen, Weiting

AU - Cao, Wenming

PY - 2017/1/17

Y1 - 2017/1/17

N2 - Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and coped with Support Vector Machines (SVM) to measure human physical function. The proposed method can catch the most representative features of depth images belonging to different actions and statuses. We analyze the representation efficiency of hand-crafted features (HOG features, and LBP features), deep learning features (CNN features, and PCANet features) and our proposed deep learning features separately in order to validate the efficiency and accuracy of our proposed method. The results show superior performance of 85.19% on 3840 samples (three actions, each with four different statuses, and every status contains sixteen sequences) when the proposed deep features combined with SVM.

AB - Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and coped with Support Vector Machines (SVM) to measure human physical function. The proposed method can catch the most representative features of depth images belonging to different actions and statuses. We analyze the representation efficiency of hand-crafted features (HOG features, and LBP features), deep learning features (CNN features, and PCANet features) and our proposed deep learning features separately in order to validate the efficiency and accuracy of our proposed method. The results show superior performance of 85.19% on 3840 samples (three actions, each with four different statuses, and every status contains sixteen sequences) when the proposed deep features combined with SVM.

KW - Deep learning

KW - High dispersal

KW - Local normalization

KW - Multi-scale feature

KW - PCA lter

KW - Physical function measurement

KW - SVM

UR - https://www.scopus.com/pages/publications/85013287865

U2 - 10.1109/BIBM.2016.7822747

DO - 10.1109/BIBM.2016.7822747

M3 - 会议稿件

AN - SCOPUS:85013287865

T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 1520

EP - 1526

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

A2 - Burrage, Kevin

A2 - Zhu, Qian

A2 - Liu, Yunlong

A2 - Tian, Tianhai

A2 - Wang, Yadong

A2 - Hu, Xiaohua Tony

A2 - Jiang, Qinghua

A2 - Song, Jiangning

A2 - Morishita, Shinichi

A2 - Burrage, Kevin

A2 - Wang, Guohua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Y2 - 15 December 2016 through 18 December 2016

ER -

Meng D, Cao G, Song X, Chen W, Cao W. Automated human physical function measurement using constrained high dispersal network with SVM-linear. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1520-1526. 7822747. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822747

Automated human physical function measurement using constrained high dispersal network with SVM-linear

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this