Sit-to-Stand Detection using Fuzzy Clustering Techniques

Tanvi Banerjee; James M. Keller; Marjorie Skubic; Carmen Abbott

doi:10.1109/FUZZY.2010.5584843

Sit-to-Stand Detection using Fuzzy Clustering Techniques

Tanvi Banerjee, James M. Keller, Marjorie Skubic, Carmen Abbott

Computer Science and Engineering

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

Abstract

The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding promising results.

Original language	American English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010
Publisher	IEEE
ISBN (Electronic)	978-1-4244-6921-5, 978-1-4244-6920-8
ISBN (Print)	978-1-4244-6919-2
DOIs	https://doi.org/10.1109/FUZZY.2010.5584843
State	Published - Sep 23 2010
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - Barcelona, Spain Duration: Jul 18 2010 → Jul 23 2010

Conference

Conference	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
Country/Territory	Spain
City	Barcelona
Period	7/18/10 → 7/23/10

ASJC Scopus Subject Areas

Artificial Intelligence
Computational Theory and Mathematics

Keywords

Government
Fuzzy Rough Set

Disciplines

Bioinformatics
Communication Technology and New Media
Databases and Information Systems
OS and Networks
Science and Technology Studies

Access to Document

10.1109/FUZZY.2010.5584843License: Unspecified

Cite this

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title = "Sit-to-Stand Detection using Fuzzy Clustering Techniques",

abstract = " The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding promising results.",

keywords = "Government, Fuzzy Rough Set",

author = "Tanvi Banerjee and Keller, {James M.} and Marjorie Skubic and Carmen Abbott",

year = "2010",

month = sep,

day = "23",

doi = "10.1109/FUZZY.2010.5584843",

language = "American English",

isbn = "978-1-4244-6919-2",

booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010",

publisher = "IEEE",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

}

TY - GEN

T1 - Sit-to-Stand Detection using Fuzzy Clustering Techniques

AU - Banerjee, Tanvi

AU - Keller, James M.

AU - Skubic, Marjorie

AU - Abbott, Carmen

PY - 2010/9/23

Y1 - 2010/9/23

N2 - The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding promising results.

AB - The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding promising results.

KW - Government

KW - Fuzzy Rough Set

UR - http://www.scopus.com/inward/record.url?scp=78549235083&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78549235083&partnerID=8YFLogxK

UR - https://corescholar.libraries.wright.edu/knoesis/1114

U2 - 10.1109/FUZZY.2010.5584843

DO - 10.1109/FUZZY.2010.5584843

M3 - Conference contribution

SN - 978-1-4244-6919-2

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010

PB - IEEE

T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010

Y2 - 18 July 2010 through 23 July 2010

ER -

Sit-to-Stand Detection using Fuzzy Clustering Techniques

Abstract

Conference

ASJC Scopus Subject Areas

Keywords

Disciplines

Access to Document

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Cite this