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

Wright State University

Research output: Contribution to conference › Presentation

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
DOIs	https://doi.org/10.1109/FUZZY.2010.5584843
State	Published - Jul 1 2010
Event	2010 IEEE International Conference on Fuzzy Systems - Duration: Jul 1 2010 → …

Conference

Conference	2010 IEEE International Conference on Fuzzy Systems
Period	7/1/10 → …

Disciplines

Bioinformatics
Communication
Communication Technology and New Media
Computer Sciences
Databases and Information Systems
Life Sciences
OS and Networks
Physical Sciences and Mathematics
Science and Technology Studies
Social and Behavioral Sciences

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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.",

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

year = "2010",

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

AU - Banerjee, Tanvi

AU - Keller, James M.

AU - Skubic, Marjorie

AU - Abbott, Carmen

PY - 2010/7/1

Y1 - 2010/7/1

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.

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

U2 - 10.1109/FUZZY.2010.5584843

DO - 10.1109/FUZZY.2010.5584843

M3 - Presentation

T2 - 2010 IEEE International Conference on Fuzzy Systems

Y2 - 1 July 2010

ER -

Sit-to-Stand Detection using Fuzzy Clustering Techniques

Abstract

Conference

Disciplines

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