Paper Info
Title
Sit-to-Stand Measurement for In-Home Monitoring Using Voxel Analysis
Abstract
We present algorithms to segment the activities of sitting and standing, and identify the regions of sit-to-stand (STS) transitions in a given image sequence. As a means of fall risk assessment, we propose methods to measure STS time using the 3-D modeling of a human body in voxel space as well as ellipse fitting algorithms and image features to capture orientation of the body. The proposed algorithms were tested on ten older adults with ages ranging from 83 to 97. Two techniques in combination yielded the best results, namely the voxel height in conjunction with the ellipse fit. Accurate STS time was computed on various STSs and verified using a marker-based motion capture system. This application can be used as part of a continuous video monitoring system in the homes of older adults and can provide valuable information to help detect fall risk and enable early interventions.
Year
DOI
Venue
2014
10.1109/JBHI.2013.2284404
Biomedical and Health Informatics, IEEE Journal of  
Keywords
Field
DocType
biomedical optical imaging,geriatrics,image segmentation,image sequences,medical image processing,risk management,3D modeling,STS time measurement,continuous video monitoring system,ellipse fied,ellipse fitting algorithms,fall risk assessment,image features,image sequence,in-home monitoring,marker-based motion capture system,older adults,sit-stand measurement,sit-stand transitions,voxel analysis,voxel space,Activity recognition,eldercare technology,ellipse fit,sit-to-stand (STS),voxel
Voxel,Sit to stand,Computer vision,Motion capture,Activity recognition,Pattern recognition,Computer science,Feature (computer vision),Ranging,Artificial intelligence,Sitting,Ellipse
Journal
Volume
Issue
ISSN
18
4
2168-2194
Citations 
PageRank 
References 
4
0.51
10
Authors
4
Name
Order
Citations
PageRank
Tanvi Banerjee18316.41
Marjorie Skubic21045105.36
James M. Keller33201436.69
Carmen Abbott4667.51