Abstract
Quantitative measures of the space an individual can reach is essential for tracking the progression of a disease and the effects of therapeutic intervention. The reachable workspace can be used to track an individuals’ ability to perform activities of daily living, such as feeding and grooming. There are few methods for quantifying upper limb performance, none of which are able to generate a reachable workspace volume from motion capture data. We introduce a method to estimate the reachable workspace volume for an individual by capturing their observed joint limits using a low cost depth camera. This method is then tested on seven individuals with varying upper limb performance. Based on these initial trials, we found that the reachable workspace volume decreased as muscular impairment increased. This shows the potential for this method to be used as a quantitative clinical assessment tool.
| Original language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 570-583 |
| Number of pages | 14 |
| Volume | 8927 |
| ISBN (Print) | 9783319161983 |
| DOIs | |
| State | Published - 2015 |
| Event | 13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: Sep 6 2014 → Sep 12 2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8927 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 13th European Conference on Computer Vision, ECCV 2014 |
|---|---|
| Country | Switzerland |
| City | Zurich |
| Period | 9/6/14 → 9/12/14 |
Fingerprint
Keywords
- Assessment
- Diagnosis
- Functional workspace
- Goniometry
- Kinect
- Muscular dystrophy
- Rehabilitation
- Skeletal Modelling
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Calculating reachable workspace volume for use in quantitative medicine. / Matthew, Robert Peter; Kurillo, Gregorij; Han, Jay J.; Bajcsy, Ruzena.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8927 Springer Verlag, 2015. p. 570-583 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8927).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Calculating reachable workspace volume for use in quantitative medicine
AU - Matthew, Robert Peter
AU - Kurillo, Gregorij
AU - Han, Jay J.
AU - Bajcsy, Ruzena
PY - 2015
Y1 - 2015
N2 - Quantitative measures of the space an individual can reach is essential for tracking the progression of a disease and the effects of therapeutic intervention. The reachable workspace can be used to track an individuals’ ability to perform activities of daily living, such as feeding and grooming. There are few methods for quantifying upper limb performance, none of which are able to generate a reachable workspace volume from motion capture data. We introduce a method to estimate the reachable workspace volume for an individual by capturing their observed joint limits using a low cost depth camera. This method is then tested on seven individuals with varying upper limb performance. Based on these initial trials, we found that the reachable workspace volume decreased as muscular impairment increased. This shows the potential for this method to be used as a quantitative clinical assessment tool.
AB - Quantitative measures of the space an individual can reach is essential for tracking the progression of a disease and the effects of therapeutic intervention. The reachable workspace can be used to track an individuals’ ability to perform activities of daily living, such as feeding and grooming. There are few methods for quantifying upper limb performance, none of which are able to generate a reachable workspace volume from motion capture data. We introduce a method to estimate the reachable workspace volume for an individual by capturing their observed joint limits using a low cost depth camera. This method is then tested on seven individuals with varying upper limb performance. Based on these initial trials, we found that the reachable workspace volume decreased as muscular impairment increased. This shows the potential for this method to be used as a quantitative clinical assessment tool.
KW - Assessment
KW - Diagnosis
KW - Functional workspace
KW - Goniometry
KW - Kinect
KW - Muscular dystrophy
KW - Rehabilitation
KW - Skeletal Modelling
UR - http://www.scopus.com/inward/record.url?scp=84928819309&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928819309&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16199-0_40
DO - 10.1007/978-3-319-16199-0_40
M3 - Conference contribution
AN - SCOPUS:84928819309
SN - 9783319161983
VL - 8927
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 570
EP - 583
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -