Knee injury prevention algorithm based on portable motion capture
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, Keon Do | ko |
| dc.contributor.author | Park, Hyung-Soon | ko |
| dc.date.accessioned | 2021-12-14T06:55:12Z | - |
| dc.date.available | 2021-12-14T06:55:12Z | - |
| dc.date.created | 2021-12-01 | - |
| dc.date.issued | 2021-01-17 | - |
| dc.identifier.citation | 2021 IEEE International Conference on Big Data and Smart Computing(BigComp) | - |
| dc.identifier.uri | http://hdl.handle.net/10203/290654 | - |
| dc.description.abstract | A large load occurs on the knee when there is a rapid movement such as jumping, cutting or pivoting. During jumping, load on knee becomes up to 6.9 times the body weight. Therefore, knee is the most important joint for body weighting, and takes about 259 days to rehabilitate in case of anterior cruciate ligament (ACL) injury. To prevent knee injury, measurement of knee condition is essential, because the prescribed exercises differ depending on the cause of knee joint instability. This paper suggests a method to measure the risk of knee injury using a portable motion capture system, and evaluates its joint kinematics estimation accuracy. | - |
| dc.language | English | - |
| dc.publisher | IEEE | - |
| dc.title | Knee injury prevention algorithm based on portable motion capture | - |
| dc.type | Conference | - |
| dc.type.rims | CONF | - |
| dc.citation.publicationname | 2021 IEEE International Conference on Big Data and Smart Computing(BigComp) | - |
| dc.identifier.conferencecountry | KO | - |
| dc.identifier.conferencelocation | Jeju, virtual | - |
| dc.contributor.localauthor | Park, Hyung-Soon | - |
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