Name
Abstract | ||
|---|---|---|
Joints angles are some of the most common measurements for the evaluation of lower limb injury risk, specially of lower limb joints. The 2D projections of these angles, as the Frontal Plane Projection Angle (FPPA), are widely used as an estimation of the angle value. Traditional procedures to measure 2D angles imply huge time investments, primarily when evaluating multiple subjects. This work presents a novel 2D video analysis system directed to capture the joint angles in a cost-and-time-effective way. It employs Kinect V2 depth sensor to track retro-reflective markers attached to the patient's joints to provide an automatic estimation of the desired angles. The information registered by the sensor is processed and managed by a computer application that expedites the analysis of the results. The reliability of the system has been studied against traditional procedures obtaining excellent results. This system is aimed to be the starting point of an autonomous injury prediction system based on machine learning techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59147-6_7 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II |
Keywords | Field | DocType |
Motion capture,2D analysis,Frontal Plane Projection Angel,Reflective markers,Kinect | Motion capture,Computer vision,Lower limb injury,Coronal plane,Pattern recognition,Lower limb,Computer science,Artificial intelligence,Prediction system | Conference |
Volume | ISSN | Citations |
10306 | 0302-9743 | 1 |
PageRank | References | Authors |
0.40 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Bailon | 1 | 2 | 1.11 |
| M. Damas | 2 | 387 | 33.04 |
| Héctor Pomares | 3 | 651 | 64.11 |
| Oresti Baños | 4 | 380 | 35.57 |