Name
Abstract | ||
|---|---|---|
There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JBHI.2020.2989722 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Biomechanical Phenomena,Humans,Motion,Movement,Range of Motion, Articular,Upper Extremity | Journal | 24 |
Issue | ISSN | Citations |
11 | 2168-2194 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Peter Matthew | 1 | 4 | 6.51 |
| Sarah Seko | 2 | 1 | 2.03 |
| Gregorij Kurillo | 3 | 494 | 34.71 |
| Ruzena Bajcsy | 4 | 2 | 1.71 |
| Louis Cheng | 5 | 0 | 0.34 |
| Jay J. Han | 6 | 26 | 4.09 |
| Jeffrey C. Lotz | 7 | 1 | 2.03 |