Name
Abstract | ||
|---|---|---|
Body weight adapted drug dosages are important for emergency treatments: Inaccuracies in body weight estimation may lead to inaccurate drug dosing. This paper describes an improved approach to estimating the body weight of emergency patients in a trauma room, based on images from an RGB-D and a thermal camera. The improvements are specific to several aspects: Fusion of RGB-D and thermal camera eases filtering and segmentation of the patient's body from the background. Robustness and accuracy is gained by an artificial neural network, which considers geometric features from the sensors as input, e.g. the patient's volume, and shape parameters. Preliminary experiments with 69 patients show an accuracy close to 90 percent, with less than 10 percent relative error and the results are compared with the physician's estimate, the patient's statement and an established anthropometric method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/12.2216042 | Proceedings of SPIE |
Keywords | Field | DocType |
human body weight,drug dosing,RGB-D,artificial neural network | Computer vision,Segmentation,Drug dosages,Filter (signal processing),Robustness (computer science),Image segmentation,Body weight,Artificial intelligence,Artificial neural network,Approximation error,Physics | Conference |
Volume | ISSN | Citations |
9784 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Pfitzner | 1 | 4 | 2.16 |
| Stefan May | 2 | 184 | 16.09 |
| Andreas Nüchter | 3 | 1341 | 90.03 |