Name
Abstract | ||
|---|---|---|
Sleeping posture reveals important information for eldercare and patient care, especially for bed ridden patients. Traditionally, some works address the problem from either pressure sensor or video image. This paper presents a multimodal approach to sleeping posture classification. Features from pressure sensor map and video image have been proposed in order to characterize the posture patterns. The spatiotemporal registration of the two modalities has been considered in the design, and the joint feature extraction and data fusion is presented. Using multi-class SVM, experiment results demonstrate that the multimodal approach achieves better performance than the approaches using single modal sensing. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.1054 | ICPR |
Keywords | Field | DocType |
video image,data fusion,bed ridden patient,posture classification,experiment result,posture pattern,multimodal sleeping posture classification,pressure sensor map,better performance,pressure sensor,multimodal approach,pose estimation,pressure sensors,principal component analysis,geriatrics,feature extraction,multimodal,leg | Modalities,Computer vision,Pattern recognition,Computer science,Support vector machine,Feature extraction,Sensor fusion,Pose,Pressure sensor,Artificial intelligence,Patient care,Modal | Conference |
Citations | PageRank | References |
11 | 1.87 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weimin Huang | 1 | 1074 | 96.95 |
| Aung Aung Phyo Wai | 2 | 84 | 12.43 |
| Siang Fook Foo | 3 | 13 | 2.29 |
| Jit Biswas | 4 | 344 | 48.04 |
| Chi-Chun Hsia | 5 | 114 | 8.62 |
| Koujuch Liou | 6 | 71 | 12.83 |