Name
Abstract | ||
|---|---|---|
This paper proposes a continuous location and skeletal tracking system using multiple RGB-Depth sensors (such as Kinects) deployed along a corridor with overlapping coverages. First, we transform the coordinates of all sensor into a unified coordinate. Second, the system recognizes users (such as patients under rehabilitation) from different views of these sensors and classifies them by their patient IDs. Third, the patient information can be continuously handed over among sensors when they move around the area. Experiment results show that the skeletal association during handover achieves approximately 90.61% in accuracy and 96.89% in precision in 44,817 experiment trials. By our observation, injured people possess asymmetric gait parameters especially on the ratio of the duration of the swing/stance phase. For example, the injured foot generally has a longer swinging duration than the healthy side. The proposed system has potential in patient rehabilitation monitoring applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/iThings.2014.31 | iThings/GreenCom/CPSCom |
Keywords | DocType | Citations |
skeleton tracking,asymmetric gait parameters,skeletal association,continuous location and skeletal tracking system,patient rehabilitation monitoring applications,multiple rgb-depth sensors,sensor network,depth sensor,gait analysis,patient rehabilitation,object tracking,rehabilitation,green computing,internet of things | Conference | 1 |
PageRank | References | Authors |
0.37 | 14 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun-Wei Qiu | 1 | 3 | 0.74 |
| Ting-Hui Chiang | 2 | 1 | 2.06 |
| Chi-Chung Lo | 3 | 1 | 0.37 |
| Li-Min Lin | 4 | 1 | 0.37 |
| Lan-Da Van | 5 | 1 | 0.37 |
| Yu-Chee Tseng | 6 | 6603 | 639.67 |
| Yu-Tai Ching | 7 | 2 | 2.41 |