Name
Abstract | ||
|---|---|---|
In this paper, a novel human body pose estimation system is introduced, which features a synchronous network of wirelessly connected sensor nodes, thus forming a wireless body sensor network (WBSN). This measurement architecture for establishing human body pose estimations works in combination with the HTC Vive base stations and a Wi-Fi router. The former emits infrared pulses and laser sweeps in the horizontal and vertical plane captured by the sensor nodes' photodiodes. The infrared data can be converted into an azimuth and elevation angle for each photodiode relative to the emitting HTC Vive device. To estimate the human body poses, the sensor data are fed into a probabilistic non-linear maximum likelihood estimator combined with a parametric human body model. Using a parametric human body model for estimating the poses proves to be more resilient to sensor noise compared to estimating the Six Degrees of Freedom (6DoF) of every sensor node individually. The solution space can be constrained to the parameters of the users body model that relates to a priori information on the subject. The combination of the proposed hardware sensor network, its synchronous sensor data, and processing algorithms yields a cost-efficient human body pose estimation system. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2910636 | IEEE ACCESS |
Keywords | Field | DocType |
Body sensor networks,distributed embedded systems,motion capture,pose estimation,sensor arrays | Human-body model,Sensor node,Base station,Wireless,Computer science,Six degrees of freedom,Pose,Real-time computing,Parametric statistics,Wireless sensor network,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dennis Laurijssen | 1 | 1 | 1.36 |
| Wim Saeys | 2 | 0 | 1.35 |
| Steven Truijen | 3 | 0 | 2.70 |
| walter daems | 4 | 1 | 3.74 |
| Jan Steckel | 5 | 7 | 7.97 |