Name
Abstract | ||
|---|---|---|
Sensors embedded into clothing for measuring human movement are becoming more widespread in research, with applications in clinical diagnostics or rehabilitation studies. A major issue with their use is the undesired effect of fabric motion artifacts corrupting movement signals. This paper presents a method for learning body movements, viewing the undesired motion as stochastic perturbations to the sensed motion, and utilising errors-in-variables models to eliminate these errors in the learning process. Experiments, both in simulation and with a physical fabric-mounted sensor, indicate improved prediction accuracy as compared to standard learning methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/HUMANOIDS.2014.7041466 | Humanoid Robots |
Keywords | Field | DocType |
fabrics,patient diagnosis,patient rehabilitation,sensors,body movement learning,clinical diagnostic,clinical rehabilitation,fabric motion artifact elimination,fabric-mounted wearable sensor,stochastic perturbation | Computer vision,Data modeling,Simulation,Computer science,Wearable computer,Acceleration,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2164-0572 | 0 | 0.34 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brendan Michael | 1 | 2 | 0.71 |
| Matthew Howard | 2 | 101 | 13.10 |