Name
Abstract | ||
|---|---|---|
This paper presents a cognitive approach for a reliable yet battery-friendly personal positioning. A user's position is learned from both historical log and possible measurements. Firstly, user's past activities recorded in the log are summarized into an activity map. Accordingly, a user-habit guided particle filtering algorithm is presented for position prediction. Specifically, our algorithm makes reference to the map to determine the most probable correct position, smoothed with occasional measurement. User's current position is modeled probabilistically by a collection of particles and her future moves are modeled with a tendency to follow a familiar path on the map; The estimate is then smoothed by Bayesian filtering. We also allow the number of particles to vary according to user's position in the map. Thus, along with better insights about user's movement experience, our approach can learn from the past and potentially improve the quality of estimates. Our experiments show that this adaptive filtering model using the activity map can deal with non-linear behaviors rather effectively. The new cognitive scheme can indeed track the user's position with a high degree of accuracy. Moreover, the algorithms exhibit low computational complexities, making them well suited for applications on wearable computers. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1641804.1641873 | MSWiM |
Keywords | Field | DocType |
battery-friendly personal positioning,current position,cognitive approach,past activity,cognitive personal positioning,adaptive particle filter,algorithms exhibit,probable correct position,activity map,new cognitive scheme,historical log,position prediction,particle filter,wearable computer,computational complexity,adaptive filter | Computer vision,Wearable computer,Computer science,Particle filter,Movement experience,Particle filtering algorithm,Artificial intelligence,Adaptive filter,Cognition,Bayesian filtering | Conference |
Citations | PageRank | References |
4 | 0.49 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Fang | 1 | 23 | 2.62 |
| Wen-Jing Hsu | 2 | 415 | 42.77 |
| Larry Rudolph | 3 | 37 | 5.01 |