Name
Abstract | ||
|---|---|---|
This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 fitness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantified relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2370216.2370437 | UbiComp |
Keywords | Field | DocType |
recognition algorithm,inertial sensor displacement effect,gradual sensor displacement condition,mutual displacement deployment,open benchmark dataset,inertial sensor unit,displacement condition,realistic dataset,activity recognition,sensor position | Inertial frame of reference,Computer vision,Software deployment,Activity recognition,Computer science,Wearable systems,Acceleration,Motion sensors,Artificial intelligence,Recognition algorithm,Statistical analysis | Conference |
Citations | PageRank | References |
23 | 0.92 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oresti Baños | 1 | 380 | 35.57 |
| M. Damas | 2 | 387 | 33.04 |
| Héctor Pomares | 3 | 651 | 64.11 |
| I. Rojas | 4 | 1750 | 143.09 |
| Máté Attila Tóth | 5 | 37 | 5.91 |
| Oliver Amft | 6 | 1257 | 132.56 |