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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zappi, P. Stiefmeier, T. Farella, E. Roggen, D. Benini, L. Troster, G. |
| Copyright Year | 2007 |
| Description | Author affiliation: Univ. of Bologna, Bologna (Zappi, P.) |
| Abstract | Activity recognition from on-body sensors is affected by sensor degradation, interconnections failures, and jitter in sensor placement and orientation. We investigate how this may be balanced by exploiting redundant sensors distributed on the body. We recognize activities by a meta-classifier that fuses the information of simple classifiers operating on individual sensors. We investigate the robustness to faults and sensor scalability which follows from classifier fusion. We compare a reference majority voting and a naive Bayesian fusion scheme. We validate this approach by recognizing a set of 10 activities carried out by workers in the quality assurance checkpoint of a car assembly line. Results show that classification accuracy greatly increases with additional sensors (50% with 1 sensor, 80% and 98% with 3 and 57 sensors), and that sensor fusion implicitly allows to compensate for typical faults up to high fault rates. These results highlight the benefit of large on- body sensor network rather than a minimum set of sensors for activity recognition and prompts further investigation. |
| Starting Page | 281 |
| Ending Page | 286 |
| File Size | 458387 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424415014 |
| DOI | 10.1109/ISSNIP.2007.4496857 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-12-03 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Degradation Quality assurance Fuses Scalability Voting Bayesian methods Sensor fusion Jitter Robustness Assembly |
| Content Type | Text |
| Resource Type | Article |
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