Reject-optional LVQ-based two-level classifier to improve reliability in footstep identification. Suutala J Pirttikangas S, R., J., &., R., J. In pages 182-187, 2004. Proc. Second International Conference on Pervasive Computing (PERVASIVE 2004), Linz / Vienna, Austria.
abstract   bibtex   
This paper reports experiments of recognizing walkers based on measurements with a pressure-sensitive EMFi-floor. Identification is based on a twolevel classifier system. The first level performs Learning Vector Quantization (LVQ) with a reject option to identify or to reject a single footstep. The second level classifies or rejects a sequence of three consecutive identified footsteps based on the knowledge from the first level. The system was able to reduce classification error compared to a single footstep classifier without a reject option. The results show a 90% overall success rate with a 20% rejection rate, identifying eleven walkers, which can be considered very reliable.
@inProceedings{
 title = {Reject-optional LVQ-based two-level classifier to improve reliability in footstep identification.},
 type = {inProceedings},
 year = {2004},
 pages = {182-187},
 publisher = {Proc. Second International Conference on Pervasive Computing (PERVASIVE 2004), Linz / Vienna, Austria},
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 last_modified = {2019-11-19T13:46:12.530Z},
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 citation_key = {isg:485},
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 abstract = {This paper reports experiments of recognizing walkers based on measurements with a pressure-sensitive EMFi-floor. Identification is based on a twolevel classifier system. The first level performs Learning Vector Quantization (LVQ) with a reject option to identify or to reject a single footstep. The second level classifies or rejects a sequence of three consecutive identified footsteps based on the knowledge from the first level. The system was able to reduce classification error compared to a single footstep classifier without a reject option. The results show a 90% overall success rate with a 20% rejection rate, identifying eleven walkers, which can be considered very reliable.},
 bibtype = {inProceedings},
 author = {Suutala J Pirttikangas S, Riekki J & Röning J}
}

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