Falling person detection using multisensor signal processing
| dc.citation.epage | 149304-1 | en_US |
| dc.citation.spage | 149304-7 | en_US |
| dc.citation.volumeNumber | 2008 | en_US |
| dc.contributor.author | Toreyin, B. U. | en_US |
| dc.contributor.author | Soyer, E. B. | en_US |
| dc.contributor.author | Onaran, I. | en_US |
| dc.contributor.author | Cetin, E. E. | en_US |
| dc.date.accessioned | 2016-02-08T10:10:35Z | |
| dc.date.available | 2016-02-08T10:10:35Z | |
| dc.date.issued | 2008 | en_US |
| dc.department | Department of Electrical and Electronics Engineering | en_US |
| dc.description.abstract | Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision. | en_US |
| dc.identifier.doi | 10.1155/2008/149304 | en_US |
| dc.identifier.eissn | 1687-6180 | |
| dc.identifier.issn | 1687-6172 | |
| dc.identifier.uri | http://hdl.handle.net/11693/23229 | |
| dc.language.iso | English | en_US |
| dc.publisher | SpringerOpen | en_US |
| dc.relation.isversionof | https://doi.org/10.1155/2008/149304 | en_US |
| dc.source.title | Eurasip Journal on Advances in Signal Processing | en_US |
| dc.title | Falling person detection using multisensor signal processing | en_US |
| dc.type | Article | en_US |
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