Human behavior cognition using smartphone sensors.

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dc.contributor.authorPei, Ling
dc.contributor.authorGuinness, Robert
dc.contributor.authorChen, Ruizhi
dc.contributor.authorLiu, Jingbin
dc.contributor.authorKuusniemi, Heidi
dc.contributor.authorChen, Yuwei
dc.contributor.authorChen, Liang
dc.contributor.authorKaistinen, Jyrki
dc.date.accessioned2020-02-06T18:37:39Z
dc.date.available2020-02-06T18:37:39Z
dc.date.issued2013-01-25
dc.description.abstractThis research focuses on sensing context modeling human behavior and developing a new architecture for a cognitive phone platform We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior Contexts in this research are abstracted as a Context Pyramid which includes six levels Raw Sensor Data Physical Parameter Features Patterns Simple Contextual Descriptors Activity Level Descriptors and Rich Context To achieve implementation of the Context Pyramid on a cognitive phone three key technologies are utilized ubiquitous positioning motion recognition and human behavior modeling Preliminary tests indicate that we have successfully achieved the Activity Level Descriptors level with our LoMoCo Location Motion Context model Location accuracy of the proposed solution is up to 1 9 meters in corridor environments and 3 5 meters in open spaces Test results also indicate that the motion states are recognized with an accuracy rate up to 92 9 using a Least Square Support Vector Machine LS SVM classifier
dc.identifier.urihttp://dx.doi.org/10.3390/s130201402
dc.identifier.urihttps://lib.digitalsquare.io/xmlui/handle/123456789/6669
dc.relation.uriSensors (Basel, Switzerland)
dc.titleHuman behavior cognition using smartphone sensors.en
dcterms.abstractThis research focuses on sensing context modeling human behavior and developing a new architecture for a cognitive phone platform We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior Contexts in this research are abstracted as a Context Pyramid which includes six levels Raw Sensor Data Physical Parameter Features Patterns Simple Contextual Descriptors Activity Level Descriptors and Rich Context To achieve implementation of the Context Pyramid on a cognitive phone three key technologies are utilized ubiquitous positioning motion recognition and human behavior modeling Preliminary tests indicate that we have successfully achieved the Activity Level Descriptors level with our LoMoCo Location Motion Context model Location accuracy of the proposed solution is up to 1 9 meters in corridor environments and 3 5 meters in open spaces Test results also indicate that the motion states are recognized with an accuracy rate up to 92 9 using a Least Square Support Vector Machine LS SVM classifier
dcterms.contributorPei, Ling
dcterms.contributorGuinness, Robert
dcterms.contributorChen, Ruizhi
dcterms.contributorLiu, Jingbin
dcterms.contributorKuusniemi, Heidi
dcterms.contributorChen, Yuwei
dcterms.contributorChen, Liang
dcterms.contributorKaistinen, Jyrki
dcterms.identifierhttp://dx.doi.org/10.3390/s130201402
dcterms.relationSensors (Basel, Switzerland)
dcterms.titleHuman behavior cognition using smartphone sensors.en
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