Motion mode recognition and step detection algorithms for mobile phone users.

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dc.contributor.authorSusi, Melania
dc.contributor.authorRenaudin, Valérie
dc.contributor.authorLachapelle, Gérard
dc.date.accessioned2020-02-06T17:33:03Z
dc.date.available2020-02-06T17:33:03Z
dc.date.issued2013-01-25
dc.description.abstractMicroelectromechanical Systems MEMS technology is playing a key role in the design of the new generation of smartphones Thanks to their reduced size reduced power consumption MEMS sensors can be embedded in above mobile devices for increasing their functionalities However MEMS cannot allow accurate autonomous location without external updates e g from GPS signals since their signals are degraded by various errors When these sensors are fixed on the user s foot the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes ZUPTs are performed to bound the position error When the sensor is in the hand the situation becomes much more complex First of all the hand motion can be decoupled from the general motion of the user Second the characteristics of the inertial signals can differ depending on the carrying modes Therefore algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed A classifier able to detect motion modes typical for mobile phone users has been designed and implemented According to the detected motion mode adaptive step detection algorithms are applied Success of the step detection process is found to be higher than 97 in all motion modes
dc.identifier.urihttp://dx.doi.org/10.3390/s130201539
dc.identifier.urihttps://lib.digitalsquare.io/xmlui/handle/123456789/5283
dc.relation.uriSensors (Basel, Switzerland)
dc.subjectClient
dc.subjectAccess to information or data
dc.subjectQuality/unreliability of data
dc.subjectPrototype
dc.subjectFunctionality
dc.subjectData collection and reporting
dc.subjectSurveillance
dc.subjectRaw data
dc.subjectAccelerometers / Motion sensors
dc.subjectDecision support algorithm
dc.titleMotion mode recognition and step detection algorithms for mobile phone users.en
dcterms.abstractMicroelectromechanical Systems MEMS technology is playing a key role in the design of the new generation of smartphones Thanks to their reduced size reduced power consumption MEMS sensors can be embedded in above mobile devices for increasing their functionalities However MEMS cannot allow accurate autonomous location without external updates e g from GPS signals since their signals are degraded by various errors When these sensors are fixed on the user s foot the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes ZUPTs are performed to bound the position error When the sensor is in the hand the situation becomes much more complex First of all the hand motion can be decoupled from the general motion of the user Second the characteristics of the inertial signals can differ depending on the carrying modes Therefore algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed A classifier able to detect motion modes typical for mobile phone users has been designed and implemented According to the detected motion mode adaptive step detection algorithms are applied Success of the step detection process is found to be higher than 97 in all motion modes
dcterms.contributorSusi, Melania
dcterms.contributorRenaudin, Valérie
dcterms.contributorLachapelle, Gérard
dcterms.identifierhttp://dx.doi.org/10.3390/s130201539
dcterms.relationSensors (Basel, Switzerland)
dcterms.subjectClient
dcterms.subjectAccess to information or data
dcterms.subjectQuality/unreliability of data
dcterms.subjectPrototype
dcterms.subjectFunctionality
dcterms.subjectData collection and reporting
dcterms.subjectSurveillance
dcterms.subjectRaw data
dcterms.subjectAccelerometers / Motion sensors
dcterms.subjectDecision support algorithm
dcterms.titleMotion mode recognition and step detection algorithms for mobile phone users.en
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