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dc.contributor.authorBergmann, Jeroen H. M.
dc.contributor.authorLangdon, Patrick M.
dc.contributor.authorMayagoitia, Ruth E.
dc.contributor.authorHoward, Newton
dc.date.accessioned2014-05-02T16:47:11Z
dc.date.available2014-05-02T16:47:11Z
dc.date.issued2014-02
dc.date.submitted2013-07
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/86372
dc.description.abstractHumans appear to be sensitive to relative small changes in their surroundings. These changes are often initially perceived as irrelevant, but they can cause significant changes in behavior. However, how exactly people’s behavior changes is often hard to quantify. A reliable and valid tool is needed in order to address such a question, ideally measuring an important point of interaction, such as the hand. Wearable-body-sensor systems can be used to obtain valuable, behavioral information. These systems are particularly useful for assessing functional interactions that occur between the endpoints of the upper limbs and our surroundings. A new method is explored that consists of computing hand position using a wearable sensor system and validating it against a gold standard reference measurement (optical tracking device). Initial outcomes related well to the gold standard measurements (r = 0.81) showing an acceptable average root mean square error of 0.09 meters. Subsequently, the use of this approach was further investigated by measuring differences in motor behavior, in response to a changing environment. Three subjects were asked to perform a water pouring task with three slightly different containers. Wavelet analysis was introduced to assess how motor consistency was affected by these small environmental changes. Results showed that the behavioral motor adjustments to a variable environment could be assessed by applying wavelet coherence techniques. Applying these procedures in everyday life, combined with correct research methodologies, can assist in quantifying how environmental changes can cause alterations in our motor behavior.en_US
dc.description.sponsorshipBrain Sciences Foundation (fellowship)en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0088080en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleExploring the Use of Sensors to Measure Behavioral Interactions: An Experimental Evaluation of Using Hand Trajectoriesen_US
dc.typeArticleen_US
dc.identifier.citationBergmann, Jeroen H. M., Patrick M. Langdon, Ruth E. Mayagoitia, and Newton Howard. “Exploring the Use of Sensors to Measure Behavioral Interactions: An Experimental Evaluation of Using Hand Trajectories.” Edited by Derek Abbott. PLoS ONE 9, no. 2 (February 7, 2014): e88080.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Synthetic Intelligence Laboratoryen_US
dc.contributor.mitauthorHoward, Newtonen_US
dc.contributor.mitauthorBergmann, Jeroen H. M.en_US
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsBergmann, Jeroen H. M.; Langdon, Patrick M.; Mayagoitia, Ruth E.; Howard, Newtonen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8503-3973
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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