Position invariance for wearables: interchangeability and single-unit usage via machine learning
buir.contributor.author | Yurtman, Aras | |
buir.contributor.author | Barshan, Billur | |
buir.contributor.orcid | Yurtman, Aras|0000-0001-6213-5427 | |
buir.contributor.orcid | Barshan, Billur|0000-0001-6783-6572 | |
dc.citation.epage | 8342 | en_US |
dc.citation.issueNumber | 10 | en_US |
dc.citation.spage | 8328 | en_US |
dc.citation.volumeNumber | 8 | en_US |
dc.contributor.author | Yurtman, Aras | |
dc.contributor.author | Barshan, Billur | |
dc.contributor.author | Redif, S. | |
dc.date.accessioned | 2021-03-08T08:05:11Z | |
dc.date.available | 2021-03-08T08:05:11Z | |
dc.date.issued | 2021 | |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description.abstract | We propose a new methodology to attain invariance to the positioning of body-worn motion-sensor units for recognizing everyday and sports activities. We first consider random interchangeability of the sensor units so that the user does not need to distinguish between them before wearing. To this end, we propose to use the compact singular value decomposition (SVD) that significantly reduces the accuracy degradation caused by random interchanging of the units. Secondly, we employ three variants of a generalized classifier that requires wearing only a single sensor unit on any one of the body parts to classify the activities. We combine both approaches with our previously developed methods to achieve invariance to both position and orientation, which ultimately allows the user significant flexibility in sensor-unit placement (position and orientation). We assess the performance of our proposed approach on a publicly available activity dataset recorded by body-worn motion-sensor units. Experimental results suggest that there is a tolerable reduction in accuracy, which is justified by the significant flexibility and convenience offered to users when placing the units. | en_US |
dc.description.provenance | Submitted by Onur Emek (onur.emek@bilkent.edu.tr) on 2021-03-08T08:05:11Z No. of bitstreams: 1 Position_Invariance_for_Wearables_Interchangeability_and_Single-Unit_Usage_via_Machine_Learning.pdf: 18741503 bytes, checksum: 9ee58066521cb39de5b21a311c907593 (MD5) | en |
dc.description.provenance | Made available in DSpace on 2021-03-08T08:05:11Z (GMT). No. of bitstreams: 1 Position_Invariance_for_Wearables_Interchangeability_and_Single-Unit_Usage_via_Machine_Learning.pdf: 18741503 bytes, checksum: 9ee58066521cb39de5b21a311c907593 (MD5) Previous issue date: 2020 | en |
dc.identifier.doi | 10.1109/JIOT.2020.3044754 | en_US |
dc.identifier.issn | 2327-4662 | |
dc.identifier.uri | http://hdl.handle.net/11693/75862 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | https://dx.doi.org/10.1109/JIOT.2020.3044754 | en_US |
dc.source.title | IEEE Internet of Things Journal | en_US |
dc.subject | Activity monitoring and classification | en_US |
dc.subject | Wearable sensing | en_US |
dc.subject | Position invariance | en_US |
dc.subject | Orientation invariance | en_US |
dc.subject | Internet of Things (IoT) | en_US |
dc.subject | Motion sensors | en_US |
dc.subject | Accelerometer | en_US |
dc.subject | Gyroscope | en_US |
dc.subject | Inertial sensors | en_US |
dc.subject | Magnetometer | en_US |
dc.subject | Pattern recognition | en_US |
dc.subject | Machine learning classifiers | en_US |
dc.title | Position invariance for wearables: interchangeability and single-unit usage via machine learning | en_US |
dc.type | Article | en_US |
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