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Lossy Compression Methods for Performance-Restricted Wearable Devices
dc.contributor.author | Klus, Lucie | |
dc.contributor.author | Lohan, Elena Simona | |
dc.contributor.author | Granell, Carlos | |
dc.contributor.author | Nurmi, Jari | |
dc.date.accessioned | 2022-12-01T18:59:32Z | |
dc.date.available | 2022-12-01T18:59:32Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Klus, L., Lohan, E. S., Granell, C., & Nurmi, J. (2020). Lossy compression methods for performance-restricted wearable devices. In WiP Proceedings of the International Conference on Localization and GNSS (ICL-GNSS 2020). CEUR-WS. | ca_CA |
dc.identifier.issn | 1613-0073 | |
dc.identifier.uri | http://hdl.handle.net/10234/201029 | |
dc.description | Part de la conferència: 10th International Conference on Localization and GNSS (ICL GNSS 2020) . Tampere (Finlandia) | ca_CA |
dc.description.abstract | With the increasing popularity, diversity, and utilization of wearable devices, the data transfer-andstorage efficiency becomes increasingly important. This paper evaluates a set of compression techniques regarding their utilization in crowdsourced wearable data. Transform-based Discrete Cosine Transform (DCT), interpolation-based Lightweight Temporal Compression (LTC) and dimensionality reduction-focused Symbolic Aggregate Approximation (SAX) were chosen as traditional methods. Additionally, an altered SAX (ASAX) is proposed by the authors and implemented to overcome some of the shortcomings of the traditional methods. As one of the most commonly measured entities in wearable devices, heart rate data were chosen to compare the performance and complexity of the selected compression methods. Main results suggest that best compression results are obtained with LTC, which is also the most complex of the studied methods. The best performance-complexity trade-off is achieved with SAX. Our proposed ASAX has the best dynamic properties among the evaluated methods. | ca_CA |
dc.format.extent | 14 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | CEUR Workshop Proceedings | ca_CA |
dc.relation | European Union’s Horizon 2020 Research and Innovation programme | ca_CA |
dc.relation.isPartOf | ICL-GNSS 2020 WiP Proceedings, June 02–04, 2020, Tampere, Finland | ca_CA |
dc.rights | ⃝c 2020 Copyright for this paper by its authors. | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | compression | ca_CA |
dc.subject | Discrete Cosine Transform (DCT) | ca_CA |
dc.subject | Lightweight Temporal Compression (LTC) | ca_CA |
dc.subject | heart rate | ca_CA |
dc.subject | Symbolic Aggregation Approximation (SAX) | ca_CA |
dc.subject | wearables | ca_CA |
dc.title | Lossy Compression Methods for Performance-Restricted Wearable Devices | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | European Union | ca_CA |
oaire.awardNumber | info:eu-repo/grantAgreement/EC/H2020/813278 | ca_CA |