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dc.contributor.authorWu, Bing-Feien_US
dc.contributor.authorHuang, Po-Weien_US
dc.contributor.authorLin, Chun-Hsienen_US
dc.contributor.authorChung, Meng-Liangen_US
dc.contributor.authorTsou, Tsong-Yangen_US
dc.contributor.authorWu, Yu-Liangen_US
dc.date.accessioned2018-08-21T05:53:39Z-
dc.date.available2018-08-21T05:53:39Z-
dc.date.issued2018-01-01en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2018.2828133en_US
dc.identifier.urihttp://hdl.handle.net/11536/144977-
dc.description.abstractMedical fields have seen increasing attention being given to image based heart rate measurement in recent years. One of the major limitations is motion artifacts of subject's head. Although there have been many studies focusing on signal extraction using different parameters and models, the development of frequency domain analysis is emerging slowly and moving in many directions. In the field of contact photoplethysmography (PPG), recent studies employed the acceleration signals to assist their spectral peak tracking algorithms. Inspired by the development of contact PPG, we are proposing a motion resistant spectral peak tracking (MRSPT) framework which eliminates the motion artifacts by integrating facial motion signals. The effectiveness of MRSPT coupled with the optimal image-based PPG (iPPG) signal has been tested against the state-of-the-art spectral peak tracking algorithms, multi-channel spectral matrix decomposition (MC-SMD), and the maximum peak selection coupled with optimal iPPG signal (Optimal MPS). Compared with MC-SMD and Optimal MPS, MRSPT uplifts the success rate-10 (success rate-5), the probability in which the absolute error is below ten (five) beats per mins, from 54.7% (36.3%) with MC-SMD and 73.0% (61.3%) with Optimal MPS to 90.7% (75.7%) with MRSPT in motion scenarios where subject moves arbitrarily with different distance or lighting. MRSPT also enhances the success rate-10 (success rate-5) from 40.7% (26.3%) with MC-SMD and 57.4% (45.7%) with Optimal MPS to 73.4% (58.4%) with MRSPT in all seven motion conditions including driving and running. Averagely, the success rate-five of Optimal MRSPT surpass the success rate-10 of both Optimal MPS and MC-SMD.en_US
dc.language.isoen_USen_US
dc.subjectBiomedical signal processingen_US
dc.subjectbiomedical monitoringen_US
dc.subjectheart rateen_US
dc.subjectimage sequence analysisen_US
dc.subjectphotoplethysmography (PPG)en_US
dc.subjectspectral peak trackingen_US
dc.titleMotion Resistant Image-Photoplethysmography Based on Spectral Peak Tracking Algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2018.2828133en_US
dc.identifier.journalIEEE ACCESSen_US
dc.citation.volume6en_US
dc.citation.spage21621en_US
dc.citation.epage21634en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000431964200001en_US
Appears in Collections:Articles