dc.contributor.author | Aydemir, Tuğba | |
dc.contributor.author | Şahin, Mehmet | |
dc.contributor.author | Aydemir, Önder | |
dc.date.accessioned | 2022-10-03T09:54:41Z | |
dc.date.available | 2022-10-03T09:54:41Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.citation | Aydemir, T., Sahin, M. & Aydemir, O. (2021). Sequential forward mother wavelet selection method for mental workload assessment on N-back task using photoplethysmography signals. Infrared Physics & Technology, 119, 103966. https://doi.org/10.1016/j.infrared.2021.103966 | en_US |
dc.identifier.issn | 1350-4495 | |
dc.identifier.issn | 1879-0275 | |
dc.identifier.uri | https://doi.org/10.1016/j.infrared.2021.103966 | |
dc.identifier.uri | https://hdl.handle.net/11436/6626 | |
dc.description.abstract | The increasing demands of a cognitive task require additional brain resources. This demand, known as mental workload, can lead to deteriorated task performance. Therefore, assessment of mental workload can provide a proper working environment to promote the working efficiency or improve safety in high-risk working environments for a subject. In this study, we present a novel sequential forward mother wavelet selection method for three levels of mental workload assessment on N-back task using photoplethysmography (PPG) signals, which non-invasively measures the blood volume changes in the microvascular bed of tissue from the skin surface with a low-cost opto-electronic technique. The proposed method was successfully applied to a PPG dataset, which was recorded from 22 healthy subjects during an N-back task using a wearable sensor. Instead of using only one mother wavelet, the features were extracted from effective mother wavelet combinations by means of a sequential forward mother wavelet selection method. In this three-class problem, the highest classification accuracy (CA) rates were achieved with 10 s (s) PPG signal segments compared with the 6 s, and 8 s PPG signal segments. For the 10 s PPG signals segments the highest CA was obtained as 76.67% for Subject 20 and the average CA for all subjects was obtained as 65.76%. Furthermore, the proposed method provided 3.59% CA improvement in average. We believed that the proposed method could ensure a great alternative to conventional mental workload assessment techniques. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Photoplethysmography | en_US |
dc.subject | Mental workload | en_US |
dc.subject | Wireless sensor | en_US |
dc.subject | Classification | en_US |
dc.title | Sequential forward mother wavelet selection method for mental workload assessment on N-back task using photoplethysmography signals | en_US |
dc.type | article | en_US |
dc.contributor.department | RTEÜ, Fen - Edebiyat Fakültesi, Fizik Bölümü | en_US |
dc.contributor.institutionauthor | Aydemir, Tuğba | |
dc.contributor.institutionauthor | Şahin, Mehmet | |
dc.identifier.doi | 10.1016/j.infrared.2021.103966 | en_US |
dc.identifier.volume | 119 | en_US |
dc.identifier.startpage | 103966 | en_US |
dc.relation.journal | Infrared Physics & Technology | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |