Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/4200
DC FieldValueLanguage
dc.contributor.authorLourens, Melanie Elizabethen_US
dc.contributor.authorBeram, Shehab Mohameden_US
dc.contributor.authorBorah, Bidyut Bikashen_US
dc.contributor.authorDube, Anand Prakashen_US
dc.contributor.authorDeka, Aniruddhaen_US
dc.contributor.authorTripathi, Vikasen_US
dc.date.accessioned2022-08-26T13:12:12Z-
dc.date.available2022-08-26T13:12:12Z-
dc.date.issued2022-04-28-
dc.identifier.citationLourens, M.et al. 2022. A review of physiological signal processing via Machine Learning (ML) for personal stress detection. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). Presented at: 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). : 345-349. doi:10.1109/icacite53722.2022.9823800en_US
dc.identifier.isbn9781665437899-
dc.identifier.urihttps://hdl.handle.net/10321/4200-
dc.description.abstractPersonal stress is maintained and measured by Machine learning. The device which is wearable has been used for the monitoring of personal self stress and data collection. In this research, it has been talked about the factors by which the physiological signal of the stress has been assessed. On the other hand, different type of technology has been used for the detection of the personal stress such as Electrocardiography (ECG) and many other devices. The observation and difficulties has been seen in this research by using this device and the technology. Stress disorder or ailment is one of the most common ailments in all individuals around the world. Stress and anxiety can greatly influence the life, emotion, behavioural pattern and thinking attributes of individuals. It is important to address this issue sooner or later. Psychological signal processing through machine learning effectively assists to detect the stress disorder at an early stage. The general system often considers some variables to detect stress. They are electrocardiogram, galvanic response, heart rate, respiration and many other elements. The ML tend to use algorithms to compare and contrast data to fetch effective e results. The paper has also carried out a statistical analysis based on three variables to fetch a proper result that provided the study group to comprehend a better understanding of the scenario. The researchers have taken the 'percentage of stress rate' cases' are considered independent variables whereas 'usage of a machine learning system' is considered a dependant variable. The study group has fetched and collected numerous data related to these three variables to get a better understanding.en_US
dc.format.extent5 p.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectMachine learningen_US
dc.subjectSignal processingen_US
dc.subjectGSRen_US
dc.subjectHeart rateen_US
dc.subjectStressen_US
dc.titleA review of physiological signal processing via Machine Learning (ML) for personal stress detectionen_US
dc.typeConferenceen_US
dc.date.updated2022-08-24T10:04:40Z-
dc.relation.conference2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)en_US
dc.identifier.doi10.1109/icacite53722.2022.9823800-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.openairetypeConference-
item.grantfulltextopen-
item.cerifentitytypePublications-
Appears in Collections:Research Publications (Management Sciences)
Files in This Item:
File Description SizeFormat
Lourens et al Review_2022.pdfArticle339.3 kBAdobe PDFView/Open
ICACITE Copyright clearance.docxCopyright Clearance240.9 kBMicrosoft Word XMLView/Open
Show simple item record

Page view(s)

242
checked on Dec 22, 2024

Download(s)

70
checked on Dec 22, 2024

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.