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Please use this identifier to cite or link to this item: http://dspace.cityu.edu.hk/handle/2031/8239
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dc.contributor.authorWan, Chiu Yeungen_US
dc.date.accessioned2016-01-07T01:24:11Z
dc.date.accessioned2017-09-19T09:15:12Z
dc.date.accessioned2019-02-12T07:33:49Z-
dc.date.available2016-01-07T01:24:11Z
dc.date.available2017-09-19T09:15:12Z
dc.date.available2019-02-12T07:33:49Z-
dc.date.issued2015en_US
dc.identifier.other2015eewcy810en_US
dc.identifier.urihttp://144.214.8.231/handle/2031/8239-
dc.description.abstractCognitive load (CL) refers to the total amount of mental resources used for completing a task in any instant. CL detection has a wide range of applications in human life, including having a more effective living and avoiding danger caused by cognitive overload. Galvanic Skin Response (GSR) had been recently suggested as an index of CL. Although GSR is not the most accurate tool for CL detection, it contains its own advantages to compensate this drawback. As GSR is an easily-captured and robust signal, a low cost and portable device can be made and it can be widely used in many situations. Experiment studies have indicated that CL level can be judged by analysis of GSR signal. However, the investigations are unilateral and not fully illustrated. Therefore, this research aims to provide a detailed analysis on CL detection using both Skin Conductance Level (SCL) and Skin Conductance Response (SCR). Subjects are going to finish tasks with different difficulty levels in both "no-stress" and "stressed" condition in this study. Analysis of Electromyography (EMG) signal will be used as a stress predictor for supporting this investigation. The analysed results will further be applied on a new designed free-recall test to observe its credibility on other types of application.en_US
dc.rightsThis work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.en_US
dc.rightsAccess is restricted to CityU users.en_US
dc.titleWireless Health Monitoringen_US
dc.contributor.departmentDepartment of Electronic Engineeringen_US
dc.description.supervisorSupervisor: Dr. CHAN, Leanne L H; Assessor: Dr. CHAN, Stanley C Fen_US
Appears in Collections:Electrical Engineering - Undergraduate Final Year Projects 

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