REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Early stress assessment is critical in order to address and avoid further health abnormalities. Conventional stress assessment methods,...

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Main Author: NAQVI, SYED FARAZ
Format: Thesis
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.20695 /
Published: 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: http://utpedia.utp.edu.my/20695/1/Syed%20Faraz%20Naqvi_17007464.pdf
http://utpedia.utp.edu.my/20695/
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spelling utp-utpedia.206952021-09-08T10:09:53Z http://utpedia.utp.edu.my/20695/ REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK NAQVI, SYED FARAZ TK Electrical engineering. Electronics Nuclear engineering Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Early stress assessment is critical in order to address and avoid further health abnormalities. Conventional stress assessment methods, i.e. one-to-one interview sessions, questionnaires and self-reporting, are highly subjective, tedious, and tend to lack accuracy. Therefore, an automatic computer-aided diagnosis (CAD) method is required for accurate and timely stress assessment. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy. 2021-01 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20695/1/Syed%20Faraz%20Naqvi_17007464.pdf NAQVI, SYED FARAZ (2021) REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK. Masters thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
NAQVI, SYED FARAZ
REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK
description Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Early stress assessment is critical in order to address and avoid further health abnormalities. Conventional stress assessment methods, i.e. one-to-one interview sessions, questionnaires and self-reporting, are highly subjective, tedious, and tend to lack accuracy. Therefore, an automatic computer-aided diagnosis (CAD) method is required for accurate and timely stress assessment. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy.
format Thesis
author NAQVI, SYED FARAZ
author_sort NAQVI, SYED FARAZ
title REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK
title_short REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK
title_full REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK
title_fullStr REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK
title_full_unstemmed REAL-TIME STRESS ASSESSMENT USING SLIDING WINDOW BASED CONVOLUTIONAL NEURAL NETWORK
title_sort real-time stress assessment using sliding window based convolutional neural network
publishDate 2021
url http://utpedia.utp.edu.my/20695/1/Syed%20Faraz%20Naqvi_17007464.pdf
http://utpedia.utp.edu.my/20695/
_version_ 1741195654545276928
score 11.62408

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