國立虎尾科技大學 |

Application of Computer Vision Techniques for Risk Assessment of Lifting Tasks : = Analyzing Worker Facial Expressions and Physiological Signals.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Application of Computer Vision Techniques for Risk Assessment of Lifting Tasks :/
其他題名:	Analyzing Worker Facial Expressions and Physiological Signals.
作者:	Shakeri, Afrooz.
面頁冊數:	1 online resource (69 pages)
附註:	Source: Masters Abstracts International, Volume: 86-01.
Contained By:	Masters Abstracts International86-01.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798383201244

Application of Computer Vision Techniques for Risk Assessment of Lifting Tasks : = Analyzing Worker Facial Expressions and Physiological Signals.
Shakeri, Afrooz.

Application of Computer Vision Techniques for Risk Assessment of Lifting Tasks :Analyzing Worker Facial Expressions and Physiological Signals. - 1 online resource (69 pages)

Source: Masters Abstracts International, Volume: 86-01.

Thesis (M.A.Sc.)--University of Windsor (Canada), 2024.

Includes bibliographical references

Lifting tasks pose significant risks to workers, necessitating effective risk assessment methods. The Revised NIOSH Lifting Equation (RNLE) helps evaluate these risks. RNLE manual assessments often introduce errors, while instrument-based methods can be costly, intrusive, and require technical staff to interpret the data. This study explores non-intrusive, cost-effective methods using computer vision for facial analysis and bio-signal sensors for physiological data to automatically classify lifting task risks as safe or risky. Ten lifting tasks were designed, collecting participants' facial data and bio-signals with a head-mounted camera and ECG and EDA sensors, labeled as safe or risky based on the RNLE. Three datasets (facial-only, bio-signals-only, and mixed) are analyzed to identify the most effective data type. The study also evaluates two models, Functional Data Analysis (FDA) and Recurrent Neural Networks (RNN), while using raw data to preserve information integrity, to find the optimal model for risk classification. Results showed that the RNN model trained on the mixed dataset achieved the highest mean accuracy (70%). This study demonstrates that lifting task risks can be estimated automatically using non-intrusive and cost-effective facial recordings and bio-signal sensors, offering an effective alternative for ergonomic assessment.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383201244Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Bio-signalsIndex Terms--Genre/Form:

554714
Electronic books.

Application of Computer Vision Techniques for Risk Assessment of Lifting Tasks : = Analyzing Worker Facial Expressions and Physiological Signals.
LDR:02783ntm a22003977 4500 001 1149756
005 20241022112631.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798383201244
035 $a (MiAaPQ)AAI31332276
035 $a AAI31332276
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Shakeri, Afrooz. $3 1476090
245 1 0 $a Application of Computer Vision Techniques for Risk Assessment of Lifting Tasks : $b Analyzing Worker Facial Expressions and Physiological Signals.
264 0 $c 2024
300 $a 1 online resource (69 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 86-01.
500 $a Advisor: Kim, E.
502 $a Thesis (M.A.Sc.)--University of Windsor (Canada), 2024.
504 $a Includes bibliographical references
520 $a Lifting tasks pose significant risks to workers, necessitating effective risk assessment methods. The Revised NIOSH Lifting Equation (RNLE) helps evaluate these risks. RNLE manual assessments often introduce errors, while instrument-based methods can be costly, intrusive, and require technical staff to interpret the data. This study explores non-intrusive, cost-effective methods using computer vision for facial analysis and bio-signal sensors for physiological data to automatically classify lifting task risks as safe or risky. Ten lifting tasks were designed, collecting participants' facial data and bio-signals with a head-mounted camera and ECG and EDA sensors, labeled as safe or risky based on the RNLE. Three datasets (facial-only, bio-signals-only, and mixed) are analyzed to identify the most effective data type. The study also evaluates two models, Functional Data Analysis (FDA) and Recurrent Neural Networks (RNN), while using raw data to preserve information integrity, to find the optimal model for risk classification. Results showed that the RNN model trained on the mixed dataset achieved the highest mean accuracy (70%). This study demonstrates that lifting task risks can be estimated automatically using non-intrusive and cost-effective facial recordings and bio-signal sensors, offering an effective alternative for ergonomic assessment.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Industrial engineering. $3 679492
653 $a Bio-signals
653 $a Computer vision
653 $a Facial analysis
653 $a Lifting risk assessment
653 $a Physical workload
655 7 $a Electronic books. $2 local $3 554714
690 $a 0546
690 $a 0984
690 $a 0354
710 2 $a University of Windsor (Canada). $b INDUSTRIAL ENGINEERING (MASc). $3 1473603
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 86-01.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332276 $z click for full text (PQDT)