A Pilot Study to Formulate Data-Driven Worker Fatigue Models of an Order Picking Operation
| dc.contributor.advisor | Jimenez, Jesus A. | |
| dc.contributor.advisor | Mendez Médiavilla, Fancis A. | |
| dc.contributor.author | Suresh, Venkataramanan | |
| dc.contributor.committeeMember | Farrell, John | |
| dc.contributor.committeeMember | Russi-Vigoya, M. Natalia | |
| dc.date.accessioned | 2024-05-13T20:41:02Z | |
| dc.date.available | 2024-05-13T20:41:02Z | |
| dc.date.issued | 2022-05 | |
| dc.description.abstract | Fatigue is one of the significant issues manual workers face while performing highly demanding physical tasks. An industrial environment where the workers must perform repetitive manual material handling tasks leads to fatigue. Human fatigue can be physical or mental and leads to musculoskeletal disorders (MSDs) and injuries and a reduction in productivity at the workplace. This research presents an approach to formulate data-driven models to predict worker’s fatigue levels while performing manual tasks using a digital twin (DT) framework. DT is a simulation tool used to represent a physical entity virtually. An order-picking activity involving a combination of manual tasks like picking, carrying, and placing was designed and simulated using human subjects in a bio-motion capture environment. The subject's motion and physiological data were collected using motion capture (MoCap) technology and Hexoskin suit. Cognitive Stroop tests were conducted during the task, and the subject's reaction time for the tests was recorded. The study used Borg's scale to indicate the subject's self-reported exertion levels while performing the task. Using physiological factors like heart rate, breathing rate, minute ventilation, biomechanical factors like body joint angles, and cognitive reaction time, three individual and one combined multiple linear regression model was derived to predict the self-reported exertion level of the order picker. The models were compared. The results show the statistical significance and residual errors of all the models. The proposed methodology using a DT framework was able to predict the self-reported exertion levels of the order picker. This research has the potential to contribute to the field of ergonomics and manual material handling industries to help schedule and assign work to the industry workers efficiently. | |
| dc.description.department | Engineering | |
| dc.format | Text | |
| dc.format.extent | 78 pages | |
| dc.format.medium | 1 file (.pdf) | |
| dc.identifier.citation | Suresh, V. (2022). A pilot study to formulate data-driven worker fatigue models of an order picking operation (Unpublished thesis). Texas State University, San Marcos, Texas. | |
| dc.identifier.uri | https://hdl.handle.net/10877/18693 | |
| dc.language.iso | en | |
| dc.subject | digital twin (DT) | |
| dc.subject | manual material handling (MMH) | |
| dc.subject | fatigue | |
| dc.subject | motion capture | |
| dc.subject | cognition | |
| dc.subject | physiological factors | |
| dc.title | A Pilot Study to Formulate Data-Driven Worker Fatigue Models of an Order Picking Operation | |
| dc.type | Thesis | |
| thesis.degree.department | Engineering | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Texas State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |