ABSTRACT
Analyzing an operator’s mental state is an important issue in manufacturing. In this paper, we focused on confusion and perceived smoothness of work progress. A 40-participant experiment was conducted in which participants performed two 50-minute assembly tasks and answered two self-report questions about perceived confusion and perceived smoothness of work progress after each step. The results showed that there was a moderate correlation between the two variables and the duration of the steps. In addition, our preliminary EMG analysis showed that there was a moderate correlation between EMG and perceived confusion.
- Ebrahim Babaei, Namrata Srivastava, Joshua Newn, Qiushi Zhou, Tilman Dingler, and Eduardo Velloso. 2020. Faces of focus: A study on the facial cues of attentional states. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–13.Google ScholarDigital Library
- Jonathan Z Bakdash and Laura R Marusich. 2017. Repeated measures correlation. Frontiers in psychology 8 (2017), 456.Google Scholar
- Agnese Brunzini, Fabio Grandi, Margherita Peruzzini, and Marcello Pellicciari. 2023. An integrated methodology for the assessment of stress and mental workload applied on virtual training. International Journal of Computer Integrated Manufacturing (2023), 1–19.Google ScholarCross Ref
- D Colombini, E Occhipinti, G Molteni, A Grieco, A Pedotti, S Boccardi, C Frigo, and O Menoni. 1985. Posture analysis. Ergonomics 28, 1 (1985), 275–284.Google ScholarCross Ref
- Francis T Durso, Kaitlin M Geldbach, and Paul Corballis. 2012. Detecting confusion using facial electromyography. Human factors 54, 1 (2012), 60–69.Google Scholar
- Sidney D’Mello, Blair Lehman, Reinhard Pekrun, and Art Graesser. 2014. Confusion can be beneficial for learning. Learning and Instruction 29 (2014), 153–170.Google ScholarCross Ref
- Sidney K D’Mello and Arthur C Graesser. 2014. Confusion. In International handbook of emotions in education. Routledge, 289–310.Google Scholar
- Zhongke Gao, Xinmin Wang, Yuxuan Yang, Chaoxu Mu, Qing Cai, Weidong Dang, and Siyang Zuo. 2019. EEG-based spatio–temporal convolutional neural network for driver fatigue evaluation. IEEE transactions on neural networks and learning systems 30, 9 (2019), 2755–2763.Google ScholarCross Ref
- Myounghoon Jeon and Bruce N Walker. 2011. What to detect? Analyzing factor structures of affect in driving contexts for an emotion detection and regulation system. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 55. SAGE Publications Sage CA: Los Angeles, CA, 1889–1893.Google ScholarCross Ref
- Brian D Lowe, Patrick G Dempsey, and Evan M Jones. 2019. Ergonomics assessment methods used by ergonomics professionals. Applied ergonomics 81 (2019), 102882.Google Scholar
- Jennifer F May and Carryl L Baldwin. 2009. Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transportation research part F: traffic psychology and behaviour 12, 3 (2009), 218–224.Google Scholar
- Mariya Pachman, Amaël Arguel, Lori Lockyer, Gregor Kennedy, and Jason Lodge. 2016. Eye tracking and early detection of confusion in digital learning environments: Proof of concept. Australasian Journal of Educational Technology 32, 6 (2016).Google ScholarCross Ref
- Oskar Palinko, Andrew L Kun, Alexander Shyrokov, and Peter Heeman. 2010. Estimating cognitive load using remote eye tracking in a driving simulator. In Proceedings of the 2010 symposium on eye-tracking research & applications. 141–144.Google ScholarDigital Library
- Margherita Peruzzini, Fabio Grandi, Sara Cavallaro, and Marcello Pellicciari. 2021. Using virtual manufacturing to design human-centric factories: an industrial case. The international journal of advanced manufacturing technology 115, 3 (2021), 873–887.Google Scholar
- Gabriele Rescio, Andrea Manni, Marianna Ciccarelli, Alessandra Papetti, Andrea Caroppo, and Alessandro Leone. 2024. A Deep Learning-Based Platform for Workers’ Stress Detection Using Minimally Intrusive Multisensory Devices. Sensors 24, 3 (2024), 947.Google ScholarCross Ref
Index Terms
- Exploring relationship between EMG, confusion and smoothness of work progress in assembly tasks
Recommendations
Biomechanics and Kinematic Responses of the Upper Extremity during Isotonic and Isokinetic Wrench-Turning Tasks
The purpose of this study was to investigate the impact of using a wrench under isotonic constant torque and isokinetic constant speed task modes TM at three work surface inclinations WSI 0ï ź, 45ï ź, and 90ï ź on the biomechanical muscle activity and ...
Work in progress: Fearful users' privacy intentions: an empirical investigation
STAST '17: Proceedings of the 7th Workshop on Socio-Technical Aspects in Security and TrustBackground. While recent research has found that the affect dimension of privacy attitude is fear focused [14], fear is known in psychology literature to be asymmetric to one's self-efficacy [5], that is one's belief in successfully solving a problem. ...
Examining Simulated Agricultural Tasks Using an Arm-Support Exoskeleton
Artificial Intelligence in HCIAbstractAgricultural work requires physical labor and often entails repetitive movements of the upper extremities, which can result in musculoskeletal disorders. The purpose of this study was to assess the impact of utilizing an arm-support exoskeleton (...
Comments