Uniwersyteckie Czasopisma NaukoweModelowanie czynników wpływających na wydajność pracowników bibliotek z użyciem metod uczenia maszynowego
Słowa kluczowe:
bibliotekarze, zmęczenie, biometryka, uczenie maszynowe, klasyfikacjaAbstrakt
Celem badań przedstawionych w artykule jest opracowanie skutecznego systemu klasyfikacji poziomu zmęczenia pracowników bibliotek na podstawie sygnałów biometrycznych i behawioralnych. System ten ma umożliwiać wczesne wykrywanie pięciu poziomów zmęczenia, od braku zmęczenia po stan uniemożliwiający efektywną pracę. W badaniu porównano skuteczność różnych algorytmów uczenia maszynowego. Uzyskane wyniki posłużą do stworzenia inteligentnych narzędzi wspierających zarządzanie dobrostanem pracowników.
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Bibliografia
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ÅKERSTEDT Torbjörn, GILLBERG Mats. Subjective and Objective Sleepiness in the Active Individual. International Journal of Neuroscience. 1990, nr 1-2, s. 29-37. ISSN 0020-7454.
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ALMANSOORI Noura, ALDULAIJAN Samah, ALTHANI Sara, HASSAN Noha M., NDIAYE Malick, AWAD Mahmoud. Manual spray painting process optimization using Taguchi robust design. International Journal of Quality & Reliability Management. 2020, nr 1, s. 46-67. ISSN 0265-671X.
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FU Rongrong, WANG Hong, ZHAO Wenbo. Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Systems with Applications. 2016, vol. 63, s. 397-411. ISSN 0957-4174.
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GLADWELL Peter, HARLAND Matthew, ADRISSI Aysha, KERSHAW Saskia, DURES Emma. Exploring the content validity of the Chalder Fatigue Scale using cognitive interviewing in an ME/CFS population. Fatigue: Biomedicine, Health & Behavior. 2024, nr 3, s. 217-237. ISSN 2164-1846.
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HAUS Erhard, SMOLENSKY Michael. Biological clocks and shift work: circadian dysregulation and potential long-term effects. Cancer Causes & Control. 2006, nr 4, s. 489-500. ISSN 1573-7225.
HU Xinyun, LODEWIJKS Gabriel. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue. Journal of Safety Research. 2020, t. 72, s. 173-187. ISSN 0022-4375.
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KO Li-Wei i in. Single channel wireless EEG device for real-time fatigue level detection. In 2015 International Joint Conference on Neural Networks (IJCNN). Killarney: IEEE, s. 1-5, 2015. ISSN 2161-4393.
LEE Hyeonjeong, LEE Jaewon, SHIN Miyoung. Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots. Electronics. 2019, nr 2, s. 1-15. ISSN 2079-9292.
LEE Jong-Seok, BAE You-Suk, LEE Wongok, LEE Hyunsuk, YU Jinkeun, CHOI Jong-Pil. Emotion and Fatigue Monitoring Using Wearable Devices. In HWANG Seong Oun, TAN Syh Yuan, FRANKLIN Bien (red.). Proceedings of the Sixth International Conference on Green and Human Information Technology. Singapore: Springer, s. 91-96, 2019. ISBN 978-981-13-0311-1.
LI Jue i in. Identification and classification of construction equipment operators’ mental fatigue using wearable eye-tracking technology. Automation in Construction. 2020, vol. 109. ISSN 0926-5805.
LÓPEZ Victoria, FERNÁNDEZ Alberto, GARCÍA Salvador, PALADE Vasile, HERRERA Francisco. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences. 2013, vol. 250, s. 113-141. ISSN 0020-0255.
LUGER Tessy, MAHER Christopher G., RIEGER Monika A., STEINHILBER Benjamin. Work-break schedules for preventing musculoskeletal symptoms and disorders in healthy workers. Cochrane Database of Systematic Reviews. 2019, nr 7. ISSN 1469-493X.
MARKATOU Marianthi, TIAN Hong, BISWAS Shameek, HRIPCSAK George. Analysis of variance of cross-validation estimators of the generalization error. Journal of Machine Learning Research. 2005, vol. 6, s. 1127-1168. ISSN 1127-1168.
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NIWA Shinji, YUKI Mori, NORO Tetsushi, SHIOYA Shunsuke, INOUE Kazutaka. A Wearable Device for Traffic Safety – A Study on Estimating Drowsiness with Eyewear. In JINS MEME, SAE Technical Paper. 2016. ISSN 0148-7191.
REN Ping. Cognitive Fatigue in Repetitive Learning Task. Techniques in Neurosurgery and Neurology. 2018, nr 1. ISSN 2637-7748.
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SEDIGHI MAMAN Zahra, CHEN Ying-Ju, BAGHDADI Amir, LOMBARDO Seamus, CAVUOTO Lora A., MEGAHED Fadel M. A data analytic framework for physical fatigue management using wearable sensors. Expert Systems with Applications. 2020, vol. 155. ISSN 0957-4174.
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TAYLOR Janet L. Motor Control and Motor Learning Under Fatigue Conditions. In GOLLHOFER Albert, TAUBE Wolfgang, NIELSEN Jens Bo (red.). Routledge Handbook of Motor Control and Motor Learning. London: Routledge, 2013, s. 353-383. ISBN 978-0-203-13274-6.
TORRES Yaniel, NADEAU Sylvie, LANDAU Kurt. Evaluation of Fatigue and Workload among Workers Conducting Complex Manual Assembly in Manufacturing. IISE Transactions on Occupational Ergonomics and Human Factors. 2021, nr 1, s. 49-63. ISSN 2472-5846.
VARANDAS Rui, LIMA Rodrigo, BERMÚDEZ I BADIA Sergi, SILVA Hugo, GAMBOA Hugo. Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning. Sensors. 2022, nr 11, s. 1-14. ISSN 1424-8220.
WAMSLEY Erin J. Memory Consolidation during Waking Rest. Trends in Cognitive Sciences. 2019, nr 3, s. 171-173. ISSN 1364-6613.
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ZENG Zhikang i in. Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms. ACS Sensors. 2020, nr 5, s. 1305-1313. ISSN 2379-3694.
ZHANG Ming, CHUNQUAN Li, YULING Shang, HUANG Hongyan, ZHU Wangchun, LIU Yujia. A task scheduling model integrating micro-breaks for optimisation of job-cycle time in human-robot collaborative assembly cells. International Journal of Production Research. 2022, nr 15, s. 4766-4777. ISSN 0020-7543.
ZHANG Yuxin, CHEN Yiqiang, PAN Zhiwen. A Deep Temporal Model for Mental Fatigue Detection. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Miyazaki: IEEE, 2018, s. 1879-1884. ISBN 978-1-5386-6650-0.
