{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T15:04:18Z","timestamp":1774710258287,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T00:00:00Z","timestamp":1619308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Funding for Outstanding Doctoral Dissertation in NUAA","award":["BCXJ19-10"],"award-info":[{"award-number":["BCXJ19-10"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX19_0196"],"award-info":[{"award-number":["KYCX19_0196"]}]},{"name":"Nanjing University of Aeronautics and Astronautics Ph.D. short-term visiting scholar project","award":["190637DF07"],"award-info":[{"award-number":["190637DF07"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["NS2020051"],"award-info":[{"award-number":["NS2020051"]}]},{"name":"Civil Aircraft Simulated Flight Test Data Procurement Project","award":["NNW2020-JT01-039"],"award-info":[{"award-number":["NNW2020-JT01-039"]}]},{"name":"Research on Safety Risk Assessment Technology and Method of Human-Computer Intelligent Interaction in Civil Aircraft Cockpit","award":["U2033202"],"award-info":[{"award-number":["U2033202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots\u2019 operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots\u2019 electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots\u2019 fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots\u2019 fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots\u2019 fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.<\/jats:p>","DOI":"10.3390\/s21093003","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T22:31:39Z","timestamp":1619389899000},"page":"3003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Identification of Pilots\u2019 Fatigue Status Based on Electrocardiogram Signals"],"prefix":"10.3390","volume":"21","author":[{"given":"Ting","family":"Pan","sequence":"first","affiliation":[{"name":"College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqing","family":"Si","sequence":"additional","affiliation":[{"name":"College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Li","sequence":"additional","affiliation":[{"name":"College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Shang","sequence":"additional","affiliation":[{"name":"College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3357\/ASEM.2435.2009","article-title":"Fatigue counter measures in aviation","volume":"80","author":"Caldwell","year":"2009","journal-title":"Aviat. 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