Nichanian, ArthurLi, Wen-ChinKorek, Wojciech TomaszWang, YifanChan, Wesley Tsz-Kin2024-07-292024-07-292024-06-01Nichanian A, Li W-C, Korek WT (2024) Self-organising maps for comparing flying performance using different inceptors. In: 21st International Conference, EPCE 2024, Held as Part of the 26th HCI International Conference, HCII 2024, 29 June - 4 July 2024, Washington DC, USA. Proceedings, Part II, Lecture Notes in Computer Science, Volume 14693, pp. 109-122978-3-031-60730-10302-9743https://doi.org/10.1007/978-3-031-60731-8_8https://dspace.lib.cranfield.ac.uk/handle/1826/22681This paper addresses a new data analysis method which is suitable to cluster flight data and complement current exceedance-based flight data monitoring programmes within an airline. The data used for this study consists of 296 simulated approaches from 4.5 NM to 1 NM to the runway threshold, flown by 74 participants (both pilots and non-pilots) with either a conventional sidestick or a gamepad in the future flight simulator at Cranfield University. It was clustered and analysed with the use of Kohonen’s Self-Organising Maps (SOM) algorithm. The results demonstrate that SOM can be a meaningful indicator for safety analysts to accurately cluster both optimal and less-optimal flying performance. This methodology can therefore complement current deviation-based flight data analyses by highlighting day-to-day as well as exceptionally good performance, bridging the cap of current analyses with safety-II principles.109-122enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/data analysishuman-machine interactionsConference paper978-3-031-60731-81611-3349