Tomotaki-Dawoud, KaramKaramTomotaki-DawoudNierula, BirgitBirgitNierulaToumaleu Siewe, FarelleFarelleToumaleu SieweKoch, ThomasThomasKochMeyer, Daniel JohannesDaniel JohannesMeyerBock, AndreasAndreasBockHeinze, MarianneMarianneHeinzeKnuth, DanielaDanielaKnuthMartin, DenisDenisMartinSchander, JuliaJuliaSchanderHilsmann, AnnaAnnaHilsmannEisert, PeterPeterEisertBosse, SebastianSebastianBosse2025-06-172025-06-172024https://publica.fraunhofer.de/handle/publica/48874510.1109/ISM63611.2024.000602-s2.0-105002721323Non-verbal cues play a crucial role in social interactions and can influence conflict dynamics. For law enforcement officers, recognizing these cues is essential for effective deescalation, yet traditional training may not fully address their complexity. This paper focuses on body gestures and presents an automated system for recognizing specific body gestures relevant to social conflict situations, aiming to foster awareness for unconsciously performed body gestures and thereby enabling the training of de-escalation strategies.enfalseExtended Reality trainingmulti-view gesture recognitionSocial signal processingconference paper