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To address this limitation, we developed threshold-free deep learning models operating on tail-curvature time series: an offline model for\n                  <jats:italic>post hoc<\/jats:italic>\n                  analysis and an online model for real-time detection. Both improved precision over threshold-based methods, particularly for low-amplitude bouts. Using the offline model, we show that a head-fixed, closed-loop preparation largely preserves naturalistic swimming kinematics. Repeated trials reduced bout frequency and increased interbout interval, whereas genetic background primarily affects bout duration and maximum amplitude; notably, casper larvae lacked the late-trial decline observed in wildtype and nacre. We further show that detector choice can alter inferred behavioral outcomes. 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