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In subterranean rodents (tuco-tucos), we used bio-loggers (accelerometers) to measure activity rhythms in both lab and field conditions, detecting diverse movements that compose activity. However, understanding these different accelerometer-detected activity components requires more complex analytical tools. Here we used supervised hidden Markov models (HMMs) as a machine learning analysis, to identify behavioral patterns in accelerometer data of tuco-tucos from field enclosures and characterize their behavioral rhythms in this condition. Activity of tuco-tucos was previously video-recorded in the laboratory with simultaneous accelerometer measurements. Video-obtained behavioral data were used in HMM models to refine (train) the classification of accelerometer recordings into different behavioral states. The classification obtained by HMM matched in 93% the one obtained by the video-observed method. 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