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We propose a new model-free method to detect change points between distinctphases in a single random trajectory of an intermittent stochastic process. Thelocal convex hull (LCH) is constructed for each trajectory point, while itsgeometric properties (e.g., the diameter or the volume) are used asdiscriminators between phases. The efficiency of the LCH method is validatedfor six models of intermittent motion, including Brownian motion with differentdiffusivities or drifts, fractional Brownian motion with different Hurstexponents, and surface-mediated diffusion. We discuss potential applications ofthe method for detection of active and passive phases in the intracellulartransport, temporal trapping or binding of diffusing molecules, alternatingbulk and surface diffusion, run and tumble (or search) phases in the motion ofbacteria and foraging animals, and instantaneous firing rates in neurons.

Unravelling intermittent features in single particle trajectories by a local convex hull method