  Single-particle tracking allows to infer the motion of single molecules in
living cells. When we observe a long trajectory (more than 100 points), it is
possible that the particle switches mode of motion over time. Then, fitting a
single model to the trajectory can be misleading. In this paper, we propose a
method to detect the temporal change points : the times at which a change of
dynamics occurs. More specifically, we consider that the particle switches
between three main modes of motion : Brownian motion, subdiffusion and
superdiffusion. We use an algorithm based on a statistic (Briane et al. 2016)
computed on local windows along the trajectory. The method is non parametric as
the statistic is not related to any particular model. This algorithm controls
the number of false change point detections in the case where the trajectory is
fully Brownian. A Monte Carlo study is proposed to demonstrate the performances
of the method and also to compare the procedure to two competitive algorithms.
At the end, we illustrate the utility of the method on real data depicting the
motion of mRNA complexes (called mRNP) in neuronal dendrites.
