Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning methods

yu Matsuda, Itsuo Hanasaki, Ryo Iwao, Hiroki Yamaguchi, Tomohide Niimi

    Research output: Contribution to journalArticle

    2 Citations (Scopus)


    We propose a novel approach to analyze random walks in heterogeneous medium using a hybrid machine-learning method based on a gamma mixture and a hidden Markov model. A gamma mixture and a hidden Markov model respectively provide the number and the most probable sequence of diffusive states from the time series position data of particles/molecules obtained by single-particle/molecule tracking (SPT/SMT) method. We evaluate the performance of our proposed method for numerically generated trajectories. It is shown that our proposed method can correctly extract the number of diffusive states when each trajectory is long enough to be frame averaged. We also indicate that our method can provide an indicator whether the assumption of a medium consisting of discrete diffusive states is appropriate or not based on the available amount of trajectory data. Then, we demonstrate an application of our method to the analysis of experimentally obtained SPT data.

    Original languageEnglish
    Pages (from-to)24099-24108
    Number of pages10
    JournalPhysical Chemistry Chemical Physics
    Issue number37
    Publication statusPublished - 2018 Jan 1


    ASJC Scopus subject areas

    • Physics and Astronomy(all)
    • Physical and Theoretical Chemistry

    Cite this