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Orthogonal Trajectories01:26

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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Related Experiment Video

Updated: Jun 25, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Toolbox for analyzing finite two-state trajectories.

O Flomenbom1, R J Silbey

  • 1Chemistry Department, MIT, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 5, 2009
PubMed
Summary

This study introduces a computational toolbox to build reduced dimension (RD) forms from two-state trajectory data. This method uniquely identifies kinetic schemes (KS) by analyzing non-exponential waiting time probability density functions.

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Last Updated: Jun 25, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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Published on: August 30, 2016

Area of Science:

  • Computational Biology
  • Statistical Mechanics
  • Biophysics

Background:

  • Experimental data often involves complex kinetic schemes (KS) with multiple states.
  • Analyzing two-state trajectories can yield ambiguous results, with multiple KS potentially fitting the same data.
  • Reduced dimensions (RD) offer a theoretical framework for uniquely identifying underlying kinetic schemes.

Purpose of the Study:

  • To develop a practical method for associating a unique RD form with experimental two-state trajectory data.
  • To provide a self-contained computational toolbox for building RD forms from finite, noiseless trajectories.
  • To enable accurate deduction of multisubstate on-off kinetic schemes from statistical trajectory properties.

Main Methods:

  • Development of a toolbox integrating established statistical data analysis methods with novel algorithms.
  • Application of statistical methods to analyze non-exponential waiting time probability density functions (WT-PDFs) in RD forms.
  • Utilizing finite, noiseless two-state trajectories as input for the RD form construction mechanism.

Main Results:

  • The toolbox successfully builds a unique RD form from input trajectory data.
  • The implemented methods are computationally efficient, analyzing large datasets rapidly on standard hardware.
  • The approach demonstrates the feasibility of uniquely determining complex kinetic schemes from limited trajectory information.

Conclusions:

  • The developed toolbox provides a robust and efficient solution for identifying unique kinetic schemes from two-state trajectory data.
  • This work advances the analysis of dynamic processes by enabling precise characterization of multisubstate on-off kinetic schemes.
  • The freely available toolbox facilitates further research in biophysics and computational biology.