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Related Experiment Video

Updated: Jun 16, 2026

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
08:48

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Published on: January 9, 2016

Robust movement segmentation by combining multiple sources of information.

Willemijn D Schot1, Eli Brenner, Jeroen B J Smeets

  • 1Research Institute MOVE, Faculty of Human Movement Sciences, VU University Amsterdam, Van der Boechorststraat 9, 1081 BT Amsterdam, The Netherlands. w.schot@fbw.vu.nl

Journal of Neuroscience Methods
|January 26, 2010
PubMed
Summary

Determining movement segment endpoints in kinematic data analysis is challenging with single parameters. The new Multiple Sources of Information (MSI) method improves accuracy and reduces bias by integrating multiple criteria for endpoint detection.

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Area of Science:

  • Biomechanics
  • Kinetics and Kinematics
  • Movement Analysis

Background:

  • Kinematic data analysis often relies on single parameters to define movement segment endpoints, necessitating manual corrections.
  • Current methods can be subjective and may not accurately capture the complex criteria humans use to identify movement cessation.

Purpose of the Study:

  • To introduce and validate a novel objective function-based method for determining movement segment endpoints.
  • To demonstrate the superiority and robustness of the Multiple Sources of Information (MSI) method over conventional single-parameter approaches.

Main Methods:

  • Developed an objective function integrating multiple kinematic parameters to identify optimal movement endpoints.
  • Applied the Multiple Sources of Information (MSI) method to goal-directed upper limb motion data.

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Last Updated: Jun 16, 2026

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
08:48

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Published on: January 9, 2016

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

  • Compared MSI method performance against traditional single-parameter endpoint detection techniques.

    • Main Results:

    • The MSI method demonstrated superior performance in identifying movement segment endpoints compared to conventional methods.
    • The MSI method proved robust against arbitrary parameter choices, reducing researcher bias.
    • Eliminated the need for subjective post-hoc corrections in kinematic data analysis.

    Conclusions:

    • The Multiple Sources of Information (MSI) method offers a more objective and accurate approach to kinematic data analysis.
    • This method enhances the reliability of movement segment endpoint determination across various applications.
    • The MSI approach is broadly applicable to diverse types of movement analysis beyond upper limb motion.