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Related Experiment Video

Updated: Jun 13, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Validation of Insole Pressure Sensor Algorithms: Implications for In-Field Detection of Initial Contact and Hamstring

Emilie E Zwicky1, Niels J Nedergaard2, Tine Alkjær3,4

  • 1Team Danmark, The Elite Sport Organization of Denmark, 2605 Brondby, Denmark.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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A new criteria-based insole pressure sensor algorithm accurately detects initial contact during side-cutting. This method precisely monitors hamstring pre-activity, crucial for preventing anterior cruciate ligament injuries in athletes.

Area of Science:

  • Biomechanics
  • Sports Medicine
  • Sensor Technology

Background:

  • Accurate initial contact (IC) detection is vital for assessing semitendinosus (ST) muscle pre-activity.
  • ST pre-activity is a protective mechanism against anterior cruciate ligament (ACL) injuries in athletes.
  • Current methods for IC detection may lack precision in real-world sports scenarios.

Purpose of the Study:

  • To develop and evaluate two insole pressure sensor (IPS) algorithms for IC detection during side-cutting.
  • To compare the accuracy of IPS algorithms against force-plate derived IC using vertical ground reaction forces (vGRF).
  • To assess the agreement between IPS-derived and vGRF-derived ST pre-activity.

Main Methods:

  • Two IPS algorithms were developed: body weight-based and criteria-based.
Keywords:
ACL injurychange-of-directioninitial contactmedial hamstringplantar pressure insoleswearable sensors

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Last Updated: Jun 13, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Published on: May 26, 2020

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  • Twenty-six female athletes performed side-cutting while IPS, vGRF, and ST electromyography were recorded.
  • Bland-Altman analysis was used to evaluate agreement and limits of agreement (LoA) between methods.

    • Main Results:

    • The body weight-based IPS algorithm showed a systematic delay in IC detection (9.2 ms) and bias in ST pre-activity (-3.5% MVC).
    • The criteria-based IPS algorithm demonstrated minimal bias in IC detection (-0.1 ms) and ST pre-activity (-0.1% MVC).
    • The criteria-based algorithm showed excellent agreement with force-plate data for both IC detection and ST pre-activity.

    Conclusions:

    • The criteria-based IPS algorithm enables accurate IC detection during side-cutting.
    • This algorithm supports practical, non-laboratory monitoring of ST pre-activity.
    • It holds potential for injury prevention strategies in team sport athletes.