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Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait.

Mina Baniasad1, Robin Martin2, Xavier Crevoisier2

  • 1Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

This study presents a new algorithm for accurately attaching Inertial Measurement Unit (IMU) sensors to body segments during motion analysis. The method is robust across various gait speeds and footwear, improving efficiency in sports and rehabilitation settings.

Keywords:
I2S pairingIMU sensor placementIMU-2-segment pairingsensor locationside identificationstride-time estimationwearable sensor

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Area of Science:

  • Biomechanics and Sports Science
  • Rehabilitation Engineering
  • Wearable Sensor Technology

Background:

  • Inertial measurement unit (IMU) sensors are crucial for motion analysis in sports and rehabilitation.
  • Current IMU-to-body segment (I2S) pairing methods are complex, time-consuming, error-prone, and limited in gait speed and sensor configuration.
  • A need exists for a robust and generalizable I2S pairing algorithm.

Purpose of the Study:

  • To develop and validate an automated algorithm for accurate IMU sensor-to-body segment and side (left/right) pairing.
  • To ensure the algorithm's robustness across a wide range of gait speeds, sensor configurations, and footwear types.
  • To assess the algorithm's generalizability to pathological gait patterns, specifically in patients with knee osteoarthritis.

Main Methods:

  • Eight IMU sensors were placed on the feet, shanks, thighs, sacrum, and trunk.
  • Healthy subjects (n=12) and patients with knee osteoarthritis (n=22) participated, walking at various speeds with and without insoles.
  • An algorithm involving stride time estimation, signal scaling, and a decision tree for segment and side recognition was employed.

Main Results:

  • The algorithm achieved high accuracy (99.7%) and precision (99.0%) for IMU sensor-to-body segment and side pairing.
  • Performance was consistent across a broad range of gait speeds (0.5 to 2.2 m/s).
  • The algorithm demonstrated robustness to footwear type and generalizability to pathological gait patterns.

Conclusions:

  • The developed algorithm provides an accurate, efficient, and robust solution for IMU sensor-to-body segment and side pairing.
  • Its versatility across gait speeds, footwear, and patient populations makes it broadly applicable in sports science and clinical rehabilitation.
  • This advancement simplifies motion analysis, reducing setup time and potential errors in sensor placement.