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Probabilistic Force Estimation and Event Localization (PFEEL) algorithm.

Yohanna MejiaCruz1, Zhaoshuo Jiang1, Juan M Caicedo2

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Engineering Structures
|June 1, 2022
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Summary
This summary is machine-generated.

This study introduces the Probabilistic Force Estimation and Event Localization (PFEEL) algorithm for human activity localization using floor vibrations. PFEEL enhances health monitoring by reducing calibration needs and improving event detection accuracy.

Keywords:
Bayesian inferenceEvent detectionFEEL algorithmFloor vibrationsImpact locationProbabilistic event detectionUncertainty quantification

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Floor vibrations are increasingly used for human activity localization and health monitoring.
  • Existing methods like the Force Estimation and Event Localization (FEEL) algorithm have limitations requiring robust setups.
  • Accurate estimation of gait parameters from floor vibrations can predict patient health status.

Purpose of the Study:

  • To develop a probabilistic approach for human activity localization using floor vibrations.
  • To introduce the Probabilistic Force Estimation and Event Localization (PFEEL) algorithm.
  • To reduce the dependency on complex experimental setups for event localization.

Main Methods:

  • The study builds upon the FEEL algorithm, incorporating probabilistic measures.
  • Random variables associated with floor dynamics are utilized for force estimation and localization.
  • The algorithm identifies optimal calibration points to maximize information gain.

Main Results:

  • The proposed PFEEL algorithm provides probabilistic force estimation and localization.
  • The method successfully reduces the number of required calibration points.
  • Validation of the PFEEL algorithm's design and development is presented.

Conclusions:

  • The PFEEL algorithm offers a more practical and efficient approach to localizing human activities via floor vibrations.
  • This probabilistic method enhances the implementation of health technologies relying on gait analysis.
  • The algorithm's ability to guide calibration simplifies deployment and improves data acquisition.