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Related Experiment Video

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Home-Based Monitor for Gait and Activity Analysis
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Lower body kinematics estimation during walking using an accelerometer.

Zahed Mantashloo1, Ali Abbasi1, Mehdi Khaleghi Tazji1

  • 1Department of Biomechanics and Sports Injuries, Faculty of Physical Education and Sports Sciences, Kharazmi University, Tehran, Iran.

Journal of Biomechanics
|March 21, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a portable method to estimate lower limb joint angles during walking using accelerometers and random forest (RF) algorithms. The findings show accurate and reliable joint angle measurements, suitable for real-world applications outside the lab.

Keywords:
AccelerometerGait analysisJoint angleMachine learningRandom forestStatistical parametric mapping

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Accurate measurement of lower limb joint angles is crucial for assessing user progress in rehabilitation and sports.
  • Traditional laboratory-based motion capture systems are expensive and difficult to deploy in real-world settings.
  • There is a need for accessible and portable methods for quantifying joint kinematics.

Purpose of the Study:

  • To continuously estimate lower limb joint angles during walking using an accelerometer and a random forest (RF) algorithm.
  • To validate the accuracy of the RF-based estimation against gold-standard Vicon motion capture data.
  • To explore the potential of wearable sensor technology for out-of-laboratory gait analysis.

Main Methods:

  • Seventy-three subjects walked at various speeds while their lower limb joint angles were captured using a 10-camera Vicon system.
  • Acceleration data from wearable sensors served as input for a random forest (RF) model to estimate ankle, knee, and hip angles.
  • Statistical analysis, including Pearson correlation coefficient (r), Mean Square Error (MSE), and paired statistical parametric mapping (SPM) t-test, was used for validation.

Main Results:

  • The RF model achieved high accuracy, with Pearson correlation coefficients (r) consistently above 0.91.
  • Mean Square Error (MSE) values for estimated joint angles ranged from 0.04 to 24.29.
  • Statistical parametric mapping (SPM) revealed no significant differences between experimental and estimated joint angles across all planes and gait cycles.

Conclusions:

  • The study successfully developed an accessible and portable procedure for quantifying lower limb joint angles using accelerometers and RF.
  • The wearable-based joint angle estimation demonstrates high accuracy and reliability, comparable to laboratory settings.
  • This technology holds significant potential for real-world gait analysis in diverse, non-laboratory environments.