Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models

Huanghe Zhang, Yi Guo, Damiano Zanotto

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Wearable sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments. However, the moderate accuracy of these systems currently limits their widespread use. In this paper, we show that support vector regression (SVR) models can be used to extract accurate estimates of fundamental gait parameters (i.e., stride length, velocity, and foot clearance), from custom-engineered instrumented insoles (SportSole) during walking and running tasks. Additionally, these learning-based models are robust to inter-subject variability, thereby making it unnecessary to collect subject-specific training data. Gait analysis was performed in N=14 healthy subjects during two separate sessions, each including 6-minute bouts of treadmill walking and running at different speeds (i.e., 85% and 115% of each subject's preferred speed). Gait metrics were simultaneously measured with the instrumented insoles and with reference laboratory equipment. SVR models yielded excellent intraclass correlation coefficients (ICC) in all the gait parameters analyzed. Percentage mean absolute errors (MAE%) in stride length, velocity, and foot clearance obtained with SVR models were 1.37%±0.49%, 1.23%±0.27%, and 2.08%±0.72% for walking, 2.59%±0.64%, 2.91%±0.85%, and 5.13%±1.52% for running, respectively. These findings provide evidence that machine learning regression is a promising new approach to improve the accuracy of wearable sensors for gait analysis.

Original languageEnglish
Article number8930581
Pages (from-to)191-202
Number of pages12
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume28
Issue number1
DOIs
StatePublished - Jan 2020

Keywords

  • Ambulatory gait analysis
  • instrumented footwear
  • machine learning regression
  • wearable technology

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