Regression Models for Estimating Kinematic Gait Parameters with Instrumented Footwear

Huanghe Zhang, Mey Olivares Tay, Zeynep Suar, Mehmet Kurt, Damiano Zanotto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Quantitative gait assessment typically involves optical motion capture systems and force plates, which result in high operating costs. Footwear-based motion tracking systems can provide a portable and affordable solution for real-time gait analysis in unconstrained environments. However, the relatively low accuracy of these systems still represents a barrier to their widespread use. In this paper, we show that linear and learning-based regression models can substantially improve the raw estimates of a set of kinematic gait parameters obtained with instrumented insoles (SportSole) from a group of N=9 healthy subjects who walked at different speeds. Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Regression (SVR) models are compared in terms of accuracy, precision, and robustness to change in gait speed, using gold-standard equipment to generate reference data. Results indicate that SVR is superior to LASSO. Indeed, the mean absolute errors (MAE) in stride length, velocity and foot-ground clearance were 1.28± 0.19%. 1.62±0.42% and 3.72±0.87% for LASSO, 1.06±0.08%. 1.13±0.08% and 3.00±0.87% for SVR, respectively. These findings provide further evidence that footwear-based systems may represent valid alternatives to laboratory equipment for assessing a basic set of gait parameters in unconstrained environments.

Original languageEnglish
Title of host publicationBIOROB 2018 - 7th IEEE International Conference on Biomedical Robotics and Biomechatronics
Pages1169-1174
Number of pages6
ISBN (Electronic)9781538681831
DOIs
StatePublished - 9 Oct 2018
Event7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BIOROB 2018 - Enschede, Netherlands
Duration: 26 Aug 201829 Aug 2018

Publication series

NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Volume2018-August
ISSN (Print)2155-1774

Conference

Conference7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BIOROB 2018
Country/TerritoryNetherlands
CityEnschede
Period26/08/1829/08/18

Keywords

  • Gait Assessment
  • Learning-based Regression
  • Sport Sole
  • Wearable Sensor Networks
  • Wearable Technology

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