Multi-modality fusion of floor and ambulatory sensors for gait classification

Syed Usama YUNAS, Abdullah Alharthi, Krikor Ozanyan

Research output: Contribution to conferencePaperpeer-review

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Abstract

In a case study of gait classification from floor and ambulatory sensors, we compare results with data from each modality. The automatic extraction of features is achieved by Principle Component Analysis and Canonical Correlation Analysis, the latter performing better even with a reduced number of components used. Non-linear classifiers are most efficient for fused features. With a Kernel Support Vector Machine around 94% accuracy is demonstrated, improving over the 87% and 79% accuracies obtained with separate floor and ambulatory sensor data, respectively.
Original languageEnglish
Pages1467-1472
Number of pages6
DOIs
Publication statusPublished - 1 Aug 2019
Event 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE) - Canada, Vancouver, Canada
Duration: 12 Jun 201914 Jun 2019

Conference

Conference 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)
Country/TerritoryCanada
CityVancouver
Period12/06/1914/06/19

Keywords

  • floor sensors, wearable sensors, sensor fusion, machine learning (ML), inertial measurement unit (IMU), principal component analysis (PCA), canonical correlation analysis (CCA)

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