Binary classification of gait impairments using a capacitance-based sensor floor system

Solveig Najork*, Laura Liebenow, Laura Pauline Scherf, Axel Steinhage, Szymon Sieciński, Marcin Grzegorzek

*Corresponding author for this work

Abstract

This work investigates to what extent it is possible to detect different gait restrictions compared to normal gait using a capacitive sensory floor. For this purpose, several gait parameters and a classification using Random Decision Forest (RDF) are calculated. Furthermore, the importance of the individual features for the different classes is analyzed using Recursive Feature Elimination (RFE). In this paper, different results are visible for the classification of single gaits, but results with an accuracy of up to 90.28% have been achieved.
Original languageEnglish
Pages105-106
Number of pages2
DOIs
Publication statusPublished - 29.01.2024
Event2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology - Hilton Malta, St. Julian's, Malta
Duration: 07.12.202309.12.2023
https://datascience.embs.org/2023

Conference

Conference2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology
Country/TerritoryMalta
CitySt. Julian's
Period07.12.2309.12.23
Internet address

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 205-32 Medical Physics, Biomedical Engineering

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