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

Solveig Kathleen Najork; Laura Liebenow; Laura Pauline Scherf; Axel Steinhage; Szymon Siecinski; Marcin Grzegorzek

doi:10.1109/IEEECONF58974.2023.10404764

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

Solveig Kathleen Najork
, Laura Liebenow
, Laura Pauline Scherf
, Axel Steinhage
, Szymon Siecinski
, Marcin Grzegorzek

Department of Medical Informatics and Artificial Intelligence (RIB/RIB1)

University of Lübeck
SensProtect GmbH
Fraunhofer Research Institution for Marine Biotechnology and Cell Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	105-106
Number of pages	2
ISBN (Electronic)	9798350383386
DOIs	https://doi.org/10.1109/IEEECONF58974.2023.10404764
Publication status	Published - 2023
Event	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 - Malta, Malta Duration: 7 Dec 2023 → 9 Dec 2023

Publication series

Name	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

Conference

Conference	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023
Country/Territory	Malta
City	Malta
Period	7/12/23 → 9/12/23

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Information Systems
Decision Sciences (miscellaneous)
Information Systems and Management
Biomedical Engineering
Health Informatics

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10.1109/IEEECONF58974.2023.10404764

Cite this

Najork, S. K., Liebenow, L., Scherf, L. P., Steinhage, A., Siecinski, S., & Grzegorzek, M. (2023). Binary classification of gait impairments using a capacitance-based sensor floor system. In 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 (pp. 105-106). (2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEEECONF58974.2023.10404764

Najork, Solveig Kathleen ; Liebenow, Laura ; Scherf, Laura Pauline et al. / Binary classification of gait impairments using a capacitance-based sensor floor system. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 105-106 (2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023).

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title = "Binary classification of gait impairments using a capacitance-based sensor floor system",

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.",

author = "Najork, \{Solveig Kathleen\} and Laura Liebenow and Scherf, \{Laura Pauline\} and Axel Steinhage and Szymon Siecinski and Marcin Grzegorzek",

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doi = "10.1109/IEEECONF58974.2023.10404764",

language = "English",

series = "2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Najork, SK, Liebenow, L, Scherf, LP, Steinhage, A, Siecinski, S & Grzegorzek, M 2023, Binary classification of gait impairments using a capacitance-based sensor floor system. in 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023, Institute of Electrical and Electronics Engineers Inc., pp. 105-106, 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023, Malta, Malta, 7/12/23. https://doi.org/10.1109/IEEECONF58974.2023.10404764

Binary classification of gait impairments using a capacitance-based sensor floor system. / Najork, Solveig Kathleen; Liebenow, Laura; Scherf, Laura Pauline et al.
2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 105-106 (2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Siecinski, Szymon

AU - Grzegorzek, Marcin

PY - 2023

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N2 - 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.

AB - 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.

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Najork SK, Liebenow L, Scherf LP, Steinhage A, Siecinski S, Grzegorzek M. Binary classification of gait impairments using a capacitance-based sensor floor system. In 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 105-106. (2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023). doi: 10.1109/IEEECONF58974.2023.10404764

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

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