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

Katedra Informatyki Medycznej i Sztucznej Inteligencji (RIB/RIB1)

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

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

Abstrakt

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.

Język oryginału	angielski
Tytuł publikacji goszczącej	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023
Wydawca	Institute of Electrical and Electronics Engineers Inc.
Strony	105-106
Liczba stron	2
ISBN (elektroniczny)	9798350383386
Identyfikatory DOI	https://doi.org/10.1109/IEEECONF58974.2023.10404764
Status publikacji	Opublikowano - 2023
Wydarzenie	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 - Malta, Malta Czas trwania: 7 gru 2023 → 9 gru 2023

Seria publikacji

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

Konferencja

Konferencja	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023
Kraj/Terytorium	Malta
Miejscowość	Malta
Okres	7/12/23 → 9/12/23

Obszary tematyczne ASJC Scopus

Sztuczna inteligencja
Interakcja człowiek-komputer
Systemy informacyjne
Nauki o podejmowaniu decyzji (różne)
Systemy informacyjne i zarządzanie
Inżynieria biomedyczna
Informatyka medyczna

Uzyskaj dostęp do dokumentu

10.1109/IEEECONF58974.2023.10404764

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

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. W 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 (str. 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 i in. / 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. str. 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",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 ; Conference date: 07-12-2023 Through 09-12-2023",

year = "2023",

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.",

pages = "105--106",

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

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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. w 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., str. 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 i 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. str. 105-106 (2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

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AU - Liebenow, Laura

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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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M3 - Conference contribution

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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. W 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. str. 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

Abstrakt

Seria publikacji

Konferencja

Obszary tematyczne ASJC Scopus

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Inne pliki i linki

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