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

doi:10.1109/IEEECONF58974.2023.10404764

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

SenseProtect GmbH

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
Pages	105-106
Number of pages	2
DOIs	https://doi.org/10.1109/IEEECONF58974.2023.10404764
Publication status	Published - 29.01.2024
Event	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology - Hilton Malta, St. Julian's, Malta Duration: 07.12.2023 → 09.12.2023 https://datascience.embs.org/2023

Conference

Conference	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology
Country/Territory	Malta
City	St. Julian's
Period	07.12.23 → 09.12.23
Internet address	https://datascience.embs.org/2023

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

2.22-32 Medical Physics, Biomedical Technology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IEEECONF58974.2023.10404764

Cite this

Najork, S., Liebenow, L., Scherf, L. P., Steinhage, A., Sieciński, S., & Grzegorzek, M. (2024). Binary classification of gait impairments using a capacitance-based sensor floor system. 105-106. Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta. https://doi.org/10.1109/IEEECONF58974.2023.10404764

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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 = "Solveig Najork and Laura Liebenow and Scherf, {Laura Pauline} and Axel Steinhage and Szymon Sieci{\'n}ski and Marcin Grzegorzek",

year = "2024",

month = jan,

day = "29",

doi = "10.1109/IEEECONF58974.2023.10404764",

language = "English",

pages = "105--106",

note = "2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology ; Conference date: 07-12-2023 Through 09-12-2023",

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Najork, S, Liebenow, L, Scherf, LP, Steinhage, A, Sieciński, S & Grzegorzek, M 2024, 'Binary classification of gait impairments using a capacitance-based sensor floor system', Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta, 07.12.23 - 09.12.23 pp. 105-106. https://doi.org/10.1109/IEEECONF58974.2023.10404764

Binary classification of gait impairments using a capacitance-based sensor floor system. / Najork, Solveig; Liebenow, Laura ; Scherf, Laura Pauline et al.
2024. 105-106 Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta.

Research output: Contribution to conference › Conference Papers › peer-review

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T1 - Binary classification of gait impairments using a capacitance-based sensor floor system

AU - Najork, Solveig

AU - Liebenow, Laura

AU - Scherf, Laura Pauline

AU - Steinhage, Axel

AU - Sieciński, Szymon

AU - Grzegorzek, Marcin

PY - 2024/1/29

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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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U2 - 10.1109/IEEECONF58974.2023.10404764

DO - 10.1109/IEEECONF58974.2023.10404764

M3 - Conference Papers

SP - 105

EP - 106

T2 - 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology

Y2 - 7 December 2023 through 9 December 2023

ER -

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

Abstract

Conference

Research Areas and Centers

DFG Research Classification Scheme

UN SDGs

Access to Document

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