Gait Phase Recognition using Textile-based Sensor

Abdulkadir Pazar; Fidan Khalilbayli; Kadir Ozlem; Ayse Feyza Yilmaz; Asli Tuncay Atalay; Ozgur Atalay; Gokhan Ince

doi:10.1109/UBMK55850.2022.9919491

Gait Phase Recognition using Textile-based Sensor

Abdulkadir Pazar, Fidan Khalilbayli, Kadir Ozlem, Ayse Feyza Yilmaz, Asli Tuncay Atalay, Ozgur Atalay, Gokhan Ince

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

4 Citations (Scopus)

Abstract

Human gait phase detection has become an emerging field of study due to its impact in various clinical studies. In this study, a system is developed to detect the toe-off, mid-swing, heel-strike, and heel-off phases of a gait cycle in real-time by using a textile-based capacitive strain sensor mounted on the kneepad. Five healthy subjects performed walks including those four phases of the gait at a constant speed and gait distance in a laboratory environment while wearing the kneepad. The phases are labeled according to the gyroscope data of the Inertial Measurement Unit (IMU) located on the kneepad. An Long Short-Term Memory (LSTM) based network is utilized to detect the phases using the capacitance data obtained from the strain sensor. Recognition of four phases with 87 % accuracy is accomplished.

Original language	English
Title of host publication	Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	338-343
Number of pages	6
ISBN (Electronic)	9781665470100
DOIs	https://doi.org/10.1109/UBMK55850.2022.9919491
Publication status	Published - 2022
Event	7th International Conference on Computer Science and Engineering, UBMK 2022 - Diyarbakir, Turkey Duration: 14 Sept 2022 → 16 Sept 2022

Publication series

Name	Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022

Conference

Conference	7th International Conference on Computer Science and Engineering, UBMK 2022
Country/Territory	Turkey
City	Diyarbakir
Period	14/09/22 → 16/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

VII. ACKNOWLEDGEMENTS The authors disclose the receipt of the following financial support for research and authorship. This work was funded by Scientific and Technological Research Council of Turkey (TUBITAK) Research Grant No:120C118.

Funders	Funder number
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	:120C118

Keywords

Gait Analysis
Inertial Measurement Unit
Long Short-Term Memory
Real-time Gait Phase Recognition
Textile-based Strain Sensor

Access to Document

10.1109/UBMK55850.2022.9919491

Cite this

Pazar, A., Khalilbayli, F., Ozlem, K., Yilmaz, A. F., Atalay, A. T., Atalay, O., & Ince, G. (2022). Gait Phase Recognition using Textile-based Sensor. In Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022 (pp. 338-343). (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UBMK55850.2022.9919491

Pazar, Abdulkadir ; Khalilbayli, Fidan ; Ozlem, Kadir et al. / Gait Phase Recognition using Textile-based Sensor. Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 338-343 (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022).

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title = "Gait Phase Recognition using Textile-based Sensor",

abstract = "Human gait phase detection has become an emerging field of study due to its impact in various clinical studies. In this study, a system is developed to detect the toe-off, mid-swing, heel-strike, and heel-off phases of a gait cycle in real-time by using a textile-based capacitive strain sensor mounted on the kneepad. Five healthy subjects performed walks including those four phases of the gait at a constant speed and gait distance in a laboratory environment while wearing the kneepad. The phases are labeled according to the gyroscope data of the Inertial Measurement Unit (IMU) located on the kneepad. An Long Short-Term Memory (LSTM) based network is utilized to detect the phases using the capacitance data obtained from the strain sensor. Recognition of four phases with 87 % accuracy is accomplished.",

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author = "Abdulkadir Pazar and Fidan Khalilbayli and Kadir Ozlem and Yilmaz, {Ayse Feyza} and Atalay, {Asli Tuncay} and Ozgur Atalay and Gokhan Ince",

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Pazar, A, Khalilbayli, F, Ozlem, K, Yilmaz, AF, Atalay, AT , Atalay, O & Ince, G 2022, Gait Phase Recognition using Textile-based Sensor. in Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022, Institute of Electrical and Electronics Engineers Inc., pp. 338-343, 7th International Conference on Computer Science and Engineering, UBMK 2022, Diyarbakir, Turkey, 14/09/22. https://doi.org/10.1109/UBMK55850.2022.9919491

Gait Phase Recognition using Textile-based Sensor. / Pazar, Abdulkadir; Khalilbayli, Fidan; Ozlem, Kadir et al.
Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 338-343 (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022).

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

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AU - Pazar, Abdulkadir

AU - Khalilbayli, Fidan

AU - Ozlem, Kadir

AU - Yilmaz, Ayse Feyza

AU - Atalay, Asli Tuncay

AU - Atalay, Ozgur

AU - Ince, Gokhan

PY - 2022

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N2 - Human gait phase detection has become an emerging field of study due to its impact in various clinical studies. In this study, a system is developed to detect the toe-off, mid-swing, heel-strike, and heel-off phases of a gait cycle in real-time by using a textile-based capacitive strain sensor mounted on the kneepad. Five healthy subjects performed walks including those four phases of the gait at a constant speed and gait distance in a laboratory environment while wearing the kneepad. The phases are labeled according to the gyroscope data of the Inertial Measurement Unit (IMU) located on the kneepad. An Long Short-Term Memory (LSTM) based network is utilized to detect the phases using the capacitance data obtained from the strain sensor. Recognition of four phases with 87 % accuracy is accomplished.

AB - Human gait phase detection has become an emerging field of study due to its impact in various clinical studies. In this study, a system is developed to detect the toe-off, mid-swing, heel-strike, and heel-off phases of a gait cycle in real-time by using a textile-based capacitive strain sensor mounted on the kneepad. Five healthy subjects performed walks including those four phases of the gait at a constant speed and gait distance in a laboratory environment while wearing the kneepad. The phases are labeled according to the gyroscope data of the Inertial Measurement Unit (IMU) located on the kneepad. An Long Short-Term Memory (LSTM) based network is utilized to detect the phases using the capacitance data obtained from the strain sensor. Recognition of four phases with 87 % accuracy is accomplished.

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KW - Inertial Measurement Unit

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BT - Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022

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ER -

Pazar A, Khalilbayli F, Ozlem K, Yilmaz AF, Atalay AT , Atalay O et al. Gait Phase Recognition using Textile-based Sensor. In Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 338-343. (Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022). doi: 10.1109/UBMK55850.2022.9919491

Gait Phase Recognition using Textile-based Sensor

Abstract

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Bibliographical note

Funding

Keywords

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