Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?
Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/14/4001 |
| _version_ | 1797562066536497152 |
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| author | Jucheol Moon Nelson Hebert Minaya Nhat Anh Le Hee-Chan Park Sang-Il Choi |
| author_facet | Jucheol Moon Nelson Hebert Minaya Nhat Anh Le Hee-Chan Park Sang-Il Choi |
| author_sort | Jucheol Moon |
| collection | DOAJ |
| description | Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%. |
| first_indexed | 2024-03-10T18:24:17Z |
| format | Article |
| id | doaj.art-ff79bd3e74c042198c3bef063ceafde8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:24:17Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ff79bd3e74c042198c3bef063ceafde82023-11-20T07:12:36ZengMDPI AGSensors1424-82202020-07-012014400110.3390/s20144001Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?Jucheol Moon0Nelson Hebert Minaya1Nhat Anh Le2Hee-Chan Park3Sang-Il Choi4Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USADepartment of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USADepartment of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USADepartment of Computer Science and Engineering, Dankook University, Yongin-si 16890, KoreaDepartment of Computer Science and Engineering, Dankook University, Yongin-si 16890, KoreaGait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%.https://www.mdpi.com/1424-8220/20/14/4001gait analysisuser identificationdeep learningmulti-modalitywearable sensors |
| spellingShingle | Jucheol Moon Nelson Hebert Minaya Nhat Anh Le Hee-Chan Park Sang-Il Choi Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole? Sensors gait analysis user identification deep learning multi-modality wearable sensors |
| title | Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole? |
| title_full | Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole? |
| title_fullStr | Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole? |
| title_full_unstemmed | Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole? |
| title_short | Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole? |
| title_sort | can ensemble deep learning identify people by their gait using data collected from multi modal sensors in their insole |
| topic | gait analysis user identification deep learning multi-modality wearable sensors |
| url | https://www.mdpi.com/1424-8220/20/14/4001 |
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