Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles
Recently, gait attracts attention as a practical biometric for devices that naturally possess walking pattern sensing. In the present study, we explored the feasibility of using a multimodal smart insole for identity recognition. We used sensor insoles designed and implemented by us to collect kinet...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9169646/ |
| _version_ | 1818725747401949184 |
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| author | Kamen Ivanov Zhanyong Mei Martin Penev Ludwig Lubich Omisore Olatunji Mumini Sau Van Nguyen Van Yan Yan Lei Wang |
| author_facet | Kamen Ivanov Zhanyong Mei Martin Penev Ludwig Lubich Omisore Olatunji Mumini Sau Van Nguyen Van Yan Yan Lei Wang |
| author_sort | Kamen Ivanov |
| collection | DOAJ |
| description | Recently, gait attracts attention as a practical biometric for devices that naturally possess walking pattern sensing. In the present study, we explored the feasibility of using a multimodal smart insole for identity recognition. We used sensor insoles designed and implemented by us to collect kinetic and kinematic data from 59 participants that walked outdoors. Then, we evaluated the performance of four neural network architectures, which are a baseline convolutional neural network (CNN), a CNN with a multi-stage feature extractor, a CNN with an extreme learning machine classifier using sensor-level fusion and CNN with extreme learning machine classifier using feature-level fusion. The networks were trained with segmented insole data using 0%, 50%, and 70% segmentation overlap, respectively. For 70% segmentation overlap and both-side data, we obtained mean accuracies of 72.8% ±0.038, 80.9% ±0.036, 80.1% ±0.021 and 93.3% ±0.009, for the four networks, respectively. The results suggest that multimodal sensor-enabled footwear could serve biometric purposes in the next generation of body sensor networks. |
| first_indexed | 2024-12-17T21:47:13Z |
| format | Article |
| id | doaj.art-b70b612aef6549e998369bb45b99e720 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T21:47:13Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b70b612aef6549e998369bb45b99e7202022-12-21T21:31:26ZengIEEEIEEE Access2169-35362020-01-01815079715080710.1109/ACCESS.2020.30169709169646Identity Recognition by Walking Outdoors Using Multimodal Sensor InsolesKamen Ivanov0https://orcid.org/0000-0002-1038-4277Zhanyong Mei1https://orcid.org/0000-0003-4648-9254Martin Penev2https://orcid.org/0000-0002-9105-2978Ludwig Lubich3https://orcid.org/0000-0003-2010-4766Omisore Olatunji Mumini4https://orcid.org/0000-0002-9740-5471Sau Van Nguyen Van5https://orcid.org/0000-0002-7029-3799Yan Yan6https://orcid.org/0000-0002-6344-136XLei Wang7https://orcid.org/0000-0002-7033-9806Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaCollege of Cyber Security, Chengdu University of Technology, Chengdu, ChinaFaculty of Telecommunications, Technical University of Sofia, Sofia, BulgariaFaculty of Telecommunications, Technical University of Sofia, Sofia, BulgariaCAS Key Laboratory for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Key Laboratory for Low-cost Healthcare, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Key Laboratory for Low-cost Healthcare, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaRecently, gait attracts attention as a practical biometric for devices that naturally possess walking pattern sensing. In the present study, we explored the feasibility of using a multimodal smart insole for identity recognition. We used sensor insoles designed and implemented by us to collect kinetic and kinematic data from 59 participants that walked outdoors. Then, we evaluated the performance of four neural network architectures, which are a baseline convolutional neural network (CNN), a CNN with a multi-stage feature extractor, a CNN with an extreme learning machine classifier using sensor-level fusion and CNN with extreme learning machine classifier using feature-level fusion. The networks were trained with segmented insole data using 0%, 50%, and 70% segmentation overlap, respectively. For 70% segmentation overlap and both-side data, we obtained mean accuracies of 72.8% ±0.038, 80.9% ±0.036, 80.1% ±0.021 and 93.3% ±0.009, for the four networks, respectively. The results suggest that multimodal sensor-enabled footwear could serve biometric purposes in the next generation of body sensor networks.https://ieeexplore.ieee.org/document/9169646/Gait recognitionsmart insoleplantar pressurewearable sensorssensor fusion |
| spellingShingle | Kamen Ivanov Zhanyong Mei Martin Penev Ludwig Lubich Omisore Olatunji Mumini Sau Van Nguyen Van Yan Yan Lei Wang Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles IEEE Access Gait recognition smart insole plantar pressure wearable sensors sensor fusion |
| title | Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles |
| title_full | Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles |
| title_fullStr | Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles |
| title_full_unstemmed | Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles |
| title_short | Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles |
| title_sort | identity recognition by walking outdoors using multimodal sensor insoles |
| topic | Gait recognition smart insole plantar pressure wearable sensors sensor fusion |
| url | https://ieeexplore.ieee.org/document/9169646/ |
| work_keys_str_mv | AT kamenivanov identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles AT zhanyongmei identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles AT martinpenev identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles AT ludwiglubich identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles AT omisoreolatunjimumini identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles AT sauvannguyenvan identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles AT yanyan identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles AT leiwang identityrecognitionbywalkingoutdoorsusingmultimodalsensorinsoles |